├── .gitattributes ├── .gitignore ├── .gitmodules ├── ArtificialIntelligence ├── 1_introduction.tex ├── 2.1_uninformed_search.tex ├── 2.2_heuristic_search.tex ├── 2.3_game_tree_search.tex ├── 3_CSP.tex ├── 4_knowledge_representation.tex ├── 5_planning.tex ├── 6_uncertainty.tex ├── 7_machine_learning.tex ├── README.md ├── fig │ ├── FOL.png │ ├── MGU.png │ ├── T01-3.png │ ├── T02-2.png │ ├── UCS.png │ ├── alpine_club_eg1.png │ ├── alpine_club_eg2.png │ ├── backtracking.png │ ├── backtracking_eg.png │ ├── blocking.png │ ├── bucket_elimination.png │ ├── count_actions.png │ ├── d-separation_example.png │ ├── diff_fc_gac.png │ ├── forward_checking.png │ ├── forward_checking_eg.png │ ├── gac.png │ ├── gac_eg.png │ ├── gac_enforce.png │ ├── game-tree.png │ ├── interpretation.png │ ├── refutation.png │ └── relevance_example.png ├── main.pdf └── main.tex ├── C-Programming ├── C-Structure.pdf ├── C-Structure.tex ├── Questions.txt ├── README.md ├── Slide01-C-Thm.pdf ├── Slide01-C-Thm.tex ├── Slide02-C-Questions.pdf ├── Slide02-C-Questions.tex ├── fig │ ├── TIOBE.PNG │ ├── aligned.pdf │ ├── aligned.xml │ ├── datatype.jpg │ └── memoryLayoutC.jpg └── mypackage.tex ├── Compilers ├── 1_introduction.tex ├── 2_lexical.tex ├── 3_syntax.tex ├── 4_semantic.tex ├── 5_runtime.tex ├── 6_codegen.tex ├── README.md ├── fig │ ├── AST.png │ ├── LR1-eg.jpg │ ├── T06.pdf │ ├── T07-1.pdf │ ├── T07-2.pdf │ ├── compiler_flow.jpg │ ├── copying_collector.jpg │ ├── dag-1.pdf │ ├── dag-2.pdf │ ├── follow_eg1.jpg │ ├── follow_eg2.jpg │ ├── mark_and_compact.jpg │ ├── reference_counting.jpg │ ├── shift-reduce.jpg │ ├── stack.jpg │ └── three-addr-code-dag.jpg ├── main.pdf └── main.tex ├── ComputerNetworking ├── 1_introduction.tex ├── 2_physical_layer.tex ├── 3_data_link_layer.tex ├── 4_network_layer.tex ├── 5_transport_layer.tex ├── 6_application_layer.tex ├── 7_summary.tex ├── README.md ├── fig │ ├── 802-11-frame-2.png │ ├── 802-11-frame-3.png │ ├── 802-11-frame.png │ ├── 802-11.png │ ├── ARP.PNG │ ├── CRC.PNG │ ├── EUI-64.png │ ├── FTP.png │ ├── IP_datagram.png │ ├── LSA-graph.png │ ├── MOSPF.png │ ├── NLRI.png │ ├── PPP.PNG │ ├── SACK.PNG │ ├── SMTP.png │ ├── TCP-transition.PNG │ ├── TCP-window.PNG │ ├── TCP.PNG │ ├── auto-MDIX.png │ ├── bridge.jpg │ ├── checksum.PNG │ ├── conflict_count.jpg │ ├── congression-AIMD.PNG │ ├── congression-slow-start.PNG │ ├── dns-example.png │ ├── extended-LAN.png │ ├── go-back-n.png │ ├── http-msg.png │ ├── http-respond.png │ ├── ieee802.PNG │ ├── ip-packet.png │ ├── ip_mac.png │ ├── ipencap.png │ ├── ipv6-format.png │ ├── ipv6-group-broadcast.png │ ├── ipv6-network-prefix.png │ ├── mac_address.jpg │ ├── multicast.png │ ├── my-router-table.png │ ├── nagle.PNG │ ├── network-flow.PNG │ ├── network-headers.pdf │ ├── network-manchester.pdf │ ├── network.drawio │ ├── next-header.png │ ├── non-pipelining-http.png │ ├── ospf_example.jpg │ ├── ospf_lsa.jpg │ ├── out-of-band.png │ ├── pipelining-http.png │ ├── protocol_family.png │ ├── reverse_path_broadcasting.png │ ├── rip_timer.png │ ├── route_all_example.jpg │ ├── router_table_example.jpg │ ├── selective-repeat.png │ ├── spanning_tree.jpg │ ├── switch-crossbar.png │ ├── switch-share.png │ ├── switch.png │ ├── tcp_close.png │ ├── tcp_connect.png │ ├── tcp_example.jpg │ ├── tcp_format.png │ ├── token-ring.png │ ├── udp_format.png │ ├── v4-to-v6.png │ ├── virtual_circuit.png │ ├── vlan.jpg │ └── wlan-example.png ├── main.pdf └── main.tex ├── ComputerOrganization ├── 0_introduction.tex ├── 1_data_representation.tex ├── 2_instruction_system.tex ├── 3_arithmetic.tex ├── 4_processor.tex ├── 5_memory.tex ├── 6_io.tex ├── 7_bus.tex ├── Lectures │ ├── Slide00-Introduction.pdf │ ├── Slide00-Introduction.tex │ ├── Slide01-Digital_Circuits.tex │ ├── Slide02-Processors.pdf │ ├── Slide02-Processors.tex │ └── fig │ │ ├── Lecture1 │ │ ├── boolean_algebra.PNG │ │ ├── boolean_algebra_axioms.PNG │ │ ├── boolean_algebra_law.PNG │ │ ├── boolean_to_logic_gate.PNG │ │ ├── delay_vs_size.PNG │ │ ├── digit_analog_comparison.PNG │ │ ├── noise_margin.PNG │ │ ├── organization.PNG │ │ └── type_of_digital_circuits.PNG │ │ └── Lecture2 │ │ ├── 1bit-ff.jpg │ │ ├── 3-8decoder.jpg │ │ ├── 8-1mux.jpg │ │ ├── 8bit-latch.jpg │ │ ├── 8bit-latch_2.jpg │ │ ├── Abstraction-Computation.pdf │ │ ├── Abstraction-FA.pdf │ │ ├── Abstraction-Function_Call.pdf │ │ ├── Abstraction-HA.pdf │ │ ├── Abstraction-Processor.pdf │ │ ├── Abstraction-nFA.pdf │ │ ├── Abstraction.xml │ │ ├── Core-i7-cache.jpg │ │ ├── Datapath-All.pdf │ │ ├── Datapath-Control-signals.pdf │ │ ├── Datapath-Multi-State.pdf │ │ ├── Datapath-Multi.pdf │ │ ├── Datapath-PC.pdf │ │ ├── Datapath-Princeton.pdf │ │ ├── Datapath-R-I.pdf │ │ ├── Datapath-R-I_2.pdf │ │ ├── Datapath-R-R.pdf │ │ ├── Datapath-State.pdf │ │ ├── Datapath-branch.pdf │ │ ├── Datapath-jal.pdf │ │ ├── Datapath-jalr.pdf │ │ ├── Datapath-lw.pdf │ │ ├── Datapath-sw.pdf │ │ ├── Datapath.xml │ │ ├── MIPS.pdf │ │ ├── Von-Neumann-Architecture-Diagram.jpg │ │ ├── Von_Neumann_Architecture.png │ │ ├── aligned.PNG │ │ ├── b-imm.PNG │ │ ├── base32.PNG │ │ ├── branch.PNG │ │ ├── clock_1.PNG │ │ ├── clock_rate-vs-cpi.PNG │ │ ├── d-ff.png │ │ ├── d-ff_2.jpg │ │ ├── d-latch.png │ │ ├── ff_1.jpg │ │ ├── ff_2.jpg │ │ ├── ff_3.jpg │ │ ├── ff_4.jpg │ │ ├── general_registers.PNG │ │ ├── halting_problem.png │ │ ├── i-imm.PNG │ │ ├── imm.PNG │ │ ├── j-imm.PNG │ │ ├── jal.PNG │ │ ├── jalr.PNG │ │ ├── lambda_formula.jpg │ │ ├── lambda_formula_2.jpg │ │ ├── lw-sw.PNG │ │ ├── lw.PNG │ │ ├── memoryLayoutC.jpg │ │ ├── memory_addressing.PNG │ │ ├── misc-mem.PNG │ │ ├── nop.PNG │ │ ├── opcode_map.PNG │ │ ├── overview.PNG │ │ ├── processor_performance-2.PNG │ │ ├── processor_performance.PNG │ │ ├── program_compilation_overview.PNG │ │ ├── r-i.PNG │ │ ├── r-i_2.PNG │ │ ├── r-r.PNG │ │ ├── register_files.PNG │ │ ├── rvi_formats.PNG │ │ ├── rvi_formats_complex.PNG │ │ ├── sr-latch.jpg │ │ ├── sw.PNG │ │ ├── system.PNG │ │ └── turing_machine.jpg ├── README.md ├── fig │ ├── Arithmetic-Division.pdf │ ├── Arithmetic-Mulplication.pdf │ ├── Arithmetic.xml │ ├── Cache-combine.pdf │ ├── Cache-page.pdf │ ├── Cache-page_address.pdf │ ├── Cache.xml │ ├── Datapath-Multi-cycle.pdf │ ├── Datapath.xml │ ├── Datapath_All.pdf │ ├── Datapath_add.pdf │ ├── Datapath_beq.pdf │ ├── Datapath_jump.pdf │ ├── Datapath_lw.pdf │ ├── Datapath_ori.pdf │ ├── Datapath_sw.pdf │ ├── Memory-Double_Decode.pdf │ ├── Memory.xml │ ├── basic_datapath.PNG │ ├── microinstruction.png │ ├── schedule.pdf │ ├── schedule.xml │ ├── sram_cycle.png │ ├── word-capacity.PNG │ └── word-length.PNG ├── main.pdf └── main.tex ├── DatabaseSystems ├── 10_recovery.tex ├── 1_introduction.tex ├── 2_sql_intro.tex ├── 3_high-level_sql.tex ├── 4_relational_algebra.tex ├── 5_ER-model.tex ├── 6.5_storage.tex ├── 6_database_design.tex ├── 7_index.tex ├── 8_transaction.tex ├── 9_concurrency.tex ├── README.md ├── fig │ ├── E-R-figure.png │ ├── RAID.png │ ├── b-tree.png │ ├── bitmap.png │ ├── bp-tree.png │ ├── bp-tree_deletion.png │ ├── bp-tree_deletion2.png │ ├── bp-tree_deletion3.png │ ├── bp-tree_deletion_alg.png │ ├── bp-tree_file_organization.png │ ├── bp-tree_insertion.png │ ├── bp-tree_insertion2.png │ ├── bp-tree_insertion3.png │ ├── checkpoints_eg.png │ ├── conflict_serializability.png │ ├── deadlock.jpg │ ├── dense_sparse_indexing.png │ ├── extendable_hash.png │ ├── extendable_hash_eg.png │ ├── free_lists.png │ ├── hash_index.png │ ├── lock_table.png │ ├── mapping_radix.png │ ├── multilevel-index.png │ ├── multitable_clustering.png │ ├── recovery.png │ ├── relationship_set(description).png │ ├── relationship_set.png │ ├── slotted_page.png │ ├── transaction_state.png │ ├── tree_protocol.png │ ├── university_E-R.png │ ├── university_schema_diagram.png │ ├── variable-length.png │ ├── view_of_data.png │ └── wait-for_graph.png ├── main.pdf └── main.tex ├── DeepLearning ├── 1_introduction.tex ├── 2_ann.tex ├── 3_optimization.tex ├── 4_issues.tex ├── 5_segmentation.tex ├── README.md ├── dl.bib ├── fig │ ├── deconvolution.jpg │ ├── deeplabv1.jpg │ ├── deeplabv2.jpg │ ├── deeplabv3+.jpg │ ├── deeplabv3.jpg │ ├── dilated_convolution.png │ ├── dl_flowchart.jpg │ ├── dl_venn.jpg │ ├── dropout.jpg │ ├── encoder-decoder.jpg │ ├── fcn.jpg │ ├── gd_catalog.jpg │ ├── lenet.png │ ├── mlp.png │ ├── neuron_model.png │ ├── normalization.jpg │ ├── pspnet.jpg │ ├── residual_block.jpg │ └── unet.jpg ├── main.pdf └── main.tex ├── DigitalCircuits ├── 1_introduction.tex ├── 2_integrated_circuit.tex ├── 3_combinitorial_circuit.tex ├── 4_sequential_circuit.tex ├── 5_circuit_design.tex ├── README.md ├── fig │ ├── 12system_protues.PNG │ ├── 12system_state.pdf │ ├── 12system_wave.PNG │ ├── 555timer.jpg │ ├── 74hc163.PNG │ ├── 74ls93.PNG │ ├── JK_flip-flop.png │ ├── Karnaugh_12system.pdf │ ├── Karnaugh_example.png │ ├── Karnaugh_graph.png │ ├── adder.jpg │ ├── circuit_machine.xml │ ├── cmos.PNG │ ├── cmos_ex.PNG │ ├── comparator.jpg │ ├── counter.jpg │ ├── decoder.jpg │ ├── design_flow.png │ ├── encoder.jpg │ ├── flip-flops.png │ ├── frequency_divisor.jpg │ ├── johnson.jpg │ ├── latches.pdf │ ├── latches.xml │ ├── logic_gates.PNG │ ├── mealy_machine.pdf │ ├── moore_machine.pdf │ ├── multiplexer.jpg │ ├── nonideal_pulse_characteristics.jpg │ ├── one-shot.jpg │ ├── parallel_adder.jpg │ ├── ring_counter.jpg │ ├── three_gates.PNG │ ├── timer.PNG │ ├── ttl.PNG │ ├── ttl_ex.PNG │ ├── word-capacity.PNG │ └── word-length.PNG ├── main.pdf └── main.tex ├── DigitalImageProcessing ├── 1_introduction.tex ├── 2_image_enhancement.tex ├── 3_frequency_domain.tex ├── 4_restoration.tex ├── 5_colorful_images.tex ├── 6_morphological_images.tex ├── 7_image_segmentation.tex ├── 8_representation.tex ├── README.md ├── fig │ ├── BSF.png │ ├── Laplacian.png │ ├── LoG.png │ ├── MPP.png │ ├── adjacency.png │ ├── bitmap-dollars.png │ ├── bitmap.png │ ├── boundary_tracing.png │ ├── canny-margin.png │ ├── chain_code.png │ ├── chain_code_difference.png │ ├── closing.png │ ├── complements.png │ ├── convex_hull_SE.png │ ├── convolution_and_correlation.png │ ├── differential.png │ ├── dilation.png │ ├── edge_detect_noise.png │ ├── edge_detect_op.png │ ├── edge_model.png │ ├── enhancement.png │ ├── erosion.png │ ├── eye.png │ ├── filter_pass.png │ ├── fourier_descriptor.png │ ├── fourier_spectrum.png │ ├── granulometry.png │ ├── high-boost.png │ ├── histogram-normalization.png │ ├── histogram-quality.png │ ├── hit-or-miss.png │ ├── homo_filter.png │ ├── hough.png │ ├── hough2.png │ ├── impulse.png │ ├── laplacian_mask.png │ ├── line_approx.png │ ├── line_detect.png │ ├── noise.png │ ├── notch.png │ ├── nyquist.png │ ├── opening.png │ ├── polygon_boundary.png │ ├── pseudo-contour.png │ ├── region_segmentation.png │ ├── restoration.png │ ├── rgb_and_cmyk.png │ ├── salt-and-pepper-noise.png │ ├── shape_number.png │ ├── signature.png │ ├── simultaneous-contrast.png │ ├── skeletons.png │ ├── skeletons2.png │ ├── sobel_and_robert.png │ ├── spectrum_angle_example.png │ ├── statistical_moment.png │ ├── texture_segment.png │ ├── top-hat.png │ ├── trans-by-cases.png │ ├── trans-cut.png │ ├── trans-gamma.png │ ├── trans-inverse.png │ ├── trans-log.png │ ├── trans-power.png │ ├── undershoot-overshoot.png │ └── webb.png ├── main.pdf └── main.tex ├── MachineLearning ├── 10_dimension_reduction.tex ├── 11_feature_selection.tex ├── 12_semi-supervised.tex ├── 13_recommendation_system.tex ├── 1_introduction.tex ├── 2_basic_concepts.tex ├── 3_linear_model.tex ├── 4_decision_tree.tex ├── 5_neural_network.tex ├── 6_svm.tex ├── 7_bayesian.tex ├── 8_ensemble_learning.tex ├── 9_clustering.tex ├── README.md ├── fig │ ├── A-Look-at-Machine-Learning-Evolution.png │ ├── AGNES.jpg │ ├── BP.png │ ├── DBSCAN.jpg │ ├── DT-boundary.png │ ├── ID3.png │ ├── LDA.png │ ├── LLE.png │ ├── LeNet.png │ ├── MP-neuron.png │ ├── SVM.png │ ├── SVR.png │ ├── TSVM.png │ ├── adaboost.jpg │ ├── attention.png │ ├── bert.png │ ├── bias-var.png │ ├── decision_tree.jpg │ ├── ensemble-learning.png │ ├── kNN.png │ ├── linear-separable.png │ ├── multihead.png │ ├── multivar-DT.png │ ├── perceptron.png │ ├── semi-supervised_learning.jpg │ ├── soft-margin-loss.png │ ├── stacking.jpg │ ├── transformer.png │ └── xor-nonlinear.png ├── main.pdf ├── main.tex └── reference.tex ├── MathematicalLogic ├── 1_propositional_logic.tex ├── 2_predicate_logic.tex ├── 3_verification.tex ├── 4_program_verification.tex ├── 5_modal_logic.tex ├── 6_bdd.tex ├── README.md ├── fig │ ├── bdd_limitation.jpg │ ├── bdd_reduction1.jpg │ ├── bdd_reduction2.jpg │ ├── bdd_reduction3.jpg │ ├── free_bound_var.jpg │ ├── kripke_model.jpg │ ├── ltl_ctl.jpg │ ├── mutual_exclusion.jpg │ ├── parse_tree_dag_eg.jpg │ ├── reduce_alg.jpg │ ├── restrict_alg.jpg │ ├── shortcut_bdd.jpg │ ├── transition_system.jpg │ └── well-formed_formula.jpg ├── main.pdf └── main.tex ├── NaturalLanguageProcessing ├── 1_introduction.tex ├── 2_cut.tex ├── 3_model.tex ├── 4_syntax.tex ├── 5_question_and_answering.tex ├── 6_machine_translation.tex ├── README.md ├── fig │ ├── CFG.png │ ├── PCFG.png │ ├── RNN.png │ ├── attention.png │ ├── best_path.png │ ├── chart_parsing.png │ ├── chart_parsing_2.png │ ├── chart_parsing_3.png │ ├── dependency_parser.png │ ├── dependency_tree.png │ ├── entropy.png │ ├── example-based_mt.png │ ├── max_matching.png │ ├── n-gram.png │ ├── projective.png │ ├── rule-based_mt.png │ ├── word_pos_labeling.png │ └── word_seq_labeling.png ├── main.pdf └── main.tex ├── OperatingSystems ├── 1_introduction.tex ├── 2_processes.tex ├── 3_threads.tex ├── 4_concurrrency.tex ├── 5_deadlock.tex ├── 6_memory.tex ├── 7_scheduling.tex ├── 8_IO.tex ├── 9_file_system.tex ├── README.md ├── fig │ ├── 5-state-process-model.jpg │ ├── C_memory.jpg │ ├── OS.drawio │ ├── OS.pdf │ ├── SPOOLing.png │ ├── TLB.png │ ├── VRR.png │ ├── deadlock.png │ ├── deadlock2.png │ ├── dynamic_partition.png │ ├── feedback.png │ ├── kernels.png │ ├── memory_address_transformation.png │ ├── process_scheduling.png │ ├── relocation.png │ ├── resource_graph.png │ ├── short-term-scheduling-all-2.png │ ├── short-term-scheduling-all.png │ ├── threads.png │ └── two-level_paging.png ├── main.pdf └── main.tex ├── PatternRecognition ├── 1_introduction.tex ├── 2_bayesian.tex ├── 3_maximum_likelihood.tex ├── 4_non-param.tex ├── 5_linear_discriminant.tex ├── 6_neural_network.tex ├── 7_stochastic_methods.tex ├── README.md ├── appendices.tex ├── fig │ ├── Fisher.png │ ├── SVM.png │ ├── bayesian_decision.png │ ├── bayesian_est.png │ ├── biased_ML_Gaussian.png │ ├── boltzmann_learning.png │ ├── covariance_matrix.png │ ├── forward_propagation.png │ ├── kNN_error_bound.png │ ├── kn-neighbor.png │ ├── linear_decision_boundary.png │ ├── nearest_neighbors.png │ ├── simulated_annealing.png │ ├── soft_margin.png │ ├── stochastic_opt_problem.png │ └── vc_dimension.png ├── main.pdf └── main.tex └── README.md /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "mylatextmpl"] 2 | path = mylatextmpl 3 | url = https://github.com/chhzh123/mylatextmpl 4 | -------------------------------------------------------------------------------- /ArtificialIntelligence/1_introduction.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{简介} 4 | \subsection{概述} 5 | \begin{itemize} 6 | \item 1997 Deep Blue 7 | \item 2011 IBM Watson 8 | \item 2016 Google DeepMind 9 | \end{itemize} 10 | 11 | 什么是AI？ 12 | \begin{itemize} 13 | \item 像人类一样思考(thinking humanly)：中文屋子 14 | \item 理智思考(thinking rationally) 15 | \item 像人类一样行为(acting humanly)：图灵测试(1950) 16 | \item 理智行为(acting rationally) 17 | \end{itemize} 18 | 19 | 常见术语 20 | \begin{itemize} 21 | \item 强AI：机器像人类一样思考 22 | \item 弱AI：机器有智能的行为 23 | \item 通用AI(AGI)：能够解决任何问题 24 | \item 窄AI：专注于某一特定任务 25 | \end{itemize} 26 | 27 | 不以模拟人类作为实现人工智能的最好方法 28 | \begin{itemize} 29 | \item 计算机和人类的体系结构不同：数值计算、视觉、并行处理 30 | \item 对人类大脑的了解太少了！ 31 | \end{itemize} 32 | 33 | \subsection{历史} 34 | \begin{itemize} 35 | \item 1950-70: Early excitement, great expections 36 | \begin{itemize} 37 | \item Samuel(1952)跳棋程序 38 | \item Newell(1955)逻辑理论家 39 | \item Dartmouth会议(1956)：AI诞生 40 | \end{itemize} 41 | \item 1970-90: Knowledge is power 42 | \item 1990-: rise of machine learning "AI Spring" 43 | \item 2010-: Deep learning 44 | \end{itemize} -------------------------------------------------------------------------------- /ArtificialIntelligence/README.md: -------------------------------------------------------------------------------- 1 | # Artificial Intelligence 2 | 3 | 人工智能笔记，所用教材Stuart Russell, Peter Norvig, *Artificial Intelligence: A Modern Approach (3rd ed)* 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\newenvironment{bayesian}{\begin{center}\begin{tikzcd}[cells={nodes={draw=black, circle}}]}{\end{tikzcd}\end{center}} 8 | 9 | \def\Ent{\mathrm{Ent}} 10 | 11 | \title{人工智能笔记} 12 | \author{陈鸿峥} 13 | \date{{\builddatemonth\today}\protect\footnote{\text{Build \builddate\today}}} % protect! 14 | 15 | \setcounter{secnumdepth}{5} 16 | 17 | \begin{document} 18 | 19 | \maketitle 20 | \renewcommand{\thefootnote}{\arabic{footnote}} 21 | \setcounter{footnote}{0} 22 | 23 | \setcounter{tocdepth}{2}%设置深度 24 | \tableofcontents 25 | 26 | \bigskip\bigskip 27 | 28 | 本课程主要选用Stuart Russell和Peter Norvig的《人工智能---一种现代化方法》(Artificial Intelligence: A Modern Approach)一书，理论及实验作业请见\url{https://github.com/chhzh123/Assignments/tree/master/ArtificalIntelligence}，内容非常详实，基本涵盖了人工智能的几大板块。 29 | 30 | \input{1_introduction} 31 | \input{2.1_uninformed_search} 32 | \input{2.2_heuristic_search} 33 | \input{2.3_game_tree_search} 34 | \input{3_CSP} 35 | \input{4_knowledge_representation} 36 | \input{5_planning} 37 | \input{6_uncertainty} 38 | \input{7_machine_learning} 39 | 40 | \end{document} -------------------------------------------------------------------------------- /C-Programming/C-Structure.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/C-Programming/C-Structure.pdf -------------------------------------------------------------------------------- /C-Programming/README.md: -------------------------------------------------------------------------------- 1 | # C Programming 2 | 3 | C语言基本理论知识介绍，用于上课 -------------------------------------------------------------------------------- /C-Programming/Slide01-C-Thm.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/C-Programming/Slide01-C-Thm.pdf 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7ZvLcpswGIWfxsvMIAQClombppuuvPCyI4MwajFyQK7tPn2FETEIMr2MsIiSleHXBek7GM6ZsRdwuTs9lXiffWUJyReuk5wW8NPCdYHnIvFRV85NJQiiprAtaSI7XQsr+ovIoiOrB5qQqteRM5Zzuu8XY1YUJOa9Gi5Ldux3S1nev+oeb8mgsIpxPqyuacKzphq6wbX+hdBt1l4ZILm/HW47y51UGU7YsVOCjwu4LBnjzdHutCR5Da/l0oz7/Erry8JKUvC/GbD+7p5BtQE+T/0o/hasn1f0Ts7yE+cHuWG5WH5uCZTsUCSknsRZwIdjRjlZ7XFctx6F5qKW8V0uzoA4TGmeL1nOystYmGASprGoV7xkP0inBcUh2aSiZbiNdk2k5OTUKcltPRG2I7w8iy5tK5KI5T0GWuTHjmKylHXE8mQNy3tk+zLzFaM4kCT/gaprA9UXPHOhCm2gCt2ZUfVsoOo5M6PqW0E1nBlVZANVf25vq8AGqmhub6vQBqrB3N5WkQ1UVb8aGobaZq23TVX1q8apWpGtVL9qnKoV2Ur1q8apWpGtVL9qnKoV2Ur1q8apWpGtVL9qnKoV2Ur1q8apTpyt0jR141GqCdogH03jVz3TVCfOVrehqvpV41Qnzla3oar6VdNU2+W8baqqXzVOVXe20sEonBkj3UlJAyPVSxpnpDv3aGCkOkPjjHSnGA2MVJ9nnNHEmYSAxCfB2FM+QgHEE/k81zGNVXco0QBJtW3mIU2eMQh6xWEE0cZxpvFt5rFOHjJugVU1buaxjqUMlPMaEBPb7PJFzwfWNtxVl5+G3YsOAsnp2iiOtvUnrQc3E4mFNXM1LQPZBDve16avQcEKoggmSzin20KcxkIRIuoPtRI0xvm9bNjRJKkvM3oz9G8XHQ9s0FfXH4rrjYjrTiUuHAs7OsRN2GEjxr03faO+vNFQXnRTecdS14e8uuQF0LS+Y06/0aTa4+L/9Y0zXHbUbSazXV0QKK9eYFresZCi4+uLvLsNfX9v38GPDof6gtsKPBaXdAgM3Q+Bu99ocwqPZT0dT+gqYyV/d49oCP/8iAaaHLQ4vf7X49LW+ccMfPwN 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Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, \emph{Compilers: Principles, Techniques \& Tools (2nd ed)}，即大名鼎鼎的龙书。 4 | 同时也参考了\href{http://web.stanford.edu/class/cs143/}{Stanford CS143: Compilers}这门课程。 5 | 6 | \section{简介} 7 | 编译器的几个阶段如下，前端包括词法(lexical)、语法(syntax)、语义(semantic)分析，中端IR生成、优化，后端代码生成。 8 | \begin{figure}[H] 9 | \centering 10 | \includegraphics[width=0.6\linewidth]{fig/compiler_flow.jpg} 11 | \end{figure} 12 | 13 | 编程语言设计的思想： 14 | \begin{itemize} 15 | \item 抽象(abstraction)：核心在于信息隐藏(infomation hiding)，只把必要的暴露出来 16 | \item 类型(types)：表达抽象、查出常见错误、使程序安全 17 | \item 重用(reuse)：开发软件系统中常见的模式（类型参数化、类与继承） 18 | \end{itemize} -------------------------------------------------------------------------------- /Compilers/5_runtime.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{运行时系统} 4 | \subsection{存储管理} 5 | \begin{figure}[H] 6 | \centering 7 | \includegraphics[width=0.5\linewidth]{fig/stack.jpg} 8 | \end{figure} 9 | 10 | \subsection{垃圾回收} 11 | \begin{itemize} 12 | \item 引用计数(reference counting)：创建加1，删除减1 13 | \begin{itemize} 14 | \item 简单、立即增量式回收 15 | \item 不能回收循环引用的示例 16 | \end{itemize} 17 | \begin{figure}[H] 18 | \centering 19 | \includegraphics[width=0.8\linewidth]{fig/reference_counting.jpg} 20 | \end{figure} 21 | \item 标记扫除(mark and sweep)：做图深搜找连通块 22 | \begin{itemize} 23 | \item 有办法清除循环引用 24 | \item 大量垃圾时效率低，无法满足实时应用 25 | \end{itemize} 26 | \item 标记压缩(mark and compact)：标记，计算新地址，拷贝对象到新地址并更新引用 27 | \begin{figure}[H] 28 | \centering 29 | \includegraphics[width=0.8\linewidth]{fig/mark_and_compact.jpg} 30 | \end{figure} 31 | \item 拷贝收集(copying collector)：堆被划分为两个区域，可达对象一旦被发现就会立即被移动，但不可达对象不做改动 32 | \begin{figure}[H] 33 | \centering 34 | \includegraphics[width=0.8\linewidth]{fig/copying_collector.jpg} 35 | \end{figure} 36 | \end{itemize} 37 | 38 | JVM采用了两代(young \& old)的方式，对于年轻的对象采用拷贝收集，对于老的对象则采用标记压缩。 -------------------------------------------------------------------------------- /Compilers/README.md: -------------------------------------------------------------------------------- 1 | # Principles of Compilers 2 | 3 | 编译器原理笔记，所用教材Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, *Compilers: Principles, Techniques & Tools* (2nd ed), [Online](https://suif.stanford.edu/dragonbook/) 4 | 5 | * 勘误表见[Errata](http://infolab.stanford.edu/~ullman/dragon/errata.html) -------------------------------------------------------------------------------- /Compilers/fig/AST.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/Compilers/fig/AST.png -------------------------------------------------------------------------------- /Compilers/fig/LR1-eg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/Compilers/fig/LR1-eg.jpg -------------------------------------------------------------------------------- /Compilers/fig/T06.pdf: -------------------------------------------------------------------------------- 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| \usepackage{footnote} 4 | \usepackage{forest} 5 | \makesavenoteenv{tabular} 6 | \usetikzlibrary{automata,backgrounds,fit,shapes,positioning} 7 | 8 | \tikzset{->, % makes the edges directed 9 | >=stealth, % makes the arrow heads bold 10 | node distance=2cm, % specifies the minimum distance between two nodes. Change if necessary. 11 | every state/.style={thick, fill=gray!10}, % sets the properties for each 'state' node 12 | } 13 | 14 | \renewcommand{\thefootnote}{\fnsymbol{footnote}} 15 | 16 | \title{编译原理笔记} 17 | \author{陈鸿峥} 18 | \date{{\builddatemonth\today}\protect\footnote{\text{Build \builddate\today}}} % protect! 19 | 20 | \begin{document} 21 | 22 | \maketitle 23 | \renewcommand{\thefootnote}{\arabic{footnote}} 24 | \setcounter{footnote}{0} 25 | 26 | \setcounter{tocdepth}{2}%设置深度 27 | \tableofcontents 28 | 29 | \bigskip\bigskip 30 | 31 | \input{1_introduction} 32 | \input{2_lexical} 33 | \input{3_syntax} 34 | \input{4_semantic} 35 | \input{5_runtime} 36 | \input{6_codegen} 37 | 38 | \end{document} -------------------------------------------------------------------------------- /ComputerNetworking/7_summary.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{总结} 4 | \begin{figure}[H] 5 | \centering 6 | \includegraphics[width=\linewidth]{fig/network-headers.pdf} 7 | \end{figure} 8 | 9 | \section{参考资料} 10 | \begin{enumerate} 11 | \item 计算机网络总结，\url{https://blog.csdn.net/n1neding/article/list/7?t=1&} 12 | \end{enumerate} -------------------------------------------------------------------------------- /ComputerNetworking/README.md: -------------------------------------------------------------------------------- 1 | # Computer Networking 2 | 3 | 计算机网络笔记，所用教材：James F. 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\tableofcontents 20 | 21 | \bigskip\bigskip 22 | 本课程使用的教材为James F.~Kurose和Keith W.~Ross的《计算机网络---自顶向下方法（第七版）》。 23 | 24 | \input{1_introduction} 25 | \input{2_physical_layer} 26 | \input{3_data_link_layer} 27 | \input{4_network_layer} 28 | \input{5_transport_layer} 29 | \input{6_application_layer} 30 | \input{7_summary} 31 | 32 | \end{document} -------------------------------------------------------------------------------- /ComputerOrganization/7_bus.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{总线} 4 | \subsection{总线概述} 5 | 总线：在多个部件之间实现互连，用于分时共享方式传输公共信息的一组数据通路 6 | \par常见IO总线：ISA、USB、PCI、VL-BUS 7 | \par现行PC机主要系统总线是PCI和ISA 8 | \par三总线结构：处理机总线、PCI总线、ISA总线 9 | \par总线周期：通过总线完成一次内存或IO设备读写操作所需时间 10 | \par总线性能影响因素 11 | \begin{itemize} 12 | \item 长度 13 | \item 连接元件数目 14 | \end{itemize} 15 | 要在存储器与总线相连部分加三态门，防止随时读取数据（高阻态） 16 | \par 总线的分类 17 | \begin{itemize} 18 | \item 内部总线：寄存器、ALU 19 | \item 系统总线：CPU、MM、IO控制器 20 | \begin{itemize} 21 | \item 控制线：决定总线功能强弱、适应性（控制IO、读写等） 22 | \item 数据线：决定一次能传送数据的位数/能力 23 | \item 地址线：决定最大寻址空间 24 | \end{itemize} 25 | \item 通信/IO/外部总线：主机、IO设备 26 | \end{itemize} 27 | * 在系统总线的数据线上，不传输握手信号 28 | \par 总线传送控制/定时方式:总线在双方交换数据的过程中需要时间上配合关系的控制，实质是一种协议或规则 29 | \begin{itemize} 30 | \item 同步方式：统一时钟信号，电路简单，适合高速设备；时钟偏移，总线长度不能很长，按最慢的设置（CPU内部总线） 31 | \item 异步方式：比同步方式慢，总线频带窄，总线传输周期长，需要握手，用于不同存取速度设备（IO总线） 32 | \begin{itemize} 33 | \item 不互锁：发送接收完就不理对方状态 34 | \item 半互锁：发送方要得到接收方的相应才可进行其他事情 35 | \item 全互锁：来回握手，接收方再得到发送方确认才可进行其他事情 36 | \end{itemize} 37 | \item 半同步方式：wait/ready信号是单向的，不是互锁的；适用于系统工作速度不高，但又包含了许多工作速度差异较大的各类设备的简单系统 38 | \item 分离方式：总线读周期分成两个子周期（寻址+数据传送），在两子周期之间，退出总线，从设备准备数据；适用于有很多主模块（如多个处理器或多个DMA设备）的系统 39 | \end{itemize} 40 | 总线主设备 41 | \begin{itemize} 42 | \item 总线使用请求信号$\to$总线使用应答信号$\to$使用总线 43 | \item 中断请求信号$\to$中断相应信号和中断向量$\to$等待CPU处理 44 | \item DMA请求信号$\to$DMA应答信号$\to$数据交换$\to$撤销DMA请求信号 45 | \end{itemize} 46 | 总线操作五个步骤 47 | \begin{itemize} 48 | \item 传输请求 49 | \item 总线仲裁 50 | \item 部件寻址 51 | \item 数据传输 52 | \item 总线释放 53 | \end{itemize} 54 | 55 | \subsection{总线设计} 56 | 高性能（宽通路，分离数据地址线）与低成本（窄通路，复用数据地址线）之间的权衡 57 | \par 复用：不同信号在同一总线上分时传送 58 | \par总线控制器：对存储空间进行分配、启动等，仲裁哪个主设备获得总线使用权 59 | \[\text{总线带宽（数据传输率）}=\text{总线宽度}/8b\times\text{总线时钟频率}\] 60 | \par注意区别总线周期（通过总线完成一次完整数据传输所需要的时间）与系统周期 61 | \par 总线的操作可分为传输请求、总线仲裁、部件寻址、数据传输、总线释放 62 | \begin{itemize} 63 | \item 主设备：能申请并获得总线控制权的设备 64 | \item 从设备：被主设备访问的设备 65 | \end{itemize} 66 | \par总线仲裁(arbiter)：总线主设备请求并获得总线控制权的过程 67 | \begin{itemize} 68 | \item 分布仲裁：自举裁决、冲突检测 69 | \item 集中仲裁：菊花链（串行逐一访问）、独立请求并行判优 70 | \end{itemize} 71 | 仲裁后，获得总线控制权的设备建立“总线忙”信号，用完即撤销 72 | \par 数据传输方式 73 | \begin{itemize} 74 | \item 基本：一个地址一个数据 75 | \item 成组/猝发(burst)：提高总线数据传输率，一个地址多个数据 76 | \end{itemize} -------------------------------------------------------------------------------- /ComputerOrganization/Lectures/Slide00-Introduction.pdf: -------------------------------------------------------------------------------- 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\end{figure} 33 | \end{frame} 34 | % 01.22 Bluespec SystemVerilog流程 35 | 36 | \begin{frame}{数字信号与模拟信号} 37 | % 01.7-14 38 | \begin{itemize} 39 | \item 模拟(analog)信号：连续 40 | \item 数字(digital)信号：离散，容许噪声，存在噪声容限(margin) 41 | \end{itemize} 42 | \begin{figure} 43 | \centering 44 | \includegraphics[width=0.8\linewidth]{fig/noise_margin.PNG} 45 | \end{figure} 46 | \end{frame} 47 | 48 | \begin{frame}{数字模拟信号对比} 49 | \begin{figure} 50 | \centering 51 | \includegraphics[width=\linewidth]{fig/digit_analog_comparison.PNG} 52 | \end{figure} 53 | \end{frame} 54 | 55 | \begin{frame}{数字电路} 56 | \begin{figure} 57 | \centering 58 | \includegraphics[width=\linewidth]{fig/type_of_digital_circuits.PNG} 59 | \end{figure} 60 | \end{frame} 61 | 62 | \begin{frame}{布尔代数} 63 | \begin{figure} 64 | \centering 65 | \includegraphics[width=\linewidth]{fig/boolean_algebra.PNG} 66 | \end{figure} 67 | \end{frame} 68 | 69 | \begin{frame}{布尔代数公理} 70 | \begin{figure} 71 | \centering 72 | \includegraphics[width=\linewidth]{fig/boolean_algebra_axioms.PNG} 73 | \end{figure} 74 | \end{frame} 75 | 76 | \begin{frame}{布尔代数性质} 77 | \begin{figure} 78 | \centering 79 | \includegraphics[width=\linewidth]{fig/boolean_algebra_law.PNG} 80 | \end{figure} 81 | \end{frame} 82 | 83 | \begin{frame}{逻辑门} 84 | \begin{figure} 85 | \centering 86 | \includegraphics[width=\linewidth]{fig/boolean_to_logic_gate.PNG} 87 | \end{figure} 88 | \end{frame} 89 | 90 | \begin{frame}{设计的折衷：时延与面积} 91 | \begin{figure} 92 | \centering 93 | \includegraphics[width=\linewidth]{fig/delay_vs_size.PNG} 94 | \end{figure} 95 | \end{frame} 96 | 97 | \end{document} -------------------------------------------------------------------------------- /ComputerOrganization/Lectures/Slide02-Processors.pdf: -------------------------------------------------------------------------------- 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Hennessy, ***Computer Organization and Design***, Elsevier, 2014 5 | 6 | 1. 计算机系统概述 7 | 2. 数据的表示与存储 8 | 3. 指令系统 9 | 4. 计算机的运算 10 | 5. 处理器 11 | 6. 存储器的层次结构 12 | 7. 输入输出系统 13 | 8. 总线 14 | 15 | ## Lectures 16 | 主要参考Daniel Sanchez (MIT), Computer System Architecture (6.823)课程，目前涉及的内容包括 17 | 18 | * 计算机概述 19 | * 计算模型 20 | * 数字电路 21 | * RISC-V指令集架构 22 | * 单/多周期CPU数据通路 -------------------------------------------------------------------------------- /ComputerOrganization/fig/Arithmetic-Division.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/ComputerOrganization/fig/Arithmetic-Division.pdf -------------------------------------------------------------------------------- /ComputerOrganization/fig/Arithmetic-Mulplication.pdf: -------------------------------------------------------------------------------- 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/ComputerOrganization/main.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/ComputerOrganization/main.pdf -------------------------------------------------------------------------------- /ComputerOrganization/main.tex: -------------------------------------------------------------------------------- 1 | \documentclass{note} 2 | \usepackage[bigstrut]{mypackage} 3 | 4 | \def\ssim{{\scriptstyle{\sim}}} 5 | \renewcommand{\mod}{\mathrm{mod}\;} 6 | 7 | \renewcommand{\thefootnote}{\fnsymbol{footnote}} 8 | 9 | \title{计算机组成原理笔记} 10 | \author{陈鸿峥} 11 | \date{{\builddatemonth\today} \footnote{\text{Build \builddate\today}}}%加了build 12 | 13 | \begin{document} 14 | 15 | \maketitle 16 | \renewcommand{\thefootnote}{\arabic{footnote}} 17 | \setcounter{footnote}{0} 18 | 19 | \setcounter{tocdepth}{2}%设置深度 20 | \tableofcontents 21 | 22 | \bigskip\bigskip 23 | 本课程使用的教材为David A.Patterson (UCB), John L.Hennessy (Stanford), 《计算机组成与设计（硬件软件接口）》. 24 | 25 | \input{0_introduction} 26 | \input{1_data_representation} 27 | \input{2_instruction_system} 28 | \input{3_arithmetic} 29 | \input{4_processor} 30 | \input{5_memory} 31 | \input{6_io} 32 | \input{7_bus} 33 | 34 | \end{document} -------------------------------------------------------------------------------- /DatabaseSystems/3_high-level_sql.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{进阶SQL} % Chap 4 4 | \subsection{内外连接} 5 | \verb'on'条件可以提供比自然连接更为丰富的连接条件 6 | \begin{lstlisting} 7 | select * 8 | from students join takes on student.ID = takes.ID; -- condition 9 | \end{lstlisting} 10 | 11 | 直接使用\verb'natural join'可能导致元组丢失，比如有一些学生没有选修任何课程，则其在student关系中不会与takes关系中任何元组配对。 12 | 因此有\textbf{外连接}： 13 | \begin{itemize} 14 | \item 左外连接(left outer join)：只保留出现在左外连接运算之前（左边）关系中的元组，若右侧关系中没有对应属性则用null代替 15 | \item 右外连接(right outer join) 16 | \item 全外连接(full outer join)：左外连接和右外连接的组合 17 | \end{itemize} 18 | 19 | 从而可以很容易查询出“所有课程一门也没有选修的学生”： 20 | \begin{lstlisting} 21 | select ID 22 | from student natural left outer join takes 23 | where course_id is null; 24 | \end{lstlisting} 25 | 26 | 为了将常规连接和外连接区分开，SQL中将常规连接称为内连接(inner join)，默认的都是内连接。 27 | 28 | \subsection{视图} 29 | 让用户看到整个逻辑模型显然是不合适的，出于安全考虑，可能需要向用户隐藏特定的数据。 30 | 本来通过select可以把需要的数据计算并存储下来，但一旦底层数据发生变化，查询的结果就不再匹配。 31 | 因此，为了解决这样的问题，SQL提供一种虚关系，称为视图(view)，只有在使用的时候才会被计算。 32 | \begin{lstlisting} 33 | create view faculty as 34 | select ID, name, dept_name 35 | from instructor; 36 | \end{lstlisting} 37 | 之后便可以直接使用\verb'from'语句访问视图。 38 | 39 | \begin{definition}[物化视图(materialized view)] 40 | 如果用于定义视图的实际关系改变，视图也会跟着修改。 41 | \end{definition} 42 | 43 | 由于视图并不是数据库底层的关系，故一般数据库不允许对视图关系进行修改。 44 | 45 | \subsection{事务} 46 | SQL标准规定当一条SQL语句被执行，就隐式开始了一个事务。 47 | 下列SQL语句之一会结束一个事务： 48 | \begin{itemize} 49 | \item Commit work：将事务所做的更新在数据库中持久保存。 50 | 在事务被提交后，一个新的事务自动开始。 51 | \item Rollback work：撤销该事务中所有SQL语句对数据库的更新，数据库将恢复到执行该事务的第一条语句之前的状态。 52 | 如果遇断电，回滚会在下一次重启时自动执行。 53 | \end{itemize} 54 | 55 | \subsection{完整性约束} 56 | 单个关系上的约束 57 | \begin{itemize} 58 | \item \verb'not null'：跟在属性定义后面 59 | \item \verb'unique (A1,A2,...,Am)'：声明$A_1,\ldots,A_m$构成一个候选码 60 | \item \verb'check()'：关系中每个元组都必须满足该谓词，如\verb'check(budget>0)' 61 | \end{itemize} 62 | 63 | 参照完整性/子集依赖：外码 64 | \begin{lstlisting} 65 | foreign key (dept_name) references department 66 | on delete cascade 67 | on update cascade 68 | \end{lstlisting} 69 | 会级联(cascade)删除或更新。 70 | 71 | \subsection{授权} 72 | \verb'grant'授权，\verb'revoke'回收 73 | \begin{itemize} 74 | \item \verb'select'：允许用户修改关系中任意数组 75 | \item \verb'insert' 76 | \item \verb'delete' 77 | \end{itemize} 78 | 用户名\verb'public'包括了当前所有用户和未来用户的权限。 79 | 80 | \begin{lstlisting} 81 | grant 82 | on 83 | to 84 | 85 | grant update (budget) on department to Amit, Satoshi; 86 | \end{lstlisting} 87 | 88 | 可以先创建角色(role)然后给用户授予角色。 -------------------------------------------------------------------------------- /DatabaseSystems/6.5_storage.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{存储与文件结构} % Chap 10 4 | \subsection{存储器件} 5 | 物理存储：易失(volatile)、非易失 6 | 7 | 磁盘被划分为道、扇区、柱面 8 | 9 | 常见磁盘性能衡量指标： 10 | \begin{itemize} 11 | \item 访问时间： 12 | \begin{itemize} 13 | \item 寻道(seek)时间：到达特定的道 14 | \item 旋转时间 15 | \end{itemize} 16 | \item 数据传输速率 17 | \item 平均失效时间(MTTF)：通常3-5年 18 | \end{itemize} 19 | 20 | 通过并行来提高性能：数据拆分(striping) 21 | \begin{itemize} 22 | \item 比特级拆分：每个字节按比特分开，存储到多个磁盘上 23 | \item 块级拆分：将块拆分到多张磁盘，即将磁盘阵列看成一张单独的大磁盘，并给块进行逻辑编号 24 | \end{itemize} 25 | 26 | 独立磁盘冗余阵列(Redundant Array of Independent Disk, RAID) 27 | 28 | \begin{minipage}[H]{0.5\linewidth} 29 | \begin{itemize} 30 | \item RAID-0：块级拆分但无冗余 31 | \item RAID-1：块级拆分+冗余，数据库常用 32 | \item RAID-2：纠错码(Error-Correcting-Code, ECC)，可以读出其余位和纠错位重建信息 33 | \item RAID-3：可以检测一整个扇区是否被正确读出 34 | \item RAID-4：在一张独立磁盘上为其他$N$张磁盘对应的块保留一个奇偶校验块 35 | \item RAID-5：将数据和奇偶校验位分布到$N+1$张磁盘中，所有磁盘都能参与读请求 36 | \item RAID-6：P+Q冗余方案，可应对多张磁盘故障情况 37 | \end{itemize} 38 | \end{minipage} 39 | \begin{minipage}[H]{0.5\linewidth} 40 | \begin{figure}[H] 41 | \centering 42 | \includegraphics[width=0.5\linewidth]{fig/RAID.png} 43 | \end{figure} 44 | \end{minipage} 45 | 46 | \subsection{文件结构} 47 | \subsubsection{定长记录} 48 | 每次插入删除都涉及后续元组的移动，可以如下维护一个空闲列表(free list) 49 | \begin{figure}[H] 50 | \centering 51 | \includegraphics[width=0.6\linewidth]{fig/free_lists.png} 52 | \end{figure} 53 | 54 | \subsubsection{变长记录} 55 | 变长记录以以下几种方式出现： 56 | \begin{itemize} 57 | \item 多种记录类型在一个文件中存储 58 | \item 允许一个或多个字段是变长的记录类型 59 | \item 允许可重复字段的记录类型，如数组或多重集合 60 | \end{itemize} 61 | \begin{figure}[H] 62 | \centering 63 | \includegraphics[width=0.5\linewidth]{fig/variable-length.png} 64 | \end{figure} 65 | 66 | 用分槽的页结构(slotted-page)来处理块中变长记录的问题 67 | \begin{figure}[H] 68 | \centering 69 | \includegraphics[width=0.5\linewidth]{fig/slotted_page.png} 70 | \end{figure} 71 | 72 | 块头包含以下信息 73 | \begin{itemize} 74 | \item 块头中记录条目的个数 75 | \item 块中空闲空间的末尾处 76 | \item 由包含记录位置和大小的记录条目组成的数组 77 | \end{itemize} 78 | 注意实际记录是从\textemph{块的尾部}往前插。 79 | 80 | \subsection{文件中记录的组织} 81 | \begin{itemize} 82 | \item 堆文件组织：一条记录可放在文件中任何地方，只要那个地方有空间存放这条记录 83 | \item 顺序文件组织：根据其搜索码值顺序存储 84 | \item 散列文件组织：散列函数 85 | \end{itemize} 86 | 87 | 多表聚簇组织 88 | \begin{figure}[H] 89 | \centering 90 | \includegraphics[width=0.5\linewidth]{fig/multitable_clustering.png} 91 | \end{figure} -------------------------------------------------------------------------------- /DatabaseSystems/8_transaction.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{事务} % Chap 14 4 | \subsection{基本概念} 5 | \begin{definition}[事务(transaction)] 6 | 构成单一逻辑工作单元的操作集合称为事务。 7 | 即使有故障，数据库系统也必须保证数据库的正确执行——要么执行整个事务，要么属于该事务的操作一个也不执行。 8 | \end{definition} 9 | 10 | 事务具有以下的基本性质(ACID)： 11 | \begin{itemize} 12 | \item 原子性(Atomicity)：要么执行完，要么不执行，不能执行到一半 $\to$ 日志、恢复系统 13 | \item 一致性(Consistency)：除了基本的数据完整性约束，还有更多的一致性约束。 14 | \item 隔离性(Isolation)：每个事务都察觉不到系统中有其他事务在并发执行，一定是完成一个再进行下一个 $\to$ 并发控制系统 15 | \item 持久性(Durability)：一个事务成功完成对数据库的改变是永久的，即使出现系统故障 $\to$ 结束前已写入磁盘，更新信息已写入磁盘 16 | \end{itemize} 17 | 18 | 事务的基本状态： 19 | \begin{itemize} 20 | \item 活动的(active)：初始状态 21 | \item 部分提交的(partially commited)：最后一条语句执行后 22 | \item 失败的(failed)：执行出错 23 | \item 中止的(aborted)：事务回滚且数据库已恢复到事务开始执行前 24 | \item 提交的(commited)：成功完成 25 | \end{itemize} 26 | \begin{figure}[H] 27 | \centering 28 | \includegraphics[width=0.3\linewidth]{fig/transaction_state.png} 29 | \end{figure} 30 | 31 | \subsection{可串行化} 32 | \begin{definition}[调度] 33 | 按照时间顺序执行的一串指令即为调度。 34 | 如果事务内的指令都不被打乱，如$$或$$，则为串行调度。 35 | \end{definition} 36 | \begin{definition}[冲突] 37 | 当$I$和$J$是不同事务在相同数据项$Q$上的操作，并且其中\textbf{至少有一个}是\verb'write'指令时，则称$I$与$J$是冲突的。 38 | \end{definition} 39 | \begin{definition}[冲突等价(conflict equivalent)] 40 | 如果调度$S$可以经过一系列\textbf{非冲突}指令交换转换为$S'$，则称$S$和$S'$是冲突等价的。 41 | 若$S'$为串行调度，则$S$为冲突可串行化的(conflict serializable)。 42 | \end{definition} 43 | \begin{figure}[H] 44 | \centering 45 | \includegraphics[width=0.8\linewidth]{fig/conflict_serializability.png} 46 | \end{figure} 47 | 可以将左侧调度通过两个非冲突指令调换转为右侧，故是冲突可串行的。 48 | 49 | 可以通过构造一个优先图(precedence graph)来判断是否冲突可串行化，顶点集由所有参与调度的事务构成，边集由满足下列三个条件之一的边$T_i\to T_j$构成： 50 | \begin{itemize} 51 | \item 在$T_j$执行read(Q)前，$T_i$执行write(Q) 52 | \item 在$T_j$执行write(Q)前，$T_i$执行read(Q) 53 | \item 在$T_j$执行write(Q)前，$T_i$执行write(Q) 54 | \end{itemize} 55 | 即只有\textemph{两者都读才不冲突，先执行到后执行连边}。 56 | 57 | 如果无环（用环检测算法），则冲突可串行化，可以通过\textbf{拓扑排序}（所有父亲执行完才到孩子执行）得到串行化顺序。 58 | 59 | \begin{definition}[可恢复调度] 60 | 若事务$T_j$读事务$T_i$先前写的数据（\textemph{先写后读}），则$T_i$的提交必须在\textemph{$T_j$的提交之前}。 61 | \end{definition} 62 | \begin{definition}[无级联(cascadeless)调度] 63 | 若事务$T_j$读事务$T_i$先前写的数据（\textemph{先写后读}），则$T_i$的提交必须在\textemph{$T_j$这一读操作之前}。 64 | \end{definition} 65 | 66 | \subsection{隔离性级别} 67 | 事务隔离性的好处：提高吞吐量和资源利用率、减少等待时间 68 | \begin{itemize} 69 | \item 可串行化(serializable) 70 | \item 可重复读(repeatable read)：只允许读已提交数据，一个事务两次读一个数据期间，其他事务不得更新该数据 71 | \item 已提交读(read committed)：只允许读已提交数据，但不要求可重复读 72 | \item 未提交读(read uncommitted)：允许读未提交数据，最低一致性要求 73 | \end{itemize} -------------------------------------------------------------------------------- /DatabaseSystems/README.md: -------------------------------------------------------------------------------- 1 | # Database Systems 2 | 3 | 数据库系统原理笔记，所用教材Abraham Silberschatz, Henry F.Korth, S.Sudarsham, *Database System Concepts (6th ed)*, 2012 -------------------------------------------------------------------------------- /DatabaseSystems/fig/E-R-figure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/DatabaseSystems/fig/E-R-figure.png 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\def\ojoin{\setbox0=\hbox{$\bowtie$}% 7 | \rule[-.02ex]{.25em}{.4pt}\llap{\rule[\ht0]{.25em}{.4pt}}} 8 | \def\leftouterjoin{\mathbin{\ojoin\mkern-5.8mu\bowtie}} 9 | \def\rightouterjoin{\mathbin{\bowtie\mkern-5.8mu\ojoin}} 10 | \def\fullouterjoin{\mathbin{\ojoin\mkern-5.8mu\bowtie\mkern-5.8mu\ojoin}} 11 | 12 | \renewcommand{\thefootnote}{\fnsymbol{footnote}} 13 | \lstset{language=sql} 14 | 15 | \title{数据库系统原理笔记} 16 | \author{陈鸿峥} 17 | \date{{\builddatemonth\today}\protect\footnote{\text{Build \builddate\today}}} % protect! 18 | 19 | \begin{document} 20 | 21 | \maketitle 22 | \renewcommand{\thefootnote}{\arabic{footnote}} 23 | \setcounter{footnote}{0} 24 | 25 | \setcounter{tocdepth}{2}%设置深度 26 | \tableofcontents 27 | 28 | \bigskip\bigskip 29 | 30 | \input{1_introduction} % Chap 1, 2 31 | \input{2_sql_intro} % Chap 3 32 | \input{3_high-level_sql} % Chap 4 33 | \input{4_relational_algebra} % Chap 6 34 | \input{5_ER-model} % Chap 7 35 | \input{6_database_design} % Chap 8 36 | \input{6.5_storage} % Chap 10 37 | \input{7_index} % Chap 11 38 | \input{8_transaction} % Chap 14 39 | \input{9_concurrency} % Chap 15 40 | \input{10_recovery} % Chap 16 41 | 42 | \end{document} 43 | 44 | % 1,2,3,4,6,7,8,10,11,14,15.1-15.2,16.1-16.4 -------------------------------------------------------------------------------- /DeepLearning/1_introduction.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | % !TEX encoding = UTF-8 3 | 4 | \section{简介} % ch1 5 | 图~\ref{fig:venn}反映了\newterm{深度学习}与其他几个常见概念之间的关系。 6 | 传统的\newterm{机器学习}（如决策树、SVM、随机森林等）常需要人工提取特征，这一步经常涉及到\newterm{特征工程}(feature engineering)，如果特征没有进行一定处理，直接丢进去让其学习，往往会产生非常糟糕的结果。在一种表示下可能可以对数据进行线性二分，而另一种表示下则没有办法。 7 | 因此，为了避免对特征的强依赖性，一种方法是利用机器学习来学习\textbf{表示(representation)本身}，再将新的表示送入到后面的学习器中让它学习\textbf{表示到输出的映射}，此即\newterm{表示学习}。 8 | 再到后来，深度学习则更加将这种思想发扬光大，表示学习只能学习到\textbf{浅层简单的特征}，那深度学习则尝试去学习\textbf{深层复杂的特征}。 9 | 10 | \bigskip 11 | \begin{tcolorbox} 12 | 事实上现在\newterm{图神经网络}(GNN)也是遵循这样的发展过程，最开始尝试在图上做机器学习\cite{yao:graph_ml_2009,li:distributed_2013,gemulla:mf_2011}；然后又开始在图上以各种随机游走的方式做图表示学习-图嵌入(embedding)\cite{perozzi:deepwalk_2014,grover:node2vec_2016}；后来发现图嵌入能够获得的特征依然太浅层了，因此现在更多则采用图神经网络\cite{kipf:gcn_2017,hamilton:graphsage_2017,li:gated_2016,velickovic:gat_2018}的方式来做图相关的工作。 13 | \end{tcolorbox} 14 | 15 | \begin{figure}[H] 16 | \centering 17 | \begin{tabular}{cc} 18 | \includegraphics[width=0.5\linewidth]{fig/dl_venn.jpg} 19 | \includegraphics[width=0.5\linewidth]{fig/dl_flowchart.jpg} 20 | \end{tabular} 21 | \caption{深度学习Venn图} 22 | \label{fig:venn} 23 | \end{figure} 24 | 25 | 深度学习在发展过程中也起过几个名字：在1940年代到1960年代被称为\newterm{控制论}(cybernetics)，之后\\ 26 | 1980到1990年代则被称为\newterm{连接主义}(connectionism)，而后从2006年到现在才被称为\newterm{深度学习}。2018年的图灵奖正式颁发给深度学习三巨头---Geoffrey Hinton，Yoshua Bengio和Yann LeCun，也奠定了深度学习在学术界的历史地位。 27 | 28 | \bigskip 29 | \begin{tcolorbox} 30 | 我也一直在思考是什么造成了深度学习在2010年代的兴起，使得如今我们快速进入软件2.0时代\cite{olukotun:software2_2018}。 31 | 总结来讲有以下几点： 32 | \begin{itemize} 33 | \item \textbf{数据}：我们每天产生的数据量越来越多，能够处理的数据量越来越大，名副其实地进入了大数据时代。 34 | 其中的一些数据经过处理能够变得很干净，2010年拥有大量标注图片的数据集ImageNet\cite{deng:imagenet_2009}就是这样的例子，它的提出使得有监督学习迎来了一波兴起。 35 | \item \textbf{算法}：我们有更多更优秀的模型，从AlexNet的dropout\cite{krizhevsky:alexnet_2012}，再到后来ResNet的残差模块\cite{he:resnet_2016}。 36 | ImageNet的发展历程，也是深度学习算法/模型改进和发展的历程。 37 | \item \textbf{软件系统}：早年的深度学习框架Caffe\cite{jia:caffe_2014}、Theano\cite{ai-rfou:theano_2016}、MXNet\cite{chen:mxnet_2016}等使得研究人员可以方便地编写神经网络模型，而2015年前后诞生的TensorFlow\cite{abadi:tensorflow_2016}和PyTorch\cite{paszke:pytorch_2019}则是成为了现在深度学习框架的主流范式，程序员不需知道底层的实施细节，只用“调库”和“调参”也可以实现很高效的模型，这些软件系统的诞生极大程度推动了深度学习的遍地开花。 38 | 再到现在XLA\cite{google:xla}和TVM\cite{chen:tvm_2018}等深度学习编译器的出现，更是进一步解放程序员，使上层模型可以方便快捷地部署到后端不同硬件平台上。 39 | \item \textbf{硬件}：GPU为深度学习做出了不可磨灭的贡献，没有GPGPU的发展，很多深度学习任务根本没有办法完成。 40 | 现在的TPU\cite{jouppi:tpu_2017}及各种神经网络加速器\cite{moreau:vta_2018}也都是在拓宽这一层面，以进一步提升深度学习的能力。 41 | \end{itemize} 42 | 上面提到的这几点都不可或缺，它们共同造就了整个深度学习栈，从而带来现在深度学习的繁荣。 43 | \end{tcolorbox} -------------------------------------------------------------------------------- /DeepLearning/README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning 2 | 3 | 深度学习笔记，所用教材Ian Goodfellow, Yoshua Bengio, Aaron Courville, *Deep Learning*, MIT Press, 2016, [Online](http://www.deeplearningbook.org/) -------------------------------------------------------------------------------- /DeepLearning/fig/deconvolution.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/DeepLearning/fig/deconvolution.jpg -------------------------------------------------------------------------------- /DeepLearning/fig/deeplabv1.jpg: -------------------------------------------------------------------------------- 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\date{{\builddatemonth\today}\protect\footnote{\text{Build \builddate\today}}} % protect! 20 | 21 | \setcounter{secnumdepth}{5} 22 | 23 | \begin{document} 24 | 25 | \maketitle 26 | \renewcommand{\thefootnote}{\arabic{footnote}} 27 | \setcounter{footnote}{0} 28 | 29 | \setcounter{tocdepth}{2}%设置深度 30 | \tableofcontents 31 | 32 | \bigskip\bigskip 33 | 34 | 本课程主要选用Ian Goodfellow, Yoshua Bengio, Aaron Courville的《深度学习》(Deep Learning)一书。 35 | 一些众所周知的概念会在笔记中略过，而着重在一些容易忽略的知识点上面。 36 | 37 | \input{1_introduction} 38 | \input{2_ann} 39 | \input{3_optimization} 40 | \input{4_issues} 41 | \input{5_segmentation} 42 | 43 | \newpage 44 | \bibliographystyle{unsrt} 45 | \bibliography{dl} 46 | 47 | \end{document} -------------------------------------------------------------------------------- /DigitalCircuits/2_integrated_circuit.tex: -------------------------------------------------------------------------------- 1 | \section{集成电路} 2 | \begin{figure}[htbp] 3 | \centering 4 | \includegraphics[width=0.8\linewidth]{fig/cmos.PNG} 5 | \caption{CMOS电路:\large\textbf{$n$内高，$p$外低}} 6 | \end{figure} 7 | \begin{figure}[htbp] 8 | \centering 9 | \includegraphics[width=0.6\linewidth]{fig/cmos_ex.PNG} 10 | \caption{CMOS或非门} 11 | \end{figure} 12 | \begin{figure}[htbp] 13 | \centering 14 | \includegraphics[width=0.6\linewidth]{fig/ttl.PNG} 15 | \caption{TTL电路} 16 | \end{figure} 17 | \begin{figure}[htbp] 18 | \label{ttl_inverse} 19 | \centering 20 | \includegraphics[width=0.6\linewidth]{fig/ttl_ex.PNG} 21 | \caption{TTL反相器} 22 | \end{figure} 23 | \par 由图\ref{ttl_inverse}，$Q_1$被$V_{CC}$上拉，始终导通. 若输入为高电平，$Q_2$导通，$Q_3$导通，输出被下拉为低电平. 同时，$Q_2$在集电极处足够低的电压可以使$Q_4$截至. 24 | \begin{figure}[htbp] 25 | \centering 26 | \includegraphics[width=0.6\linewidth]{fig/three_gates.PNG} 27 | \caption{三态门} 28 | \end{figure} -------------------------------------------------------------------------------- /DigitalCircuits/5_circuit_design.tex: -------------------------------------------------------------------------------- 1 | \section{电路设计} 2 | \subsection{电路分类} 3 | \begin{enumerate} 4 | \item 摩尔(Moore)电路 5 | \begin{figure}[htbp] 6 | \centering 7 | \includegraphics[width=0.4\linewidth]{fig/moore_machine.pdf} 8 | \caption{触发器} 9 | \end{figure} 10 | \item 米勒(Mealy)电路 11 | \begin{figure}[htbp] 12 | \centering 13 | \includegraphics[width=0.4\linewidth]{fig/mealy_machine.pdf} 14 | \caption{触发器} 15 | \end{figure} 16 | \end{enumerate} 17 | \subsection{设计步骤} 18 | \par 基于状态转移表格的方法 19 | \begin{enumerate} 20 | \item 状态图 21 | \item 次态表 22 | \item 触发器转移表 23 | \begin{center} 24 | \begin{tabular}{|c|c|c|c|} 25 | \hline 26 | $Q^n$ & $Q^{n+1}$ & J & K\\\hline 27 | 0 & 0 & 0 & X\\\hline 28 | 0 & 1 & 1 & X\\\hline 29 | 1 & 0 & X & 1\\\hline 30 | 1 & 1 & X & 0\\\hline 31 | \end{tabular} 32 | \end{center} 33 | \item 触发器JK卡诺图 34 | \item JK驱动方程 35 | \item 时序电路 36 | \end{enumerate} 37 | \par 基于状态方程的方法 38 | \begin{enumerate} 39 | \item 状态表 40 | \item 次态卡诺图 41 | \item 状态方程 $Q^{n+1}=J\ol{Q^n}+\ol{K}Q^n$ 42 | \item 驱动方程 43 | \item 时序电路 44 | \end{enumerate} 45 | \subsection{实例操作} 46 | \par 目的：用JK触发器实现一个12进制同步计数器 47 | \begin{enumerate} 48 | \item 状态转换图 49 | \begin{figure}[H] 50 | \centering 51 | \includegraphics[width=0.7\linewidth]{fig/12system_state.pdf} 52 | \end{figure} 53 | \item 确定电路所需触发器数目\\ 54 | 由于$2^4=16>12$，故需要$4$个JK触发器 55 | \item 次态卡诺图 56 | \begin{figure}[H] 57 | \centering 58 | \includegraphics[width=0.4\linewidth]{fig/Karnaugh_12system.pdf} 59 | \end{figure} 60 | \item 触发器状态方程，由卡诺图可得 61 | \[\begin{aligned} 62 | Q_0^{n+1}&=\overline{Q_0}\\ 63 | Q_1^{n+1}&=Q_0\overline{Q_1}+\overline{Q_0}Q_1\\ 64 | Q_2^{n+1}&=Q_0Q_1\ol{Q_2}+\ol{Q_1}Q_2\ol{Q_3}+\ol{Q_0}Q_2\ol{Q_3}\\ 65 | Q_3^{n+1}&=\ol{Q_2}Q_3+Q_0Q_1Q_2\ol{Q_3} 66 | \end{aligned}\] 67 | \item 触发器驱动方程，由 68 | \[Q^{n+1}=J\ol{Q^n}+\ol{K}Q^n\] 69 | 将状态方程整理为上式形式，可得 70 | \[\begin{aligned} 71 | J_0&=1 \qquad &K_0&=1\\ 72 | J_1&=Q_0 \qquad &K_0&=Q_0\\ 73 | J_2&=Q_1Q_0 \qquad &K_2&=\ol{\ol{Q_3}\ol{Q_1}+\ol{Q_3}\ol{Q_0}}=\ol{Q_3}+Q_1Q_0\\ 74 | J_3&=Q_2Q_1Q_0 \qquad &K_3&=Q_2 75 | \end{aligned}\] 76 | \item 检查自启动\\ 77 | 当输入为1111和0000时，可自动跳转至0001；输入为1101时，跳转至0010；输入为1110时，跳转至0011 78 | \end{enumerate} 79 | \par Proteus电路图连接如下 80 | \begin{figure}[H] 81 | \centering 82 | \includegraphics[width=0.9\linewidth]{fig/12system_protues.PNG} 83 | \end{figure} 84 | \par 仿真结果如下 85 | \begin{figure}[H] 86 | \centering 87 | \includegraphics[width=0.6\linewidth]{fig/12system_wave.PNG} 88 | \end{figure} -------------------------------------------------------------------------------- /DigitalCircuits/README.md: -------------------------------------------------------------------------------- 1 | # Digital Circuits 2 | 3 | 数字电路笔记，所用教材：Thomas L.Floyd, ***Digital Fundamentals (10th ed)***, Pearson Edu. Inc., 2009 4 | 5 | 1. 概述 6 | 2. 基本概念 7 | 3. 集成电路 8 | 4. 组合电路 9 | 5. 时序电路 10 | 6. 电路设计 -------------------------------------------------------------------------------- /DigitalCircuits/fig/12system_protues.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/DigitalCircuits/fig/12system_protues.PNG -------------------------------------------------------------------------------- /DigitalCircuits/fig/12system_state.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/DigitalCircuits/fig/12system_state.pdf -------------------------------------------------------------------------------- /DigitalCircuits/fig/12system_wave.PNG: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /DigitalImageProcessing/README.md: -------------------------------------------------------------------------------- 1 | # Digital Image Processing 2 | 3 | 数字图像处理笔记，所用教材Rafel C. Gonzalez, Richard E. 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19 | \tableofcontents 20 | 21 | \bigskip\bigskip 22 | 23 | \input{1_introduction} 24 | \input{2_image_enhancement} 25 | \input{3_frequency_domain} 26 | \input{4_restoration} 27 | \input{5_colorful_images} 28 | \input{6_morphological_images} 29 | \input{7_image_segmentation} 30 | \input{8_representation} 31 | 32 | \end{document} -------------------------------------------------------------------------------- /MachineLearning/11_feature_selection.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{特征选择} 4 | 特征选择是从原始特征中挑选出最有代表性、性能最好的特征，而特征提取则是用映射（或变换）的方法将原始特征变换维较少的新特征；降维则是通过原始特征构造新的特征，新的特征能够更好表示原始数据，且特征数量较原始特征少。 5 | 6 | \subsection{子集搜索与子集评价} 7 | 特征选择的两个关键环节：子集搜索和子集评价 8 | 9 | 产生初始候选子集，评价候选子集的好坏，基于评价结果产生下一个候选子集。 10 | \begin{itemize} 11 | \item 前向搜索：逐渐增加相关特征 12 | \item 后向搜索：从完整特征集合开始，逐渐减少特征 13 | \item 双向搜索：每一轮逐渐增加相关特征，同时减少无关特征 14 | \end{itemize} 15 | 16 | \subsection{特征选择方法} 17 | \begin{itemize} 18 | \item 过滤型(filter)：先进行特征选择，再进行建模； 19 | 评价指标： 20 | \begin{itemize} 21 | \item 皮尔逊相关系数：系数取值区间为$[-1,1]$，$-1$完全负相关，$+1$完全正相关，$0$则没有相关 22 | \[r=\frac{\mathop{cov}(X,Y)}{\mathop{var}(X)\mathop{var}(Y)}\] 23 | 计算简单，但是不能反映变量之间的非线性关系 24 | \item 信息增益：特征子集$A$确定了对数据集$D$的一个划分，$A$上的取值将数据集$D$分为$V$份，每一份用$D^v$表示，$\mathop{Ent}(D^v)$表示$D^v$上的信息熵 25 | \[\mathop{Gain}(D,A)=\mathop{Ent}(D)-\sum_{v=1}^V\frac{|D^v|}{|D|}\mathop{Ent}(D^v)\] 26 | \end{itemize} 27 | \item 封装型(wrapper)：直接将学习器的性能作为特征子集的评价标准，如召回率、准确率、AUC等指标。LVW (Las Vegas Wrapper)采用随机策略进行子集搜索，并以最终分类器的误差作为特征子集评价标准 28 | \begin{enumerate} 29 | \item 在循环的每一轮随机产生一个特征子集 30 | \item 在随机产生的特征子集上通过交叉验证推断当前特征子集的误差 31 | \item 多次循环，在多个随机产生的特征子集中选择误差最小的特征子集作为最终解 32 | \end{enumerate} 33 | \item 嵌入型(embedded)：将特征选择和模型训练融为一体 34 | \begin{itemize} 35 | \item 正则化方法：L1范数LASSO回归$\lambda\norm{\vw}_1$，L2范数岭回归$\lambda\norm{\vw}_2$ 36 | \item 随机森林/决策树：平均不纯度减少，平均精确率减少 37 | \item 递归特征消除(Recursive Feature Eliminiation, RFE)：反复构建模型选出最好/最坏的特征，将选出来的特征放到一边，然后在剩余特征上重复这个过程，遍历所有特征。 38 | 在这个过程中特征被消除的次序就是特征的排序。 39 | 消除的稳定性很大程度取决于模型迭代时选用的底层模型。 40 | \end{itemize} 41 | \item 无监督特征选择：Laplacian score选择那些能够最好保持数据流形结构的特征子集 42 | \end{itemize} 43 | 44 | 数据和特征决定了机器学习的上限，而模型和算法意在逼近这个上限，特征工程就是最大限度地从原始数据中提取特征以供算法和模型使用。 -------------------------------------------------------------------------------- /MachineLearning/1_introduction.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{简介} 4 | 机器学习理论主要是设计和分析一些让计算机可以自动“学习”的算法，数据挖掘是用人工智能、机器学习、统计学和数据库的交叉方法在相对较大型的数据集中发现模式的计算过程。 5 | 大体上看，数据挖掘可以视为机器学习和数据库的交叉，它主要利用机器学习界提供的技术来\textbf{分析}海量数据，利用数据库界提供的技术来\textbf{管理}海量数据。 6 | 7 | \begin{quote} 8 | "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." 9 | \hfill---Tom Mitchell 10 | \end{quote} 11 | 12 | \subsection{分类} 13 | 机器学习(machine learning)通常可以分为以下几类： 14 | \begin{itemize} 15 | \item 监督学习(supervised learning)：有标签(label) 16 | \begin{itemize} 17 | \item 回归(regression)：连续值 18 | \item 分类(classification)：离散值 19 | \end{itemize} 20 | \item 无监督学习(unsupervised learning)：无标签 21 | \begin{itemize} 22 | \item 降维 23 | \item 聚类(clustering) 24 | \end{itemize} 25 | \item 强化学习(reinforcement learning)：延后的标签 26 | \end{itemize} 27 | 28 | \subsection{历史} 29 | \begin{itemize} 30 | \item 推理期(1950-1970)：逻辑理论家(Logic Theorist)A.~Newell \& H.~Simon(1975图灵奖) 31 | \item 知识期(1970s)：知识工程之父E.~A.~Feigenbaum(1994图灵奖) 32 | \item 学习期(1980s)：决策树、归纳逻辑程序设计(Prolog) 33 | \end{itemize} 34 | 35 | 机器学习的五大学派(tribe)： 36 | \begin{itemize} 37 | \item 符号主义(symbolist) 38 | \item 联结主义(connectionist) 39 | \item 进化主义(evolutionaries) 40 | \item 贝叶斯主义(bayesians) 41 | \item 类比主义(analogizers) 42 | \end{itemize} 43 | \begin{figure}[H] 44 | \centering 45 | \includegraphics[width=0.8\linewidth]{fig/A-Look-at-Machine-Learning-Evolution.png} 46 | \end{figure} -------------------------------------------------------------------------------- /MachineLearning/2_basic_concepts.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{基础概念} 4 | \subsection{训练集与测试集} 5 | \begin{itemize} 6 | \item 留出法(hold-out)：直接将数据集划分为两个互斥的集合，一个作为训练集，另一个作为测试集 7 | \begin{itemize} 8 | \item 注意数据分布的一致性，通过多次随机划分取平均保证 9 | \item 通常用$2/3\thicksim 4/5$的样本用于训练，其余用作测试 10 | \end{itemize} 11 | \item 交叉验证法(cross validation)/$k$折(fold)交叉验证：划分为$k$个互斥子集，其中$k-1$个用于训练，最后一个用于验证，训练$k$次，对这$k$次结果取平均 12 | \begin{itemize} 13 | \item 通常采用10次10折交叉验证，每次都换划分方式，确保随机性 14 | \end{itemize} 15 | \item 自助法(bootstrapping)：从原始数据集中放回采样得到新数据集作为测试集 16 | \begin{itemize} 17 | \item 在数据集较小的、难以有效划分训练/测试集时比较有用 18 | \item 但改变了初始数据集分布，会引入估计偏差 19 | \end{itemize} 20 | \end{itemize} 21 | 22 | 注意：通常将习得模型实际使用中遇到的数据称为测试集，而将训练的数据划分为训练集与验证集(validation) 23 | 24 | \subsection{参数选择} 25 | 参数通常包括 26 | \begin{itemize} 27 | \item 模型本身的参数(parameter)：通过学习改变 28 | \item 超参数(superparameter)：预先设定，调参实际上就是在选择算法 29 | \end{itemize} 30 | 31 | \subsection{性能度量} 32 | \begin{itemize} 33 | \item 回归：通常采用均方误差(MSE) 34 | \item 分类：错误率、精度 35 | \end{itemize} 36 | 37 | 对于二分类问题，有混淆矩阵(confusion matrix) 38 | \begin{center} 39 | \begin{tabular}{|c|c|c|}\hline 40 | & 预测正 & 预测反\\\hline 41 | 真实正 & TP(真正例) & FN(假反例)\\\hline 42 | 真实反 & FP(假正例) & TN(真反例)\\\hline 43 | \end{tabular} 44 | \end{center} 45 | \[\begin{aligned} 46 | \text{查准率}P&=\frac{TP}{TP+FP}\\ 47 | \text{查全率}R&=\frac{TP}{TP+FN} 48 | \end{aligned}\] 49 | 50 | 为了衡量机器学习算法的泛化性能，需要知道以下指标： 51 | \begin{itemize} 52 | \item 方差(variance)：度量同样大小的训练集的变动所导致的学习性能的变化，即刻画数据扰动所造成的影响 53 | \[\Var{\vx}=\mathbb{E}_D\lrs{\lrp{f(\vx;D)-\bar{f}(\vx)}^2}\] 54 | \item 偏差(bias)：度量学习算法的期望预测和真实结果的偏离程度，即刻画学习算法本身的拟合能力 55 | \[b^2(\vx)=\lrp{\bar{f}(\vx)-y}^2\] 56 | \item 噪声(noise)：表达在当前任务上任何学习算法所能达到的期望泛化误差的下界 57 | \[\eps^2=\mathbb{E}_D\lrs{\lrp{y_D-y}^2}\] 58 | \end{itemize} 59 | 60 | 泛化误差可以分解为 61 | \[E(f;D)=b^2(\vx)+\Var{\vx}+\eps^2\] 62 | 即泛化性能是由学习算法的能力、数据的充分性及学习任务本身的难度决定的 63 | \begin{figure}[H] 64 | \centering 65 | \includegraphics[width=0.4\linewidth]{fig/bias-var.png} 66 | \end{figure} -------------------------------------------------------------------------------- /MachineLearning/7_bayesian.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{贝叶斯优化} 4 | \subsection{高斯混合模型} 5 | 最常用的混合模型即高斯混合模型(Gaussian Mixture Model, GMM), 6 | \[p(\vx)=\sum_{k=1}^K\pi_k\mathcal{N}(\vx\mid\mu_k,\Sigma_k)\] 7 | 代表有$K$个高斯分布进行混合，其中$\pi_k$为混合系数，满足 8 | \[\sum_{k}=1^K\pi_k=1,\pi_k\geq 0,\forall k\] 9 | 10 | 求最大似然 11 | \[\ln p(X\mid\pi,\mu,\Sigma)=\sum_{n=1}^N\ln\lrp{\sum_{k=1}^K\pi_k\mathcal{N}\lrp{\vx^{(n)}\mid\mu_k,\Sigma_k}}\] 12 | 优化变量为$\Theta=\{\pi_k,\mu_k,\Sigma_k\}$。 13 | 14 | \subsection{EM算法} 15 | 未观测变量即隐变量(latent variable)，令$X$表示已观测变量集，$Z$表示隐变量集，$\Theta$表示模型参数。 16 | 若对$\Theta$做极大似然估计，则应最大化对数似然 17 | \[LL(\Theta\mid X,Z)=\ln P(X,Z\mid\Theta)\] 18 | 但由于$Z$是隐变量，上式无法直接求解。 19 | 这时可以通过对$Z$计算期望，来最大化已观测数据的对数边际似然(marginal likelihood) 20 | \[LL(\Theta\mid X)=\ln P(X\mid\Theta)=\ln\sum_Z P(X,Z\mid\Theta)\] 21 | 22 | 期望最大化(Expectation-Maximization, EM)算法是常用估计参数隐变量的方法（非梯度优化）。 23 | \begin{itemize} 24 | \item 期望(E)步：利用当前估计的参数值来计算对数似然的期望值 25 | \item 最大化(M)步：寻找能使E步产生的似然期望最大化的参数值 26 | \end{itemize} 27 | 新得到的参数值重新被用于E步，直至收敛至局部最优解。 28 | 29 | 在GMM上用EM算法与K-means非常类似，只不过EM算法是软指派(soft assignment)，而且每一个中心根据软指派数据的加权平均进行移动。b -------------------------------------------------------------------------------- /MachineLearning/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning -------------------------------------------------------------------------------- /MachineLearning/fig/A-Look-at-Machine-Learning-Evolution.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | \documentclass{note} 2 | \usepackage[table,optidef]{mypackage} 3 | 4 | \renewcommand{\thefootnote}{\fnsymbol{footnote}} 5 | 6 | \title{机器学习笔记} 7 | \author{陈鸿峥} 8 | \date{{\builddatemonth\today} \protect\footnote{\text{Build \builddate\today}}}%加了build 9 | 10 | \begin{document} 11 | 12 | \maketitle 13 | \renewcommand{\thefootnote}{\arabic{footnote}} 14 | \setcounter{footnote}{0} 15 | 16 | \setcounter{tocdepth}{2}%设置深度 17 | \tableofcontents 18 | 19 | \bigskip\bigskip 20 | 本笔记对应周志华的《机器学习》（西瓜书）。 21 | 22 | \input{1_introduction} 23 | \input{2_basic_concepts} 24 | \input{3_linear_model} 25 | \input{4_decision_tree} 26 | \input{5_neural_network} 27 | \input{6_svm} 28 | \input{7_bayesian} 29 | \input{8_ensemble_learning} 30 | \input{9_clustering} 31 | \input{10_dimension_reduction} 32 | \input{11_feature_selection} 33 | \input{12_semi-supervised} 34 | \input{13_recommendation_system} 35 | \input{reference} 36 | 37 | \end{document} -------------------------------------------------------------------------------- /MachineLearning/reference.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{参考资料} 4 | \begin{enumerate} 5 | \item 周志华，《机器学习》，2016 6 | \item Jure Leskovec, Stanford CS246: Mining Massive Data Sets, \url{http://web.stanford.edu/class/cs246/} 7 | \end{enumerate} -------------------------------------------------------------------------------- /MathematicalLogic/README.md: -------------------------------------------------------------------------------- 1 | # Mathematical Logic 2 | 3 | 数理逻辑笔记 -------------------------------------------------------------------------------- /MathematicalLogic/fig/bdd_limitation.jpg: -------------------------------------------------------------------------------- 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\renewcommand{\thefootnote}{\arabic{footnote}} 22 | \setcounter{footnote}{0} 23 | 24 | \setcounter{tocdepth}{2}%设置深度 25 | \tableofcontents 26 | 27 | \bigskip\bigskip 28 | 29 | \input{1_propositional_logic} 30 | \input{2_predicate_logic} 31 | \input{3_verification} 32 | \input{4_program_verification} 33 | \input{5_modal_logic} 34 | \input{6_bdd} 35 | 36 | \end{document} -------------------------------------------------------------------------------- /NaturalLanguageProcessing/3_model.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{语言模型} 4 | 语言模型主要包括统计语言模型和神经语言模型。 5 | 6 | \subsection{统计语言模型} 7 | 考虑语句的先验概率 8 | \[p(s)=\prod_{i=1}^{m}p(w_i\mid w_1\cdots w_{i-1}),\;p(w_1\mid w_0)=p(w_1)\] 9 | 其中$w_i$可以是字、词、短语等，称为\textbf{统计基元}，通常用\underline{词}代之。 10 | 11 | 为减少历史基元的个数，将$w_1w_2\cdots w_{i-1}$映射到等价类$S(w_1w_2\cdots w_{i-1})$，使等价类的数目远小于原来不同历史基元的数目，则有 12 | \[p(w_i\mid w_1\cdots w_{i-1})=p(w_i\mid S(w_1\cdots w_{i-1}))\] 13 | 14 | n元文法(n-gram)模型 15 | \begin{itemize} 16 | \item 当$n=1$时，出现在第$i$位上的基元$w_i$独立于历史，1元文法也被uni-gram或monogram 17 | \item $n=2$时，2-gram(bi-gram)称为1阶马尔可夫链 18 | \item $n=3$时，3-gram(tri-gram)称为2阶马尔可夫链，以此类推 19 | \end{itemize} 20 | 实际操作加上句首\verb''和句尾标记\verb''。 21 | \begin{figure}[H] 22 | \centering 23 | \includegraphics[width=0.6\linewidth]{fig/n-gram.png} 24 | \end{figure} 25 | 26 | 应用： 27 | \begin{itemize} 28 | \item 音字转换问题：给定拼音转为汉字串 29 | \item 汉语分词问题 30 | \end{itemize} 31 | 32 | 对于n-gram，由最大似然估计求得 33 | \[p(w_i\mid w_{i-n+1}^{i-1})=f(w_i\mid w_{i-n+1}^{i-1})=\frac{c(w_{i-1+1})}{\sum_{w_i}c(w_{i-n+1}^i)}\] 34 | 其中$\sum_{w_i}c(w_{i-n+1}^i)$是历史串$w_{i-n+1}^{i-1}$在给定语料中出现的次数，即$c(w_{i-n+1}^{i-1})$。 35 | 36 | \begin{definition}[困惑度(perplexity)] 37 | 假定测试语料$T$由$L$个句子$(t_1,\ldots,t_L)$构成，则整个测试集的概率为 38 | \[p(T)=\prod_{i=1}^Lp(t_i)\] 39 | \end{definition} 40 | 41 | 为避免数据匮乏/稀疏导致的零概率问题，需要做数据平滑：调整最大似然估计的概率值，使零概率增值，使非零概率下调，消除零概率，改进模型的整体正确率。 42 | 基本目标是测试样本语言模型的\textbf{困惑度越小越好}，基本约束是 43 | \[\sum_{w_i}p(w_i\mid w_1,w_2,\ldots,w_{i-1})=1\] 44 | \begin{itemize} 45 | \item 加一法： 46 | \[p(w_i\mid w_{i-1})=\frac{1+c(w_{i-1}w_i)}{|V|+\sum_{w_i}c(w_{i-1}w_i)}\] 47 | \item 减值法/折扣法：将剩余概率量分配给未见概率 48 | \begin{itemize} 49 | \item Good-Turing估计 50 | \item 绝对减值法：从每个计数$r$中减去相同的量，剩余概率量由未见事件均分 51 | \[p_r=\begin{cases} 52 | \frac{r-b}{N} & r>0\\ 53 | \frac{b(R-n_0)}{Nn_0} & r=0 54 | \end{cases}\] 55 | 其中$n_0$为样本中未出现的事件数目，$b\leq 1$为减去的常量，$b(R-n_0)/N$是由于减值而产生的剩余概率量 56 | \end{itemize} 57 | \end{itemize} 58 | 59 | \subsection{神经语言模型} 60 | 对于固定窗口大小的神经网络显然是不适用的，需要采用一种能够处理任意长度输入的架构，即循环神经网络(RNN)，不同神经元共享相同的参数。 61 | \begin{figure}[H] 62 | \centering 63 | \includegraphics[width=0.6\linewidth]{fig/RNN.png} 64 | \end{figure} 65 | 66 | RNN的优点： 67 | \begin{itemize} 68 | \item 能够处理任意长度输入 69 | \item $t$时刻可以访问之前任意时刻的信息 70 | \item 对于较长输入，模型大小不变 71 | \item 权重共享 72 | \end{itemize} 73 | 74 | 不足：循环计算过程慢，难以访问到距当前时刻较远的信息。 -------------------------------------------------------------------------------- /NaturalLanguageProcessing/5_question_and_answering.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{问答与对话} 4 | \begin{definition}[问答系统] 5 | 输入：自然语言的问句，而非关键词的组合\\ 6 | 输出：直接答案，而非文档集合 7 | \end{definition} 8 | 9 | 优点: 10 | \begin{itemize} 11 | \item 相对于基于知识推理的问答系统而言：不受知识库规模限制，不受领域限制，更加接近真实应用需求 12 | \item 相对于搜索引擎而言：问答式检索系统接受的是自然语言形式的提问，由于自然语言处理技术的应用，对用户意图的把握更加准确，呈现给用户的答案更加准确 13 | \end{itemize} 14 | \par 缺点:目前问答式检索系统仅能处理有限的简单问题，如Factoid问题等 15 | 16 | 问答式检索方法： 17 | \begin{itemize} 18 | \item \textbf{信息检索+信息抽取} 19 | \item \textbf{信息检索+模式匹配}：离线阶段获取答案模式，在线阶段首先判断当前提问属于哪一类，然后使用这类提问的所有模式来抽取候选答案 20 | \item \textbf{信息检索+自然语言处理技术} 21 | \item \textbf{基于统计翻译模型的问答技术}：把提问句看作答案句在同一语言内的一种翻译 22 | \end{itemize} 23 | 24 | 新方法基于深度学习：把回答问题的过程看作一个黑盒，通过复杂神经网络和超大规模数据集训练出一个拟合能力强大的模型。 25 | 26 | 对话管理（状态跟踪和学习）方法： 27 | \begin{itemize} 28 | \item \textbf{有限状态机}(finite state machine)：太受限了 29 | \item 基于\textbf{框架}的方法(frame-based)： 30 | \begin{itemize} 31 | \item 使用框架的结构指导对话过程：机器根据框架进行提问，人也根据框架进行回复 32 | \item 问答过程就是一个槽-值填充过程：所有槽都填满了，就可以通过信息系统查询 33 | \item 用户可以一次性回答多个系统问题 34 | \end{itemize} 35 | \item \textbf{统计方法(MDP)}：状态、动作、目标 36 | \end{itemize} -------------------------------------------------------------------------------- /NaturalLanguageProcessing/6_machine_translation.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{机器翻译} 4 | 基本翻译方法 5 | \begin{itemize} 6 | \item 直接转换 7 | \item 基于规则的翻译方法 8 | \item 基于中间语言 9 | \item 基于语料库：基于事例、统计翻译、神经网络翻译 10 | \end{itemize} 11 | 12 | \subsection{基于规则的翻译方法} 13 | \begin{enumerate} 14 | \item 对源语言句子进行词法分析 15 | \item 句法/语义分析 16 | \item 源语言句子结构到译文结构的转换 17 | \item 译文句法结构生成 18 | \item 源语言词汇到译文词汇转换 19 | \item 译文词法选择与生成 20 | \end{enumerate} 21 | \begin{figure}[H] 22 | \centering 23 | \includegraphics[width=0.6\linewidth]{fig/rule-based_mt.png} 24 | \end{figure} 25 | 26 | 优点：保持原文结构、规范语句 27 | \par 弱点：规则由人工编写，工作量大，主观性强，不利于系统扩充，灵活性低（对非规范语言现象无法处理） 28 | 29 | \subsection{基于语料库的翻译方法} 30 | \begin{figure}[H] 31 | \centering 32 | \includegraphics[width=0.6\linewidth]{fig/example-based_mt.png} 33 | \end{figure} 34 | 35 | 优点：不需源语言句子符合语法规定，也不许对源语言句子做深入分析 36 | \par 缺点：两个不同句子之间相似性难以把握，难以处理陌生语言现象，且事例库庞大起来难以搜索 37 | 38 | \subsection{统计翻译} 39 | \subsubsection{基本方法} 40 | \begin{itemize} 41 | \item 源语言：$S=s_1^m=s_1s_2\cdots s_m$ 42 | \item 目标语言：$T=t_1^l=t_1t_2\cdots t_n$ 43 | \end{itemize} 44 | 通过最大后验求解 45 | \[\argmax_TP(T\mid S)=\argmax_T P(S\mid T)P(T)\] 46 | 其中$P(S\mid T)$为翻译模型(TM)，确定了单词和词语如何被翻译(fidelity)，从平行语料中学习； 47 | $P(T)$为语言模型(LM)，确定怎么写出好的目标语言的句子(fluency)，从单语语料中学习。 48 | 49 | 三个关键问题： 50 | \begin{itemize} 51 | \item 估计语言模型概率$P(T)$ 52 | \item 估计翻译模型概率$P(S\mid T)$：关键问题是怎样定义目标语言句子中的词与源语言句子中的词之间的对应关系，基本原理是对位(alignment)模型 53 | \item 快速搜索最大值解 54 | \end{itemize} 55 | 56 | \subsubsection{基于短语的翻译模型} 57 | 基于词的翻译模型很难消除歧义，很难处理一对多、多对一问题。 58 | 以短语为基本翻译单元，遵循短语划分、短语翻译、短语调序的步骤。 59 | \begin{definition}[对齐一致性] 60 | $S^j$中每个词$S_k$，若$(k,k')\in A$，则$i'\leq k'\leq j'$，$T_i^j$中每个词$T_{t'}$，若$(t,t')\in A$，则$i\leq t\leq j$。 61 | \end{definition} 62 | 63 | 关键问题是学习短语翻译规则（双语句对词语对齐、短语翻译规则抽取）、估计短语翻译概率。 64 | 65 | \subsection{神经机器翻译} 66 | 统计机器翻译都是人工设定的模块和特征，可解释性高、模块定制化、错误追踪，但是数据稀疏、不擅长复杂结构、依赖先验知识。 67 | 68 | 分布式的语义表示是统计机器翻译到机器翻译的核心 69 | \begin{figure}[H] 70 | \centering 71 | \includegraphics[width=0.6\linewidth]{fig/attention.png} 72 | \end{figure} 73 | 74 | BLEU(BiLingual Evaluation Understudy)评价方法：统计同时出现在系统译文和参考译文中的$n$元词个数，最后把匹配到$n$元词的数目除以系统译文的$n$元词数目，得到评测结果。 -------------------------------------------------------------------------------- /NaturalLanguageProcessing/README.md: -------------------------------------------------------------------------------- 1 | # Natural Language Processing (NLP) 2 | 3 | 自然语言处理笔记 -------------------------------------------------------------------------------- /NaturalLanguageProcessing/fig/CFG.png: 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\makesavenoteenv{tabular} 5 | \usepackage{csquotes} 6 | \renewcommand{\mkbegdispquote}[2]{\kaiti} 7 | 8 | \renewcommand{\thefootnote}{\fnsymbol{footnote}} 9 | 10 | \title{统计自然语言处理笔记} 11 | \author{陈鸿峥} 12 | \date{{\builddatemonth\today}\protect\footnote{\text{Build \builddate\today}}} % protect! 13 | 14 | \begin{document} 15 | 16 | \maketitle 17 | \renewcommand{\thefootnote}{\arabic{footnote}} 18 | \setcounter{footnote}{0} 19 | 20 | \setcounter{tocdepth}{2}%设置深度 21 | \tableofcontents 22 | 23 | \bigskip\bigskip 24 | 25 | \input{1_introduction} 26 | \input{2_cut} 27 | \input{3_model} 28 | \input{4_syntax} 29 | \input{5_question_and_answering} 30 | \input{6_machine_translation} 31 | 32 | \end{document} -------------------------------------------------------------------------------- /OperatingSystems/3_threads.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{线程} 4 | 在没有线程概念的系统中，进程是\textbf{资源分配}、调度/执行的单位；而在有线程概念的系统中，线程就成了\textbf{基本调度单位}/程序执行流最小单元，由线程ID、程序计数器、寄存器集合和堆栈组成。 5 | 6 | 线程独立拥有寄存器和栈等现场状态，只会与进程共享地址空间、代码和IO文件资源，\textbf{不与进程共享上下文寄存器}。 7 | 8 | 线程的优点： 9 | \begin{itemize} 10 | \item 创建速度快 11 | \item 终止所用时间少 12 | \item 切换时间少 13 | \item 通信效率高，同一进程无需调用内核，共享存储空间 14 | \end{itemize} 15 | 16 | \bigskip 17 | 用户级线程(ULT)：线程管理都由应用程序完成（线程库），内核不知道线程的存在，优点： 18 | \begin{itemize} 19 | \item 线程切换不需要模式切换 20 | \item 调度算法可以应用程序专用 21 | \item ULT不需要内核支持，线程库可以在任何OS上运行 22 | \end{itemize} 23 | 缺点： 24 | \begin{itemize} 25 | \item 一个线程阻塞会导致整个进程阻塞（因用户级线程对操作系统不可见，操作系统对整个进程进行调度） 26 | \item 不能利用多核和多处理器技术 27 | \end{itemize} 28 | 29 | \bigskip 30 | 内核级线程(KLT)：线程管理由内核完成（提供API），调度基于线程进行，优点： 31 | \begin{itemize} 32 | \item 线程阻塞不会导致进程阻塞 33 | \item 可以利用多核和多处理器技术 34 | \item 内核例程本身也可以使用多线程 35 | \end{itemize} 36 | 缺点： 37 | \begin{itemize} 38 | \item 线程切换需要进行模式切换 39 | \end{itemize} 40 | 41 | \bigskip 42 | 多线程模型 43 | \begin{itemize} 44 | \item 多对一：多个用户级线程映射到一个内核级线程，线程管理在用户空间进行，效率高；若内核服务阻塞，则整个进程被阻塞 45 | \item 一对一：每个用户级线程映射到一个内核级线程，开销大 46 | \item 多对多：折中 47 | \end{itemize} 48 | \begin{figure}[H] 49 | \centering 50 | \includegraphics[width=0.6\linewidth]{fig/threads.png} 51 | \end{figure} 52 | 53 | 线程与进程之间的关系 54 | \begin{itemize} 55 | \item 1：1，每个进程都有唯一线程，DOS、传统Unix 56 | \item M：1，一个进程多个线程，Windows NT、Linux、Mac OS、iOS 57 | \item 1：M，一个线程可在多个进程环境中迁移 58 | \end{itemize} 59 | 60 | * Linux并不区分线程和进程，采用Copy On Write (COW)方式 -------------------------------------------------------------------------------- /OperatingSystems/8_IO.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{IO管理与磁盘调度} 4 | 控制设备和内存或CPU之间的数据传送方式： 5 | \begin{itemize} 6 | \item 程序控制(programmed IO)：需要不断读取IO控制器的状态寄存器，才能决定是否读写数据 7 | \item 中断驱动方式(interrupt-driven IO)：IO设备主动打断CPU运行并请求服务 8 | \item 直接内存访问(Direct Memory Access, DMA)：直接在IO设备和内存之间开辟直接的数据交换通路 9 | \end{itemize} 10 | 11 | 假脱机技术(Simultaneous Peripheral Operations On Line, SPOOL)/虚拟设备技术： 12 | 专门利用一道程序来完成对设备的IO操作，而无需使用外围IO处理机 13 | \begin{figure}[H] 14 | \centering 15 | \includegraphics[width=0.7\linewidth]{fig/SPOOLing.png} 16 | \end{figure} 17 | 优点是高速虚拟IO操作，实现对独享设备的共享 18 | 19 | IO缓冲：CPU快IO慢，提高外设利用率，尽可能使外设处于忙的状态（多道程序并发）；增加缓冲区有利于提高命中率，操作系统采用软件方法实现缓冲技术 20 | \begin{itemize} 21 | \item 单方向缓冲：单缓冲、双缓冲、环形缓冲 22 | \item 双方向缓冲：缓冲池(buffer pool) 23 | \item 循环缓冲 24 | \end{itemize} 25 | 26 | 磁盘调度算法 27 | \begin{itemize} 28 | \item 先来先服务FCFS：公平简单，平均寻道距离大 29 | \item 最短寻找时间优先(shortest seek time first, SSTF)：与当前磁头邻近的磁道，性能比FCFS好，可能出现饥饿；移臂距离最小 30 | \item 电梯算法/扫描算法SCAN：比较实用的调度算法：在最短寻找时间优先算法的基础上规定了磁头的方向，不利于磁头一端的访问请求 31 | \item 循环扫描算法C-SCAN：到头会回到另一侧重新开始，移臂方向改变最少 32 | \end{itemize} 33 | 34 | Windows支持两类RAID配置 35 | \begin{itemize} 36 | \item 硬件RAID：若干独立的物理磁盘，通过磁盘控制器或磁盘存储柜，组合成逻辑磁盘。冗余信息的创建和重新生成由控制器负责处理 37 | \item 软件RAID：不连续的磁盘空间通过容错软件磁盘驱动程序(Fault-Tolerant software DISK driver, FTDISK)组合成逻辑分区。Windows的软件RAID机制实现了RAID1（镜像）和RAID5（交错块分布式奇偶校验） 38 | \end{itemize} -------------------------------------------------------------------------------- /OperatingSystems/README.md: -------------------------------------------------------------------------------- 1 | # Operating Systems 2 | 3 | 操作系统原理笔记，所用教材：William Stallings, *Operating Systems - Internals and Design Principles (8th ed.)*, Pearson, 2017 -------------------------------------------------------------------------------- /OperatingSystems/fig/5-state-process-model.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chhzh123/CS-Notes/266d6601234a9ac814e9163c40202eaeadc8efde/OperatingSystems/fig/5-state-process-model.jpg -------------------------------------------------------------------------------- /OperatingSystems/fig/C_memory.jpg: -------------------------------------------------------------------------------- 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22 | \par 关于计算机系统的内容在此不再赘述，详情参见\textbf{计算机组成原理}的笔记。 23 | 24 | \input{1_introduction} 25 | \input{2_processes} 26 | \input{3_threads} 27 | \input{4_concurrrency} 28 | \input{5_deadlock} 29 | \input{6_memory} 30 | \input{7_scheduling} 31 | \input{8_IO} 32 | \input{9_file_system} 33 | 34 | \end{document} -------------------------------------------------------------------------------- /PatternRecognition/1_introduction.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{简介} % 1.1-1.6 4 | 机器学习侧重于处理的算法，而模式识别则包括了数据预处理、实际运算和数据输出的完整过程。 5 | \begin{itemize} 6 | \item 模式识别：涵盖的范围广，包括特征提取、特征选择、降维、各种分类器等。 7 | \item 机器学习：主要是讲学习，更多关于分类器如何训练模型，而不涉及特征方面的知识。 8 | \end{itemize} 9 | 10 | 良好特征的四个特点： 11 | \begin{itemize} 12 | \item 可区别性（不同类） 13 | \item 可靠性（同类） 14 | \item 独立性（特征之间） 15 | \item 参数少（复杂性） 16 | \end{itemize} 17 | 18 | 一个对象的所有特征参数组成特征向量。 19 | 同样需要从高维测量空间（样本）中提取特征映射到低维特征空间。 20 | 21 | 模式识别分为两类 22 | \begin{itemize} 23 | \item 结构/句法模式识别 24 | \item 统计/神经网络模式识别 25 | \end{itemize} 26 | 27 | 模式识别系统过程如下 28 | \[\text{传感器}\to\text{分割}\to\text{特征提取}\to\text{\textbf{分类器}}\to\text{后处理}\] 29 | 设计循环 30 | \[\text{采集}\to\text{选择基本特征}\to\text{选择模型}\to\text{训练分类}\to\text{评价}\] -------------------------------------------------------------------------------- /PatternRecognition/6_neural_network.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{多层神经网络} % 6.1-6.6 6.8 (6.9) 4 | % 前馈网 6.2 5 | % BP 6.3 6 | % 误差表面 6.4 7 | % 6.5 反向传播作为特征映射 8 | % Bayes与NN 6.6 9 | % 激活函数, 权重衰减, early stoping 6.8 10 | % 6.9 二阶技术（Hessian, Newton, Quickprop, 共轭梯度） 11 | 12 | 三层神经网络：输入层、隐含层、输出层，也称多层感知器(multilayer perceptron, MLP) 13 | \begin{figure}[H] 14 | \centering 15 | \includegraphics[width=0.8\linewidth]{fig/forward_propagation.png} 16 | \end{figure} 17 | 18 | 前馈运算如下，判别函数$y_k(\vx,\vw)$为每个输出单元产生的信号 19 | \[y_{k}(\mathbf{x}, \mathbf{w})=\sigma\left(\sum_{j=1}^{M} w_{k j}^{(2)} h\left(\sum_{i=1}^{D} w_{j i}^{(1)} x_{i}+w_{j 0}^{(1)}\right)+w_{k 0}^{(2)}\right)\] 20 | 21 | 任何从输入到输出的连续映射函数都可以用一个三层非线性网络实现，只要有足够的隐单元$M$、适当的非线性函数和权值。 22 | 23 | 最小化误差函数 24 | \[E(\mathbf{w})=\frac{1}{2} \sum_{n=1}^{N}\left\|\mathbf{y}\left(\mathbf{x}_{n}, \mathbf{w}\right)-\mathbf{t}_{n}\right\|^{2}\] 25 | 26 | 隐含层到输出层的权重更新公式为 27 | \[\Delta w_{kj}=\eta\delta_k y_j=\eta(t_k-z_k)f'(net_k)y_j\] 28 | 输入层到隐含层的权重更新公式为 29 | \[\Delta w_{ji}=\eta x_i\delta_j=\eta\lrs{\sum_{k=1}^c w_{kj}\delta_k}f'(net_j)x_i\] 30 | 31 | sigmoid函数 32 | \[f(\text{net})=a\tanh(b\cdot\text{net})=a\lrs{\frac{1-\ee^{-b\cdot \text{net}}}{1+\ee^{-b\cdot\text{net}}}}=\frac{2a}{1+\ee^{-b\cdot\text{net}}}-a\] 33 | 34 | 改进反向传播的一些技术： 35 | \begin{itemize} 36 | \item 输入信号尺度变换（归一化） 37 | \item 带有噪声/人工扩充数据 38 | \item 权值初始化 39 | \item 隐单元数目、权重衰减、隐含层数目 40 | \item 更好的激活函数 41 | \item 优化器（学习率、冲量项） 42 | \item 批训练 43 | \item 早停(early stopping) 44 | \end{itemize} -------------------------------------------------------------------------------- /PatternRecognition/7_stochastic_methods.tex: -------------------------------------------------------------------------------- 1 | % !TEX root = main.tex 2 | 3 | \section{随机方法} % 7.1-7.3 (7.4-7.5) 4 | 假设给定多个变量$s_i$，其中每个变量数值都取两个离散值之一，记为$\pm 1$。 5 | 优化问题为确定$N$个$s_i$的合适取值，使下述代价函数或能量函数最小 6 | \[E=-\frac{1}{2}\sum_{i,j=1}^N w_{ij}s_is_j\] 7 | 其中$w_{ij}$是对称的，取值可正可负。 8 | \begin{figure}[H] 9 | \centering 10 | \includegraphics[width=0.9\linewidth]{fig/stochastic_opt_problem.png} 11 | \end{figure} 12 | 13 | \subsection{模拟退火} % 7.2 随机搜索 14 | 一个系统具有能量$E_\gamma$通过下式给出 15 | \[P(\gamma)=\frac{\ee^{-E_\gamma/T}}{Z(T)}\] 16 | 其中分子为Boltzmann因子，而$Z$是一个归一化常量/分配(partition)函数 17 | \[Z(T)=\sum_{\gamma'}\ee^{-E_{\gamma'}/T}\] 18 | 为Boltzmann因子对所有构型的求和。 19 | 20 | \begin{algorithm}[H] 21 | \caption{模拟退火(Simulated Annealing)} 22 | \begin{algorithmic}[2] 23 | \State 将网络随机初始化，并设一个高的初始温度$T(1)$ 24 | \State 随机选择节点$i$，设其状态为$s_i=+1$，计算该构型下系统总能量$E_a$ 25 | \State 改变其道候选状态不$s_i=-1$，系统总能量为$E_b$ 26 | \If{$E_b