├── LICENSE ├── M-Learning ├── 1211.5063.pdf ├── Bayesian.pdf ├── Bn.pdf ├── BrownBoost.pdf ├── CV.pdf ├── CV_vs_bootstrap.pdf ├── Decision_Trees.pdf ├── ExactInference.pdf ├── ExponentialFamilies.pdf ├── FMP.pdf ├── GANNuerics.pdf ├── GaussianMarkovNets.pdf ├── GradientBoost.pdf ├── LDA.pdf ├── LPBoost.pdf ├── LogitBoost.pdf ├── MadaBoost.pdf ├── MarkovNets.pdf ├── NFSandMDL.pdf ├── NFSandPDL.pdf ├── NFSvsOrzar.pdf ├── Nutshell.pdf ├── Random_Subspace_method.pdf ├── SMO-platts.pdf ├── SVM.pdf ├── Trees.pdf ├── Untitled.ipynb ├── adaboost4.pdf ├── adam.pdf ├── boosting.pdf ├── cawley10a.pdf ├── cvfinal.pdf ├── cycleGAN.pdf ├── darn.pdf ├── dataaugmentation.pdf ├── detailedLDA.pdf ├── determinationcoff.pdf ├── dropout.pdf ├── elasticnet.pdf ├── error-analysis.pdf ├── ganequal.pdf ├── gdecent.pdf ├── graph_segment.pdf ├── hmm.pdf ├── improvedgan.pdf ├── infoGAN.pdf ├── l7.pdf ├── l8.pdf ├── lasso.pdf ├── learning-theory.pdf ├── logistic.pdf ├── logit_vs_probit.pdf ├── machine-learning-cheat-sheet.pdf ├── multicollinearity.pdf ├── multicollinearty2.pdf ├── nade.pdf ├── nash.pdf ├── outlier.pdf ├── outlier2.pdf ├── pca_tutorial.pdf ├── percepton.pdf ├── perceptron_notes.pdf ├── poison.pdf ├── ppca.pdf ├── probability_cheatsheet.pdf ├── pseudor2.pdf ├── randomsearch.pdf ├── regresion.pdf ├── regularisation.pdf ├── relu.pdf ├── residual_analysis.pdf ├── resnet.pdf ├── resnet1.pdf ├── smo.pdf ├── stacking.pdf ├── stackingissues.pdf ├── stackingwork.pdf ├── tcbbR3.pdf ├── unrolledgan.pdf ├── xavierinitialisation.pdf ├── xgboost.pdf └── xgboost_supp.pdf ├── MIT ML ├── assignments │ ├── hw1.pdf │ ├── hw1a_soln.pdf │ ├── hw1b_soln.pdf │ ├── hw1errata.txt │ ├── hw2.pdf │ ├── hw2_soln.pdf │ ├── hw2errata.txt │ ├── hw3.pdf │ ├── hw3_soln.pdf │ ├── hw3errata.txt │ ├── hw4.pdf │ ├── hw4_soln.pdf │ ├── hw4errata.txt │ ├── hw5.pdf │ ├── hw5_soln.pdf │ ├── hw5errata.txt │ ├── p1.zip │ ├── p3.zip │ ├── perceptron_test.m │ ├── perceptron_train.m │ ├── prob1_data.zip │ ├── prob2_data.zip │ └── strimage.m ├── exams │ ├── final_f01.pdf │ ├── final_f01soln.pdf │ ├── final_f02.pdf │ ├── final_f02soln.pdf │ ├── final_f03.pdf │ ├── final_f03soln.pdf │ ├── final_f04.pdf │ ├── final_f04soln.pdf │ ├── midterm_f01.pdf │ ├── midterm_f01soln.pdf │ ├── midterm_f02.pdf │ ├── midterm_f02soln.pdf │ ├── midterm_f03.pdf │ ├── midterm_f03soln.pdf │ ├── midterm_f04.pdf │ ├── midterm_f04soln.pdf │ └── midterm_f06soln.pdf ├── lecture-notes │ ├── lec1.pdf │ ├── lec10.pdf │ ├── lec11.pdf │ ├── lec12.pdf │ ├── lec13.pdf │ ├── lec14.pdf │ ├── lec15.pdf │ ├── lec16.pdf │ ├── lec17.pdf │ ├── lec18.pdf │ ├── lec19.pdf │ ├── lec2.pdf │ ├── lec20.pdf │ ├── lec21.pdf │ ├── lec22.pdf │ ├── lec23.pdf │ ├── lec3.pdf │ ├── lec4.pdf │ ├── lec5.pdf │ ├── lec6.pdf │ ├── lec7.pdf │ ├── lec8.pdf │ └── lec9.pdf ├── readings │ └── lagrange.pdf └── tools │ ├── files.m │ ├── matrix.m │ └── plot.m ├── MIT Probability ├── assignments │ ├── MIT6_041F10_assn01.pdf │ ├── MIT6_041F10_assn01_sol.pdf │ ├── MIT6_041F10_assn02.pdf │ ├── MIT6_041F10_assn02_sol.pdf │ ├── MIT6_041F10_assn03.pdf │ ├── MIT6_041F10_assn03_sol.pdf │ ├── MIT6_041F10_assn04.pdf │ ├── MIT6_041F10_assn04_sol.pdf │ ├── MIT6_041F10_assn05.pdf │ ├── MIT6_041F10_assn05_sol.pdf │ ├── MIT6_041F10_assn06.pdf │ ├── MIT6_041F10_assn06_sol.pdf │ ├── MIT6_041F10_assn07.pdf │ ├── MIT6_041F10_assn07_sol.pdf │ ├── MIT6_041F10_assn08.pdf │ ├── MIT6_041F10_assn08_sol.pdf │ ├── MIT6_041F10_assn09.pdf │ ├── MIT6_041F10_assn09_sol.pdf │ ├── MIT6_041F10_assn10.pdf │ ├── MIT6_041F10_assn10_sol.pdf │ ├── MIT6_041F10_assn11.pdf │ └── MIT6_041F10_assn11_sol.pdf ├── exams │ ├── MIT6_041F10_final.pdf │ ├── MIT6_041F10_final_f09.pdf │ ├── MIT6_041F10_final_f09_sol.pdf │ ├── MIT6_041F10_final_info.pdf │ ├── MIT6_041F10_final_s09.pdf │ ├── MIT6_041F10_final_s09_sol.pdf │ ├── MIT6_041F10_final_sol.pdf │ ├── MIT6_041F10_quiz01.pdf │ ├── MIT6_041F10_quiz01_f09.pdf │ ├── MIT6_041F10_quiz01_f09_sol.pdf │ ├── MIT6_041F10_quiz01_info.pdf │ ├── MIT6_041F10_quiz01_review.pdf │ ├── MIT6_041F10_quiz01_s09.pdf │ ├── MIT6_041F10_quiz01_s09_sol.pdf │ ├── MIT6_041F10_quiz01_sol.pdf │ ├── MIT6_041F10_quiz02.pdf │ ├── MIT6_041F10_quiz02_f09.pdf │ ├── MIT6_041F10_quiz02_f09_sol.pdf │ ├── MIT6_041F10_quiz02_info.pdf │ ├── MIT6_041F10_quiz02_review.pdf │ ├── MIT6_041F10_quiz02_s08.pdf │ ├── MIT6_041F10_quiz02_s08_sol.pdf │ └── MIT6_041F10_quiz02_sol.pdf ├── recitations │ ├── MIT6_041F10_rec01.pdf │ ├── MIT6_041F10_rec01_sol.pdf │ ├── MIT6_041F10_rec02.pdf │ ├── MIT6_041F10_rec02_sol.pdf │ ├── MIT6_041F10_rec03.pdf │ ├── MIT6_041F10_rec03_sol.pdf │ ├── MIT6_041F10_rec04.pdf │ ├── MIT6_041F10_rec04_sol.pdf │ ├── MIT6_041F10_rec05.pdf │ ├── MIT6_041F10_rec05_sol.pdf │ ├── MIT6_041F10_rec06.pdf │ ├── MIT6_041F10_rec06_sol.pdf │ ├── MIT6_041F10_rec07.pdf │ ├── MIT6_041F10_rec07_sol.pdf │ ├── MIT6_041F10_rec08.pdf │ ├── MIT6_041F10_rec08_sol.pdf │ ├── MIT6_041F10_rec09.pdf │ ├── MIT6_041F10_rec09_sol.pdf │ ├── MIT6_041F10_rec10.pdf │ ├── MIT6_041F10_rec10_sol.pdf │ ├── MIT6_041F10_rec11.pdf │ ├── MIT6_041F10_rec11_sol.pdf │ ├── MIT6_041F10_rec12.pdf │ ├── MIT6_041F10_rec12_sol.pdf │ ├── MIT6_041F10_rec13.pdf │ ├── MIT6_041F10_rec13_sol.pdf │ ├── MIT6_041F10_rec14.pdf │ ├── MIT6_041F10_rec14_sol.pdf │ ├── MIT6_041F10_rec15.pdf │ ├── MIT6_041F10_rec15_sol.pdf │ ├── MIT6_041F10_rec16.pdf │ ├── MIT6_041F10_rec16_sol.pdf │ ├── MIT6_041F10_rec17.pdf │ ├── MIT6_041F10_rec17_sol.pdf │ ├── MIT6_041F10_rec18.pdf │ ├── MIT6_041F10_rec18_sol.pdf │ ├── MIT6_041F10_rec19.pdf │ ├── MIT6_041F10_rec19_sol.pdf │ ├── MIT6_041F10_rec20.pdf │ ├── MIT6_041F10_rec20_sol.pdf │ ├── MIT6_041F10_rec21.pdf │ ├── MIT6_041F10_rec21_sol.pdf │ ├── MIT6_041F10_rec22.pdf │ ├── MIT6_041F10_rec22_sol.pdf │ ├── MIT6_041F10_rec23.pdf │ ├── MIT6_041F10_rec23_sol.pdf │ ├── MIT6_041F10_rec24.pdf │ └── MIT6_041F10_rec24_sol.pdf └── tutorials │ ├── MIT6_041F10_tut01.pdf │ ├── MIT6_041F10_tut01_sol.pdf │ ├── MIT6_041F10_tut02.pdf │ ├── MIT6_041F10_tut02_sol.pdf │ ├── MIT6_041F10_tut03.pdf │ ├── MIT6_041F10_tut03_sol.pdf │ ├── MIT6_041F10_tut04.pdf │ ├── MIT6_041F10_tut04_sol.pdf │ ├── MIT6_041F10_tut05.pdf │ ├── MIT6_041F10_tut05_sol.pdf │ ├── MIT6_041F10_tut06.pdf │ ├── MIT6_041F10_tut06_sol.pdf │ ├── MIT6_041F10_tut07.pdf │ ├── MIT6_041F10_tut07_sol.pdf │ ├── MIT6_041F10_tut08.pdf │ ├── MIT6_041F10_tut08_sol.pdf │ ├── MIT6_041F10_tut09.pdf │ ├── MIT6_041F10_tut09_sol.pdf │ ├── MIT6_041F10_tut10.pdf │ ├── MIT6_041F10_tut10_sol.pdf │ ├── MIT6_041F10_tut11.pdf │ └── MIT6_041F10_tut11_sol.pdf └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Puneet Mangla 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /M-Learning/1211.5063.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/M-Learning/1211.5063.pdf -------------------------------------------------------------------------------- /M-Learning/Bayesian.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/M-Learning/Bayesian.pdf -------------------------------------------------------------------------------- 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0.1367372182078336 -1 -4\n", 100 | "-0.4201670368266409 0.9074467814501962 -1 -4\n", 101 | "0.5365729180004349 0.8438539587324921 -1 -4\n", 102 | "0.9999902065507035 0.004425697988050785 -1 -4\n", 103 | "0.5440211108893698 -0.8390715290764524 -1 -4\n", 104 | "-0.4121184852417566 -0.9111302618846769 -1 -4\n", 105 | "-0.9893582466233818 -0.14550003380861354 -1 -4\n", 106 | "-0.6569865987187891 0.7539022543433046 -1 -4\n", 107 | "0.27941549819892586 0.960170286650366 -1 -4\n", 108 | "0.9589242746631385 0.28366218546322625 -1 -4\n", 109 | "0.7568024953079282 -0.6536436208636119 -1 -4\n", 110 | "-0.1411200080598672 -0.9899924966004454 -1 -4\n", 111 | "-0.9092974268256817 -0.4161468365471424 -1 -4\n", 112 | "-0.8414709848078965 0.5403023058681398 -1 -4\n" 113 | ] 114 | }, 115 | { 116 | "ename": "ValueError", 117 | "evalue": "math domain error", 118 | "output_type": "error", 119 | "traceback": [ 120 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 121 | "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", 122 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0msinx\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mcosx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m#print(sinx,cosx)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mpsinx\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mpcosx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msinx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcosx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msinx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcosx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;31m#print(psinx,pcosx)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 123 | "\u001b[0;31mValueError\u001b[0m: math domain error" 124 | ] 125 | } 126 | ], 127 | "source": [ 128 | "import matplotlib.pyplot as plt\n", 129 | "import math\n", 130 | "from random import randint\n", 131 | "# trignometric\n", 132 | "x = [i-100 for i in range(200)]\n", 133 | "y = [0.0 for i in range(200)]\n", 134 | "k = 100\n", 135 | "for i in range(k):\n", 136 | " ck ,m,n= randint(-10,10),randint(-10,10),randint(-10,10)\n", 137 | " print (ck,m,n)\n", 138 | " for j in range(len(x)):\n", 139 | " sinx , cosx = math.sin(x[j]) , math.cos(x[j])\n", 140 | " #print(sinx,cosx)\n", 141 | " psinx , pcosx = math.pow(sinx,int(m)) , math.pow(cosx,int(n))\n", 142 | " print(sinx,cosx,m,n)\n", 143 | " #print(psinx,pcosx)\n", 144 | " #y[j] += ck*psinx*pcosx\n", 145 | "\n", 146 | "plt.plot(x,y)\n", 147 | "plt.show()\n", 148 | " \n", 149 | " \n", 150 | " \n" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": null, 156 | "metadata": {}, 157 | "outputs": [], 158 | "source": [] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": null, 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": null, 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": null, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": null, 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [] 194 | } 195 | ], 196 | "metadata": { 197 | "kernelspec": { 198 | "display_name": "Python 3", 199 | "language": "python", 200 | "name": "python3" 201 | }, 202 | "language_info": { 203 | "codemirror_mode": { 204 | "name": "ipython", 205 | "version": 3 206 | }, 207 | "file_extension": ".py", 208 | "mimetype": 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The u_2's in one 7 | term in the numerator and one term in the denominator should be u_1's. 8 | 9 | 10 | 11 | 12 | 13 | 14 | Section B 15 | ========== 16 | 17 | Prob 1: 18 | ------- 19 | Q. What is the stopping criterion for the Perceptron 20 | training algorithm? 21 | 22 | A. It should stop when it makes no errors at all on the training data. For 23 | purposes of this assignment, you may assume that the training data is 24 | always linearly separable and the Perceptron will always stop, after 25 | enough updates. 26 | 27 | 28 | Q. Should we allow for offsets (i.e. \theta_0) in the classifier? 29 | 30 | A. No. The decision boundary should always go through the origin, i.e., 31 | \theta_0 = 0 always. 32 | 33 | 34 | 35 | 36 | Prob 2: 37 | ------- 38 | Q. I've implemented the SVM in matlab, but it seems to be 39 | taking a really long time to train the data from Prob #1. Is this normal, 40 | or am I doing something wrong? 41 | 42 | A. We just did a quick svm_train() implementation and it seems to run 43 | fairly quickly (i.e. in seconds) on data from Prob #1. It also seem to be 44 | giving the right results as well, so I don't know what's going wrong for 45 | you. Try the MATLAB on Athena, if you aren't sure your version is the 46 | latest and greatest. 47 | 48 | 49 | 50 | Q. When we implement SVM classifier, do we need to include 'c' as one of 51 | the input? 52 | 53 | A. No, you needn't. 54 | 55 | 56 | 57 | Q. Should we use the generalized rule with the offset etc.? 58 | 59 | A. No, you needn't. For this problem, just implement a simple SVM without 60 | offset or regularization. 61 | 62 | 63 | 64 | 65 | 66 | Prob 3: 67 | ------- 68 | Q. In part (a) we are asked to plot the image which was 69 | mis-classified. Could you tell me how I should do that? I looked into the 70 | data file, and it seems to be some integer values. But how can I plot 71 | them? 72 | 73 | A. look at the strimage.m code in the "data" section of the pset: 74 | http://courses.csail.mit.edu/6.867/hw1/ 75 | 76 | Also look at the link to actual PNG images (also available from pset 77 | page): http://alawi.csail.mit.edu/~alawi/6867/hw1/images/ 78 | -------------------------------------------------------------------------------- /MIT ML/assignments/hw2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw2.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw2_soln.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw2_soln.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw2errata.txt: -------------------------------------------------------------------------------- 1 | Problem #1 2 | =========== 3 | 10/11: 4 | 5 | The goal of this problem is to help you appreciate the importance 6 | of the feature space in which active learning is performed. We've 7 | provided you two feature mappings: phi_1 and phi_2. The first one 8 | maps X_in to a space that is essentially the same as the original space 9 | (except for an added offset). The second one, on the other hand, 10 | maps X_in to a space that is quite different. Thus, if the regression 11 | is linear in phi_2's space, the problem discusses what happens when 12 | active learning is performed in phi_1's space (which is almost like 13 | the original space) vs. phi_2's space. 14 | 15 | To clarify this, we're updating the problem set so that the last sentence 16 | of Prob 1(c) should instead be: 17 | 18 | "For each set of points, use the feature mapping $\phi_2$ to perform 19 | regression and compute the resulting mean squared prediction errors (MSE) 20 | over the entire data set (again, using $\phi_2$)." 21 | 22 | Note that this change also effects how regression (and evaluation) is 23 | performed in 1(d). 24 | 25 | Also, we emphasize that IF YOU HAVE ALREADY SUBMITTED THE SOLUTIONS, 26 | YOU NEED NOT RESUBMIT! Solutions adhering to the original problem statement 27 | will not be penalized. 28 | 29 | 10/10: Some students had trouble loading the file 'al.mat'. 30 | This was usually because they were using an older version of MATLAB. 31 | We've updated the data file 'al.mat'. The updated file contains exactly 32 | the same data, but is saved so as to be compatible with both 33 | MATLAB v6 and MATLAB v7. The original data file is still 34 | available-- as "al_orig.mat" 35 | 36 | 37 | 10/10: When choosing points according to the active learning strategy, at 38 | each iteration you may (1) restrict your choices to only those points that 39 | have not been previously selected or (2) choose from all points, even 40 | those points that have been chosen earlier (i.e., idx will then contain 41 | repeated entries). Either strategy is acceptable. When 42 | following (2), you must use y_noisy for each of the instances of the 43 | repeated data point. We recognize that in a more realistic active 44 | learning setting, you'd have the opportunity to make multiple distinct 45 | observations at a single point; that is not the case here. 46 | 47 | 48 | 10/10: When computing MSPE, you must compare the predicted values of y 49 | to the true value (y_true), not y_noisy. 50 | 51 | 52 | 10/10: The values of k1 and k2 specified in part (c) are also the ones 53 | to be used in part (d). 54 | 55 | 56 | 57 | Problem #2 58 | =========== 59 | 60 | 61 | 62 | 63 | Problem #3 64 | =========== 65 | 10/9: The discriminant function we expect you to write should be of the form: 66 | 67 | f = discriminant function(alpha, X, kernel type, X test) 68 | 69 | X was missing as an argument. 70 | 71 | 72 | 73 | 10/10: The perceptron update rules should be written with '<=' rather than strict inequality '<' tests for mistakes. 74 | -------------------------------------------------------------------------------- /MIT ML/assignments/hw3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw3.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw3_soln.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw3_soln.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw3errata.txt: -------------------------------------------------------------------------------- 1 | Problem #1 2 | =========== 3 | 10/24: 4 | In Eqn 3, the square ("^2") should be inside the expectation, i.e., it should 5 | read as: 6 | E{error_{1}(S_n)} = E{(y- \hat{f}_{S_{n-1}}(x))^2} 7 | -------------------------------------------------------------------------------- /MIT ML/assignments/hw4.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw4.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw4_soln.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw4_soln.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw4errata.txt: -------------------------------------------------------------------------------- 1 | Prob #1: 2 | --------- 3 | 4 | In Equation 3, the second exponent should be outside the parenthesis, 5 | i.e., the right-hand side of the equation should read as: 6 | 7 | \theta_{i|y}^{\frac{x_i+1}{2}} (1-\theta_{i|y})^{\frac{1-x_i}{2}} 8 | 9 | Also, the "empty" Equation 4 was a LaTeX error on our part :-) There's 10 | nothing that is supposed to be there. 11 | 12 | 13 | The answers to this problem will not depend upon the particular prior you 14 | choose for the distribution of y. In other words, if P(y=1) = \theta_y and 15 | P(y=-1) = 1- \theta_y, your answers will, essentially, not depend on the 16 | prior distribution of \theta_y. In particular, you are free to choose a 17 | uniform prior P(\theta_y) = 1, if it simplifies your math. Your answers 18 | will *not* be penalized on this aspect. 19 | 20 | 21 | 1(a) 22 | ----- 23 | The right-hand side of the equation should have a normalization 24 | constant to ensure that the posterior P(\theta|D) is a valid probability 25 | distribution, i.e., the expression \prod_{i} \prod_{y} (....) should be 26 | preceded by a normalization constant. 27 | 28 | 29 | 1(d) 30 | ---- 31 | You may assume that -A2 is positive definite. Also, there is an 32 | error in the statement: instead of the determinant of A2 i.e. |A2| = n 33 | C(r) (approx.), it should be that the determinant of -A2 i.e. |-A2| = 34 | (n^r) C(r), approximately. 35 | 36 | 37 | 1(e) 38 | ---- 39 | The d in exponent should be replaced by r, i.e., it should be 40 | (2\pi/n)^{r/2} 41 | 42 | 43 | 44 | 45 | Prob #2: 46 | -------- 47 | 48 | - Please see hw4/prob2/README for some clarifications on what the split 49 | function should return. 50 | - Also, some people were having difficulty loading the cepstra data. 51 | Please try the updated files and contact Ali if you still run into 52 | problems. 53 | - In part (a), t_0 should be 1, and t_m should be N+1. 54 | 55 | Hint for (b): 56 | - A d-dimensional multivariate Gaussian has PDF: 57 | f(X; \mu, \Sigma) = {1 \over \sqrt{(2\pi)^d|\Sigma|}} \exp\left(-{1 \over 2}(X - \mu)' \Sigma^{-1} (X - \mu)\right) 58 | - Maximum likelihood estimators for its parameters are: 59 | \mu = {1 \over N}\sum_{i=1}^N X_i 60 | \Sigma = {1 \over N}\sum_{i=1}^N (X_i - \mu)(X_i - \mu)' 61 | - The number of degrees of freedom is d for mu and d(d+1)/2 for Sigma 62 | (not one for each). 63 | - You will need to assume that splitpoints don't occur sufficiently early 64 | in the data (skip, say, the first and last 30 possible splitpoints). This 65 | will mitigate small det(Sigma) problems that some of you have encountered 66 | and also removes some serious artifiacts. 67 | 68 | Prob #3: 69 | -------- 70 | 71 | (e) Only run call_boosting.m for the first 30 iterations. The m-file 72 | call_boosting.m has been updated. -------------------------------------------------------------------------------- /MIT ML/assignments/hw5.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw5.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw5_soln.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/hw5_soln.pdf -------------------------------------------------------------------------------- /MIT ML/assignments/hw5errata.txt: -------------------------------------------------------------------------------- 1 | Prob 1: 2 | ------- 3 | 4 | 5 | 6 | Prob 2: 7 | ------- 8 | 9 | 11/25: 10 | ====== 11 | There is a bug in the em_gp.m code. 12 | At line 131 (in function Overall_Log_Likelihood), please change 13 | ll_i = log_likelihood_gp(V(k,:), t, Y_obs); 14 | to 15 | ll_i = log_likelihood_gp(V(i,:), t, Y_obs); 16 | 17 | i.e. the index "k" should be "i" 18 | 19 | 20 | 21 | Also, the "division by zero" warning happens when the current 22 | cluster has no genes assigned to it. We have changed the code to 23 | not throw a warning in such a case. 24 | The updated code (which includes the bug fix mentioned above) 25 | is available in hw5/prob2/ sub-directory as code-updated.zip 26 | 27 | 28 | The input argument "init_class" in em_gp.m is optional, as you 29 | may have noticed. If not supplied, it is initialized by randomly 30 | assigning each gene to one of k clusters (1..k) 31 | 32 | 33 | 11/27 34 | ====== 35 | Some students have reported problems in using plot_results(). 36 | This seems to be happening because 't.dat' is being loaded as a 37 | column vector as a row vector. Using t' (i.e, the transpose) instead 38 | of t in the function call should fix this. -------------------------------------------------------------------------------- /MIT ML/assignments/p1.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/p1.zip -------------------------------------------------------------------------------- /MIT ML/assignments/p3.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/p3.zip -------------------------------------------------------------------------------- /MIT ML/assignments/perceptron_test.m: -------------------------------------------------------------------------------- 1 | function test_err = perceptron_test(theta, X_test, y_test) 2 | % test_err = perceptron_test(theta, X_test, y_test) 3 | % test a Perceptron classifier on given data and theta 4 | % 5 | 6 | [m, d] = size(X_test); 7 | y_pred = sign(X_test*theta); 8 | test_err = sum(abs(sign(y_test - y_pred)))/m; 9 | end 10 | -------------------------------------------------------------------------------- /MIT ML/assignments/perceptron_train.m: -------------------------------------------------------------------------------- 1 | function [theta, k] = perceptron_train(X,y) 2 | % [theta, k] = perceptron_train(X,y) 3 | % train a Perceptron classifier on given data 4 | % 5 | num_correct = 0; 6 | 7 | [n,d] = size(X); 8 | 9 | curr_index = 1; 10 | theta = zeros(d,1); 11 | k = 0; 12 | 13 | is_first_iter = 1; 14 | 15 | while (num_correct < n) 16 | xt = X(curr_index,:)'; 17 | yt = y(curr_index); 18 | 19 | a = sign(yt*(theta'*xt)); 20 | 21 | if (is_first_iter==1 | a < 0) 22 | num_correct = 0; 23 | theta = theta + (yt*xt); 24 | k = k+1; 25 | is_first_iter = 0; 26 | else 27 | num_correct = num_correct + 1; 28 | end 29 | 30 | curr_index = 1 + curr_index; 31 | if (curr_index > n) 32 | curr_index = 1; 33 | end 34 | 35 | end 36 | -------------------------------------------------------------------------------- /MIT ML/assignments/prob1_data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/prob1_data.zip -------------------------------------------------------------------------------- /MIT ML/assignments/prob2_data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/assignments/prob2_data.zip -------------------------------------------------------------------------------- /MIT ML/assignments/strimage.m: -------------------------------------------------------------------------------- 1 | function strimage(a) 2 | lena = size(a); 3 | lena = lena(2); 4 | xy = sscanf(a(4:lena), '%d:%d'); 5 | lenxy = size(xy); 6 | lenxy = lenxy(1); 7 | grid = []; 8 | grid(784) = 0; 9 | for i=2:2:lenxy 10 | grid(xy(i-1)) = xy(i) * 100/255; 11 | end 12 | image(reshape(grid,28,28)) 13 | end 14 | 15 | -------------------------------------------------------------------------------- /MIT ML/exams/final_f01.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/exams/final_f01.pdf -------------------------------------------------------------------------------- /MIT ML/exams/final_f01soln.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Puneet2000/In-Depth-ML/4ec7444bb12e268b22da70480c9d0f5102a6416d/MIT ML/exams/final_f01soln.pdf 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-------------------------------------------------------------------------------- /MIT ML/tools/files.m: -------------------------------------------------------------------------------- 1 | % Written by Jason Rennie for 6.891, Sept. 2000 2 | 3 | % To run a matlab script (.m extension), simply type the name of the 4 | % script on the command-line. Here, we assume that a file named 5 | % 'linear_regression.m' exists in the current directory. 6 | linear_regression 7 | 8 | % WARNING!!! Matlab won't let you use any file name you want. For 9 | % example Matlab treats dashes, '-', as special characters. 10 | 11 | % Get a listing of available variables with 'who' or 'whos' 12 | x = [1 2 3 4 5] 13 | y = [1 2 3; 4 5 6] 14 | who 15 | whos 16 | -------------------------------------------------------------------------------- /MIT ML/tools/matrix.m: -------------------------------------------------------------------------------- 1 | % Written by Jason Rennie for 6.891, Sept. 2000 2 | 3 | % Use the percent sign to write a comment 4 | % THIS IS A SAMPLE COMMENT 5 | 6 | % In order to get help from matlab, type 'help' or 'helpwin' 7 | help 8 | helpwin 9 | 10 | % To get help on a specific function, type 'help function' 11 | help sum 12 | 13 | % Use these help commands to get some details on Matlab syntax 14 | help paren 15 | help punct 16 | 17 | % Create a matrix with square brackets; put spaces (or commas) between elements 18 | x = [1 2 3 4 5] 19 | % Use semi-colon to not print result of command 20 | y = [1.2,1.7,2.9,4.5,5.6]; 21 | 22 | % Create a vector of equally spaced values with the colon operator 23 | x = 1:5 24 | % Specify difference between values by splicing the difference between 25 | % the start and end points 26 | z = 1:0.5:5 27 | 28 | % Access elements with parenthesis 29 | x(2) 30 | y(4) 31 | y(5) 32 | 33 | % Create a new row with a semi-colon 34 | z = [1 2 3; 4 5 6] 35 | 36 | % To perform matrix operations, use regular operators (e.g. +, -, *, /) 37 | % To perform vector/array operations, use dot operators (e.g. `.*', `./') 38 | z = x.*y 39 | 40 | % To square the elements of an array, use the dot-power operator 41 | z = x.^2 42 | 43 | % Transpose a matrix with a single-quote 44 | b = x*y' 45 | 46 | % Use the 'size' function to get the width and height of a matrix 47 | [n,m] = size(x'*y) 48 | 49 | % Obtain the length of a vector, use the function 'length' 50 | l = length(x) 51 | 52 | % Invert a matrix with the command 'inv' 53 | c = [1 2;2 1] 54 | d = inv(c) 55 | 56 | % Use the pseudo-inverse command, 'pinv', to invert singular matrices 57 | c = [1 2;1 2] 58 | d = pinv(c) 59 | 60 | -------------------------------------------------------------------------------- /MIT ML/tools/plot.m: -------------------------------------------------------------------------------- 1 | % Written by Jason Rennie for 6.891, Oct. 2000 2 | 3 | % Plot some points on a graph 4 | a = [1;2;3;4;5]; 5 | plot(a,'bx'); 6 | 7 | % Plot some more points on that same graph 8 | b = [6;5;4;3;2]; 9 | hold on; 10 | plot(b, 'go'); 11 | 12 | % See help to figure out what 'bx' and 'go' mean 13 | help plot 14 | helpwin plot 15 | 16 | % Clear the graph with every new plot command 17 | hold off; 18 | 19 | % Plot a square---note that here we are controling both x and y axes 20 | c = [2;2;2;3;4;4;4;3;2]; 21 | d = [2;3;4;4;4;3;2;2;2]; 22 | plot(c,d,'k-'); 23 | 24 | % Plot two squares on the same graph (without using the 'hold on' command) 25 | plot(c,d,'b-',c+1,d+1,'r--'); 26 | 27 | % Set range of x and y axes 28 | axis([0 6 1 7]); 29 | 30 | % Put multiple plots in the same window 31 | subplot(2,1,1), plot(a,'bx'); 32 | subplot(2,1,2), plot(c,d,'k-'); 33 | axis([0 6 1 7]); 34 | 35 | % Return to single plot in window 36 | subplot(1,1,1); 37 | plot(c,d,'b-',c+1,d+1,'r--'); 38 | axis([0 6 1 7]); 39 | 40 | % Set labels and title. 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1](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/multicollinearity.pdf) 9 | - [Multicollinearity Analysis 2](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/multicollinearty2.pdf) 10 | - [Residual Analysis](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/residual_analysis.pdf) 11 | - [Cofficient of determination](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/determinationcoff.pdf) 12 | - [Outlier Analysis 1](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/outlier.pdf) 13 | - [Outlier Analysis 1](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/outlier2.pdf) 14 | - [Local Outlier factors](https://blog.stealthbits.com/local-outlier-factor-part-2) 15 | - [Regression Shrinkage and selection via Lasso](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/lasso.pdf) 16 | - [Elastic Net - Regularisation and variable selection](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/elasticnet.pdf) 17 | 18 | ### Logistic Regression 19 | - [Logistic Regression Concept](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/logistic.pdf) 20 | - [Logit vs Probit model](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/logit_vs_probit.pdf) 21 | - [Poisson Model](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/poison.pdf) 22 | - [Pseudo R2 for logistic Regression](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/pseudor2.pdf) 23 | 24 | ### Trees 25 | - [Decison Tress intermidiate](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/Decision_Trees.pdf) 26 | - [Understanding Random Forests](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/Trees.pdf) 27 | - [Decison tree from Scratch 1](https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/) 28 | - [Decison tree from Scratch 2](https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/#fourteen) 29 | - [Random Subspace Method](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/Random_Subspace_method.pdf) 30 | 31 | ### Cross Validation 32 | - [CV vs Bootstraping](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/CV_vs_bootstrap.pdf) 33 | - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/cawley10a.pdf) 34 | - [Andrew Ng's Percentile cv method of cross-validation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/cvfinal.pdf) 35 | 36 | ### Support Vector machines 37 | - [SVM's concept](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/SVM.pdf) 38 | - [SMO algorithm](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/SMO-platts.pdf) 39 | - [Simplified SMO by stanford](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/smo.pdf) 40 | 41 | ### Perceptron Update 42 | - [Perceptron 1](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/perceptron_notes.pdf) 43 | - [perceptron 2](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/percepton.pdf) 44 | 45 | ### Linear Discriminant Analysis 46 | - [LDA introduction](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/LDA.pdf) 47 | - [Detailed LDA](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/detailedLDA.pdf) 48 | 49 | ### Boosting 50 | - [Boosting 1](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/boosting.pdf) 51 | - [Boosting 2](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/boosting2.pdf) 52 | - [Gradient Boost overview](http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/1) 53 | - [Gradient Boost from scratch](https://www.kaggle.com/grroverpr/gradient-boosting-simplified/) 54 | - [Gradient Boosting Machines](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/GradientBoost.pdf) 55 | - [XgBoost](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/xgboost.pdf) 56 | - [XGBoost Supplement](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/xgboost_supp.pdf) 57 | - [AdaBoost and the Super Bowl of Classifiers](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/adaboost.pdf) 58 | - [Adaboost Wikipedia](https://en.wikipedia.org/wiki/AdaBoost#Implementations_in_Python) 59 | - [Adaboost Tutorial](http://mccormickml.com/2013/12/13/adaboost-tutorial/) 60 | - [Logit Boosting Algorithm](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/LogitBoost.pdf) 61 | - [MadaBoost Algorithm](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/MadaBoost.pdf) 62 | - [BrownBoost Algorithm](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/BrownBoost.pdf) 63 | - [LPBoost Algorithm](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/LPBoost.pdf) 64 | 65 | ### Stack Generalization/Stacking 66 | - [Stack Generalization by Wolpert](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/stacking.pdf) 67 | - [When does it work?](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/stackingwork.pdf) 68 | - [Issues with Stack generalisation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/stackingissues.pdf) 69 | - [Stacking from scratch in python](https://machinelearningmastery.com/implementing-stacking-scratch-python/) 70 | - [Non-Negative least square cofficient method for classification](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/tcbbR3.pdf) 71 | 72 | ### Data Preprocessing 73 | - [Missing Values](https://en.wikipedia.org/wiki/Missing_data) 74 | - [Outliners](https://en.wikipedia.org/wiki/Outlier) 75 | - [Categorial Encoding](https://en.wikipedia.org/wiki/Categorical_variable) 76 | 77 | ### Constraint solving 78 | - [Constraint Solving](http://www.cs.ubc.ca/~schmidtm/MLSS/) 79 | 80 | ### Kaggle Past Competitions with winning Solutions 81 | - [Kaggle competitions and solutions 1](http://ndres.me/kaggle-past-solutions/) 82 | - [Kaggle competitions and solutions 2](http://www.chioka.in/kaggle-competition-solutions/) 83 | 84 | ### Convolutional Neural Networks 85 | - [Intiutve Explanation of CNN](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) 86 | - [How is a convolutional neural network able to learn invariant features?](https://www.quora.com/How-is-a-convolutional-neural-network-able-to-learn-invariant-features) 87 | - [Max vs average Pooling](https://www.quora.com/What-is-the-benefit-of-using-average-pooling-rather-than-max-pooling) 88 | - [Dropout](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/dropout.pdf) 89 | - [Explaining Xavier Initialisation easily](http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization) 90 | - [Xaviers Initialisation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/xavierinitialisation.pdf) 91 | - [Batch Normalizaton](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/Bn.pdf) 92 | - [Understanding the backward pass through Batch Normalization Layer](https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html) 93 | - [Random vs Grid Search](https://stats.stackexchange.com/questions/160479/practical-hyperparameter-optimization-random-vs-grid-search) 94 | - [Random Search For Hyper Parameter Optimisation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/randomsearch.pdf) 95 | - [Gradient Descent Optimisation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/gdecent.pdf) 96 | - [Adam Optimisation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/adam.pdf) 97 | - [Fractional Pooling](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/FPM.pdf) 98 | - [ReLU's](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/relu.pdf) 99 | - [Data Augmentation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/dataaugmentation.pdf) 100 | - [CNN in Pytorch](https://blog.algorithmia.com/convolutional-neural-nets-in-pytorch/) 101 | - [Resnet](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/resnet1.pdf) 102 | - [Principal Compponent Analysis](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/pca_tutorial.pdf) 103 | 104 | 105 | ### Sequence Models 106 | - [Understanding RNN](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/) 107 | - [Guide to Recurrent Neural Networks: Understanding the Intuition](https://towardsdatascience.com/illustrated-guide-to-recurrent-neural-networks-79e5eb8049c9) 108 | - [Guide to LSTM’s and GRU’s: A step by step explanation](https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21) 109 | - [Neural Turing machines]() 110 | - [HMM](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/hmm.pdf) 111 | - [The magic of LSTM neural networks](https://medium.com/datathings/the-magic-of-lstm-neural-networks-6775e8b540cd) 112 | - [The fall of RNN / LSTM](https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0) 113 | - [Pixel CNN](http://sergeiturukin.com/2017/02/22/pixelcnn.html) 114 | - [Blind Spot problem in PixelCNN](https://towardsdatascience.com/blind-spot-problem-in-pixelcnn-8c71592a14a) 115 | - [Gated Pixel CNN](https://arxiv.org/pdf/1606.05328.pdf) 116 | 117 | ### Transpose Convolution 118 | - [An Introduction to different Types of Convolutions in Deep Learning](https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d) 119 | - [Up-sampling with Transposed Convolution](https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0) 120 | - [Demystifying Transpose Convolution](https://towardsdatascience.com/transpose-convolution-77818e55a123) 121 | - [Deconvolution and Checkerboard Artifacts](https://distill.pub/2016/deconv-checkerboard/) 122 | - [Convolution arithmetic tutorial](http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html) 123 | 124 | ### Generative Adversial Networks 125 | - [GAN — GAN Series (from the beginning to the end)](https://medium.com/@jonathan_hui/gan-gan-series-2d279f906e7b) 126 | - [What is wrong with the GAN cost function?](https://medium.com/@jonathan_hui/gan-what-is-wrong-with-the-gan-cost-function-6f594162ce01) 127 | - [EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES](https://arxiv.org/pdf/1412.6572.pdf) 128 | - [Introduction to GANs, NIPS 2016 | Ian Goodfellow, OpenAI](https://www.youtube.com/watch?v=9JpdAg6uMXs) 129 | - [Differentiable Inference and Generative Models](http://www.cs.toronto.edu/~duvenaud/courses/csc2541/) 130 | - [Pytorch CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) 131 | - [Various GAN's implemented in torch](https://github.com/wiseodd/generative-models) 132 | - [Nash Equilibrium](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/nash.pdf) 133 | - [InfoGAN](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/infoGAN.pdf) 134 | - [UnrolledGAN](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/unrolledgan.pdf) 135 | - [GAN NUmerics](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/GANNuerics.pdf) 136 | - [cycleGAN](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/cycleGAN.pdf) 137 | - [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/1211.5063.pdf) 138 | - [Improved Techniques for Training GANs](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/improvedgan.pdf) 139 | - [Are GANs Created Equal? A Large-Scale Study](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/ganequalpdf) 140 | 141 | ### Probabilistic Models in Deep Learning 142 | - [Probabilistic Graphical Models Tutorial — Part 1](https://blog.statsbot.co/probabilistic-graphical-models-tutorial-and-solutions-e4f1d72af189) 143 | - [Probabilistic Graphical Models Tutorial — Part 2](https://blog.statsbot.co/probabilistic-graphical-models-tutorial-d855ba0107d1) 144 | - [Bayesian Directed Networks](https://github.com/Puneet2000/In-Depth-ML/blob/master/Bayesian.pdf) 145 | - [Markov Undirected Networks](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/MarkovNets.pdf) 146 | - [Gaussian BN](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/GaussianMarkovNets.pdf) 147 | - [Exponential Families](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/ExponentialFamilies.pdf) 148 | - [Exact Inference - Vatiable Elimination](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/ExactInference.pdf) 149 | - [Graph Models in Nutshell](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/Nutshell.pdf) 150 | 151 | ### Restricted Boltzmann Machines 152 | - [Introduction](https://towardsdatascience.com/deep-learning-meets-physics-restricted-boltzmann-machines-part-i-6df5c4918c15) 153 | - [Very Good Explanation of RBM I](https://medium.com/@neuralnets/boltzmann-machines-transformation-of-unsupervised-deep-learning-part-1-42659a74f530) 154 | - [Very Good Explanation of RBM II](https://medium.com/@neuralnets/boltzmann-machines-transformation-of-unsupervised-deep-learning-part-2-cfb1dab81437) 155 | - [Orignal Paper]() 156 | - [Guide to Training RBM's] 157 | - [Tensorflow Example](https://towardsdatascience.com/deep-learning-meets-physics-restricted-boltzmann-machines-part-ii-4b159dce1ffb) 158 | 159 | ### Adversial Examples and Adversial Training 160 | - [Adversarial Examples and Adversarial Training](https://www.youtube.com/watch?v=CIfsB_EYsVI) 161 | - [Explaining and Harnessing Adversarial Examples](https://arxiv.org/pdf/1412.6572.pdf) 162 | - [Intriguing properties of neural networks](https://arxiv.org/pdf/1312.6199.pdf) 163 | - [Regularisation of Neural Networks by Enforcing Lipschitz Continuity](https://arxiv.org/pdf/1804.04368.pdf) 164 | - [Adversarial Diversity and Hard Positive Generation](https://www.cv-foundation.org//openaccess/content_cvpr_2016_workshops/w12/papers/Rozsa_Adversarial_Diversity_and_CVPR_2016_paper.pdf) 165 | - [DeepFool: a simple and accurate method to fool deep neural networks](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Moosavi-Dezfooli_DeepFool_A_Simple_CVPR_2016_paper.pdf) 166 | - [Universal adversarial perturbations](https://arxiv.org/pdf/1610.08401.pdf) 167 | - [Towards Evaluating the Robustness of Neural Networks](https://arxiv.org/pdf/1608.04644.pdf) 168 | 169 | ### Extras and Interesting 170 | - [Machine learning cheat sheet](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/machine-learning-cheat-sheet.pdf) 171 | - [Probability cheat sheet](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/probability_cheatsheet.pdf) 172 | - [Regularisation](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/regularisation.pdf) 173 | - [error Analysis](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/error-analysis) 174 | - [MIT probability course](https://github.com/Puneet2000/In-Depth-ML/tree/master/MIT%20Probability) 175 | - [MIT Machine learning course](https://github.com/Puneet2000/In-Depth-ML/tree/master/MIT%20ML) 176 | - [“Deep learning - Information theory & Maximum likelihood.”](https://jhui.github.io/2017/01/05/Deep-learning-Information-theory/) 177 | - [Exemplar CNNs and Information Maximization](https://www.inference.vc/exemplar-cnns-and-information-maximization/) 178 | - [Dilated Convolutions and Kronecker Factored Convolutions](https://www.inference.vc/dilated-convolutions-and-kronecker-factorisation/) 179 | - [Swish Activation Function by Google](https://medium.com/@neuralnets/swish-activation-function-by-google-53e1ea86f820) 180 | - [Capsule Networks](https://medium.com/@pechyonkin/part-iv-capsnet-architecture-6a64422f7dce) 181 | - [Maximum Likelihood Estimation](https://towardsdatascience.com/parameter-inference-maximum-likelihood-2382ef895408) 182 | - [Maximum Aposteriori](https://towardsdatascience.com/parameter-inference-maximum-aposteriori-estimate-49f3cd98267a) 183 | - [Understanding and Using Principal Component Analysis (PCA)](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/l7.pdf) 184 | - [How PCA works](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/l8.pdf) 185 | - [Probabilistic Principal Component Analysis](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/ppca.pdf) 186 | - [The Neural Autoregressive Distribution Estimator](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/nade.pdf) 187 | - [Deep AutoRegressive Networks](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/darn.pdf) 188 | - [When Bayes, Ockham, and Shannon come together to define machine learning](https://towardsdatascience.com/when-bayes-ockham-and-shannon-come-together-to-define-machine-learning-96422729a1ad) 189 | - [No-Free-Lunch and the Minimum Description Length](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/NFSandMDL.pdf) 190 | - [No Free Lunch versus Occam’s Razor in Supervised Learning](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/NFSvsOrzar.pdf) 191 | - [No-Free-Lunch and the Problem Description Length](https://github.com/Puneet2000/In-Depth-ML/blob/master/M-Learning/NFSandPDL.pdf) 192 | - [Demystifying KL Divergence](https://towardsdatascience.com/demystifying-kl-divergence-7ebe4317ee68) 193 | 194 | ### Other Tutorials 195 | - [CS231n Stanford course](https://www.youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk) 196 | - [Machine Learning for beginners](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) 197 | - [Mathematics of machine learning](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) 198 | - [PyTorch tutorials for deep Learning ](https://www.kaggle.com/kanncaa1/pytorch-tutorial-for-deep-learning-lovers) 199 | - [PyTorch](https://pytorch.org/tutorials/) 200 | 201 | 202 | --------------------------------------------------------------------------------