├── .gitignore ├── 10_pyspark └── PySpark_Cheat_Sheet_Python.pdf ├── 11_Math ├── Calculus │ └── Calculus_Cheat_Sheet_All.pdf ├── Linearalgebra │ └── Savov_Notes.pdf └── Statistics │ └── stats_handout.pdf ├── 12_visualization ├── Python_Bokeh_Cheat_Sheet.pdf └── Python_Matplotlib_Cheat_Sheet.pdf ├── 13_Git ├── atlassian_git_cheatsheet.pdf └── github-git-cheat-sheet.pdf ├── 14_linux └── bash_cheat_sheet.pdf ├── 1_Linuxbookforbeginners └── Book_linuxfun.pdf ├── 2_Python ├── PythonForDataScience.pdf └── python.pdf ├── 3_numpy ├── Numpy_Python_Cheat_Sheet.pdf └── numpy.pdf ├── 4_scipy └── Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf ├── 5_pandas ├── PandasPythonForDataScience.pdf └── pandas.pdf ├── 6_flask └── flask-cheatsheet.pdf ├── 7_scikitlearn └── Scikit_Learn_Cheat_Sheet_Python.pdf ├── 8_tensorflow ├── altoros-tensorflow-cheat-sheet.pdf └── tensorflow.md ├── 9_keras └── Keras_Cheat_Sheet_Python.pdf └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by .ignore support plugin (hsz.mobi) 2 | ### Python template 3 | # Byte-compiled / optimized / DLL files 4 | __pycache__/ 5 | *.py[cod] 6 | *$py.class 7 | 8 | # C extensions 9 | *.so 10 | 11 | # Distribution / packaging 12 | .Python 13 | build/ 14 | develop-eggs/ 15 | dist/ 16 | downloads/ 17 | eggs/ 18 | .eggs/ 19 | lib/ 20 | lib64/ 21 | parts/ 22 | sdist/ 23 | var/ 24 | wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | .idea/ 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | .hypothesis/ 51 | 52 | # Translations 53 | *.mo 54 | *.pot 55 | 56 | # Django stuff: 57 | *.log 58 | .static_storage/ 59 | .media/ 60 | local_settings.py 61 | 62 | # Flask stuff: 63 | instance/ 64 | .webassets-cache 65 | 66 | # Scrapy stuff: 67 | .scrapy 68 | 69 | # Sphinx documentation 70 | docs/_build/ 71 | 72 | # PyBuilder 73 | target/ 74 | 75 | # Jupyter Notebook 76 | .ipynb_checkpoints 77 | 78 | # pyenv 79 | .python-version 80 | 81 | # celery beat schedule file 82 | celerybeat-schedule 83 | 84 | # SageMath parsed files 85 | *.sage.py 86 | 87 | # Environments 88 | .env 89 | .venv 90 | env/ 91 | venv/ 92 | ENV/ 93 | env.bak/ 94 | venv.bak/ 95 | 96 | # Spyder project settings 97 | .spyderproject 98 | .spyproject 99 | 100 | # Rope project settings 101 | .ropeproject 102 | 103 | # mkdocs documentation 104 | /site 105 | 106 | # mypy 107 | .mypy_cache/ 108 | 109 | -------------------------------------------------------------------------------- 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| A `placeholder` is a value to input on a TensorFlow computation. It cannot be 12 | evaluated, as it must be fed a value using a `feed_dict`. 13 | 14 | x = tf.placeholder(tf.float32, shape=(1024, 1024)) 15 | y = tf.matmul(x, x) # Matrix Multiplication (or Dot product) 16 | 17 | with tf.Session() as session: 18 | print(session.run(y)) # Error: x wasn't fed any value 19 | 20 | rand_array = np.random.rand(1024, 1024) 21 | print(session.run(y, feed_dict={x: rand_array}) # Success 22 | 23 | ## Variables 24 | 25 | Variables are useful to hold and update parameters. Unlike placeholders, 26 | variables are initialized with some value and can be updated through the 27 | computation. They are in-memory buffers containing tensors. 28 | 29 | # create two variables 30 | w = tf.Variable(tf.random_normal([784, 200], stddev=0.35), 31 | name='w') 32 | b = tf.Variable(tf.zeros([200]), name='b') 33 | 34 | ### Initialization 35 | 36 | A variable initializer must be run before other ops in the model can be run. 37 | Exists an operation that runs all variable initilizations: 38 | `tf.initialize_all_variables()`. 39 | 40 | # create two variables 41 | w = tf.Variable(tf.random_normal([784, 200], stddev=0.35), 42 | name='w') 43 | b = tf.Variable(tf.zeros([200]), name='b') 44 | ... 45 | # add an op to initialize the variables 46 | init_op = tf.initialize_all_variables() 47 | 48 | # later, when launching the model 49 | with tf.Session() as sess: 50 | # run the init operation 51 | sess.run(init_op) 52 | ... 53 | # use the model 54 | ... 55 | 56 | #### Initialization from another variable 57 | 58 | # create a variable with a random value. 59 | w1 = tf.Variable(tf.random_normal([784, 200], stddev=0.35), 60 | name='w1') 61 | # create another variable with the same value as 'w1'. 62 | w2 = tf.Variable(w1.initialized_value(), name='w1') 63 | # Create another variable with twice the value of 'w1' 64 | w1_twice = tf.Variable(w1.initialized_value() * 2.0, name="w1_twice") 65 | 66 | ### Saving a session's variables 67 | 68 | Use a `tf.train.Saver()` object to save the session's variables (all or some) 69 | and restore it later. 70 | 71 | # Create some variables. 72 | v1 = tf.Variable(..., name="v1") 73 | v2 = tf.Variable(..., name="v2") 74 | ... 75 | # Add an op to initialize the variables. 76 | init_op = tf.initialize_all_variables() 77 | 78 | # Add ops to save and restore all the variables. 79 | saver = tf.train.Saver() 80 | 81 | # Later, launch the model, initialize the variables, do some work, save the 82 | # variables to disk. 83 | with tf.Session() as sess: 84 | sess.run(init_op) 85 | # Do some work with the model. 86 | .. 87 | # Save the variables to disk. 88 | save_path = saver.save(sess, "/tmp/model.ckpt") 89 | print("Model saved in file: %s" % save_path) 90 | 91 | ### Restoring a session's variables 92 | 93 | How to restore variables: 94 | 95 | # Create some variables. 96 | v1 = tf.Variable(..., name="v1") 97 | v2 = tf.Variable(..., name="v2") 98 | ... 99 | # Add ops to save and restore all the variables. 100 | saver = tf.train.Saver() 101 | 102 | # Later, launch the model, use the saver to restore variables from disk, and 103 | # do some work with the model. 104 | with tf.Session() as sess: 105 | # Restore variables from disk. 106 | saver.restore(sess, "/tmp/model.ckpt") 107 | print("Model restored.") 108 | # Do some work with the model 109 | ... 110 | 111 | ### Choose variables to save in a session 112 | 113 | # Create some variables. 114 | v1 = tf.Variable(..., name="v1") 115 | v2 = tf.Variable(..., name="v2") 116 | ... 117 | # Add ops to save and restore only 'v2' using the name "my_v2" 118 | saver = tf.train.Saver({"my_v2": v2}) 119 | # Use the saver object normally after that. 120 | ... 121 | -------------------------------------------------------------------------------- /9_keras/Keras_Cheat_Sheet_Python.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jalajthanaki/cheat_sheets_of_python_libraries/e53b4cbe0dc83ba9e4d522466c7f8d5c9e2051df/9_keras/Keras_Cheat_Sheet_Python.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Cheat Sheets of Python Libraries 2 | 3 | This repository contains following cheat sheets. 4 | 5 | * Basic Linux OS commands 6 | * Python for data science 7 | * numpy 8 | * scipy 9 | * pandas 10 | * flask 11 | * scikitlearn 12 | * tensorflow 13 | * keras 14 | * pyspark 15 | * Important mathematical concepts 16 | * visualisation libraries: Bokeh and matplotlib 17 | * Git commands 18 | 19 | Enjoy..! 20 | 21 | Hope this will help you. --------------------------------------------------------------------------------