TensorFlow Quick Reference Table – Cheat Sheet.
TensorFlow is very popular deep learning library, with its complexity can be overwhelming especially for new users. Here is a short summary of often used functions, if you want to download it in pdf it is available here:
TensorFlow Cheat Sheet – WWW.MLHYPE.COM
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Import TensorFlow: 

import tensorflow as tf  
Basic math operations: 

tf.add()  sum 
tf.subtract()  substraction 
tf.multiply()  multiplication 
tf.div()  division 
tf.mod()  module 
tf.abs()  absolute value 
tf.negative()  negative value 
tf.sign()  return sign 
tf.reciprocal()  reciprocal 
tf.square()  square 
tf.round()  nearest intiger 
tf.sqrt()  square root 
tf.pow()  power 
tf.exp()  exponent 
tf.log()  logarithm 
tf.maximum()  maximum 
tf.minimum()  minimum 
tf.cos()  cosine 
tf.sin()  sine 
Basic operations on tensors: 

tf.string_to_number()  converts string to numeric type 
tf.cast()  casts to new type 
tf.shape()  returns shape of tensor 
tf.reshape()  reshapes tensor 
tf.diag()  creates tensor with given diagonal values 
tf.zeros()  creates tensor with all elements set to zero 
tf.fill()  creates tensor with all elements set given value 
tf.concat()  concatenates tensors 
tf.slice()  extracts slice from tensor 
tf.transpose()  transpose the argument 
tf.matmul()  matrices multiplication 
tf.matrix_determinant()  determinant of matrices 
tf.matrix_inverse()  computes inverse of matrices 
Control Flow: 

tf.while_loop()  repeat body while condition true 
tf.case()  case operator 
tf.count_up_to()  incriments ref untill limit 
tf.tuple()  groups tensors together 
Logical/Comparison Operators: 

tf.equal()  returns truth value elementwise 
tf.not_equal()  returns truth value of X!=Y 
tf.less()  returns truth value of X<Y 
tf.less_equal()  returns truth value of X<=Y 
tf.greater()  returns truth value of X>Y 
tf.greater_equal()  returns truth value of X>=Y 
tf.is_nan()  returns which elements are NaN 
tf.logical_and()  returns truth value of ‘AND’ for given tensors 
tf.logical_or()  returns truth value of ‘OR’ for given tensors 
tf.logical_not()  returns truth value of ‘NOT’ for given tensors 
tf.logical_xor()  returns truth value of ‘XOR’ for given tensors 
Working with Images: 

tf.image.decode_image()  converts image to tensor type uint8 
tf.image.resize_images()  resize images 
tf.image.resize_image_with_crop_or_pad()  resize image by cropping or padding 
tf.image.flip_up_down()  flip image horizontally 
tf.image.rot90()  rotate image 90 degrees counterclockwise 
tf.image.rgb_to_grayscale()  converts image from RGB to grayscale 
tf.image.per_image_standardization()  scales image to zero mean and unit norm 
Neural Networks: 

tf.nn.relu()  rectified linear activation function 
tf.nn.softmax()  softmax activation function 
tf.nn.sigmoid()  sigmoid activation function 
tf.nn.tanh()  hyperbolic tangent activation function 
tf.nn.dropout  dropout 
tf.nn.bias_add  adds bias to value 
tf.nn.all_candidate_sampler()  set of all classes 
tf.nn.weighted_moments()  returns mean and variance 
tf.nn.softmax_cross_entropy_with_logits()  softmax cross entropy 
tf.nn.sigmoid_cross_entropy_with_logits()  sigmoid cross entropy 
tf.nn.l2_normalize()  normalization using L2 Norm 
tf.nn.l2_loss()  L2 loss 
tf.nn.dynamic_rnn()  RNN specified by given cell 
tf.nn.conv2d()  2D convolutions given 4D input 
tf.nn.conv1d()  1D convolution given 3D input 
tf.nn.batch_normalization()  batch normalization 
tf.nn.xw_plus_b()  computes matmul(x,weights)+biases 
High level Machine Learning: 

tf.contrib.keras  Keras API as high level API for TensorFlow 
tf.contrib.layers.one_hot_column()  one hot encoding 
tf.contrib.learn.LogisticRegressor()  logistic regression 
tf.contrib.learn.DNNClassifier()  DNN classifier 
tf.contrib.learn.DynamicRnnEstimator()  Rnn Estimator 
tf.contrib.learn.KMeansClustering()  KMeans Clusstering 
tf.contrib.learn.LinearClassifier()  linear classifier 
tf.contrib.learn.LinearRegressor()  linear regressor 
tf.contrib.learn.extract_pandas_data()  extract data from Pandas dataframe 
tf.contrib.metrics.accuracy()  accuracy 
tf.contrib.metrics.auc_using_histogram()  AUC 
tf.contrib.metrics.confusion_matrix()  confusion matrix 
tf.contrib.metrics.streaming_mean_absolute_error()  mean absolute error 
tf.contrib.rnn.BasicLSTMCell()  basic lstm cell 
tf.contrib.rnn.BasicRNNCell()  basic rnn cell 
Placeholders and Variables: 

tf.placeholder()  defines placeholder 
tf.Variable(tf.random_normal([3, 4], stddev=0.1)  defines variable 
tf.Variable(tf.zeros([50]), name=’x’)  defines variable 
tf.global_variables_initializer()  initialize global variables 
tf.local_variables_initializer()  initialize local variables 
with tf.device(“/cpu:0”):  pin variable to CPU 
v = tf.Variable()  
with tf.device(“/gpu:0”):  pin variable to GPU 
v = tf.Variable()  
sess = tf.Session()  run session 
sess.run()  
sess.close()  
with tf.Session() as session:  run session(2) 
session.run()  
saver=tf.train.Saver()  Saving and restoring variables. 
saver.save(sess,’file_name’)  
saver.restore(sess,’file_name’)  
Working with Data: 

tf.decode_csv()  converts csv to tensors 
tf.read_file()  reads file 
tf.write_file()  writes to file 
tf.train.batch()  creates batches of tensors 
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If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com.
Recommended reading list:
HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikitlearn to track an example machinelearning project endtoend Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details 

TensorFlow Machine Learning Cookbook This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get handson experience with Linear Regression techniques with TensorFlow. The next chapters cover important highlevel concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with handson recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production 

Learning TensorFlow: A Guide to Building Deep Learning Systems TensorFlow is currently the leading opensource software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an endtoend guide to TensorFlow designed for data scientists, engineers, students and researchers. With this book you will learn how to: Get up and running with TensorFlow, rapidly and painlessly Build and train popular deep learning models for computer vision and NLP Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs Train models at scale, and deploy TensorFlow in a production setting 

TensorFlow for Machine Intelligence: A HandsOn Introduction to Learning Algorithms TensorFlow, a popular library for machine learning, embraces the innovation and communityengagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API. 

Machine Learning with TensorFlow Being able to make nearrealtime decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world. 