tensorflow for deep learning.pdf

1. Introduction to Deep Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Machine Learning eats Computer Science 10 Deep Learning Primitives 11 Fully Connected Layer 11 Convolutional Layer 12 Recurrent Neural Network (RNN) Layers 13 Long Short-Term Memory (LSTM) Cells 14 Deep Learning Zoo 14 LeNet 14 AlexNet 15 ResNet 16 Neural Captioning Model 16 Google Neural Machine Translation 17 One shot models 18 AlphaGo 19 Generative Adversarial Networks 20 Neural Turing Machines 21 Deep Learning Frameworks 22 Empirical Learning 25 2. Introduction toTensorfow Primitives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Introducing Tensors 27 Scalars, Vectors, and Matrices 28 Matrix Mathematics 31 Tensors 33 Tensors in physics. 35 Mathematical Asides 37 Basic Computations in Tensorflow 38 Initializing Constant Tensors. 38 Sampling Random Tensors 40 Tensor Addition and Scaling 40 Matrix Operations 41 Tensor types 42 Tensor Shape Manipulations 43 Introduction to Broadcasting 44 Imperative and Declarative Programming 45 Tensorflow Graphs 46 Tensorflow Sessions 46 Tensorflow Variables 47 Review 4