tensorflow for deep learning.pdf下载

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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 Shor
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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

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

TensorFlow for Deep Learning(Early Release) 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除本资源转载自网络，如有侵权，请联系上传者或csdn删除

Deep learning with tensorflow 2017.4.pdf

In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. You will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By the end of this book, you will be an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.

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