Deep Learning with TensorFlow.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networ...
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Full text (MCPHS users only) |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2017
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; Introducing machine learning; Supervised learning; Unsupervised learning; Reinforcement learning; What is deep learning?; How the human brain works; Deep learning history; Problems addressed; Neural networks; The biological neuron; An artificial neuron; How does an artificial neural network learn?; The backpropagation algorithm; Weights optimization; Stochastic gradient descent; Neural network architectures.
- Multilayer perceptronDNNs architectures; Convolutional Neural Networks; Restricted Boltzmann Machines; Autoencoders; Recurrent Neural Networks; Deep learning framework comparisons; Summary; Chapter 2: First Look at TensorFlow; General overview; What's new with TensorFlow 1.x?; How does it change the way people use it?; Installing and getting started with TensorFlow; Installing TensorFlow on Linux; Which TensorFlow to install on your platform?; Requirements for running TensorFlow with GPU from NVIDIA; Step 1: Install NVIDIA CUDA; Step 2: Installing NVIDIA cuDNN v5.1+
- Step 3: GPU card with CUDA compute capability 3.0+Step 4: Installing the libcupti-dev library; Step 5: Installing Python (or Python3); Step 6: Installing and upgrading PIP (or PIP3); Step 7: Installing TensorFlow; How to install TensorFlow; Installing TensorFlow with native pip; Installing with virtualenv; Installing TensorFlow on Windows; Installation from source; Install on Windows; Test your TensorFlow installation; Computational graphs; Why a computational graph?; Neural networks as computational graphs; The programming model; Data model; Rank; Shape; Data types; Variables; Fetches; Feeds.
- TensorBoardHow does TensorBoard work?; Implementing a single input neuron; Source code for the single input neuron; Migrating to TensorFlow 1.x; How to upgrade using the script; Limitations; Upgrading code manually; Variables; Summary functions; Simplified mathematical variants; Miscellaneous changes; Summary; Chapter 3: Using TensorFlow on a Feed-Forward Neural Network; Introducing feed-forward neural networks; Feed-forward and backpropagation; Weights and biases; Transfer functions; Classification of handwritten digits; Exploring the MNIST dataset; Softmax classifier; Visualization.
- How to save and restore a TensorFlow modelSaving a model; Restoring a model; Softmax source code; Softmax loader source code; Implementing a five-layer neural network; Visualization; Five-layer neural network source code; ReLU classifier; Visualization; Source code for the ReLU classifier; Dropout optimization; Visualization; Source code for dropout optimization; Summary; Chapter 4: TensorFlow on a Convolutional Neural Network; Introducing CNNs; CNN architecture; A model for CNNs
- LeNet; Building your first CNN; Source code for a handwritten classifier; Emotion recognition with CNNs.
