Book Description
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain.
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.
With a problem-solution approach, 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 using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.
What You Will Learn
- Install TensorFlow and use it for CPU and GPU operations
- Implement DNNs and apply them to solve different AI-driven problems.
- Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code.
- Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box.
- Use different regression techniques for prediction and classification problems
- Build single and multilayer perceptrons in TensorFlow
- Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases.
- Learn how restricted Boltzmann Machines can be used to recommend movies.
- Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection.
- Master the different reinforcement learning methods to implement game playing agents.
- GANs and their implementation using TensorFlow.
Table of Contents
1: TensorFlow - An Introduction
- Introduction
- Installing TensorFlow
- Hello world in TensorFlow
- Understanding the TensorFlow program structure
- Working with constants, variables, and placeholders
- Performing matrix manipulations using TensorFlow
- Using a data flow graph
- Migrating from 0.x to 1.x
- Using XLA to enhance computational performance
- Invoking CPU/GPU devices
- TensorFlow for Deep Learning
- Different Python packages required for DNN-based problems
2: Regression
- Introduction
- Choosing loss functions
- Optimizers in TensorFlow
- Reading from CSV files and preprocessing data
- House price estimation-simple linear regression
- House price estimation-multiple linear regression
- Logistic regression on the MNIST dataset
3: Neural Networks - Perceptron
- Introduction
- Activation functions
- Single layer perceptron
- Calculating gradients of backpropagation algorithm
- MNIST classifier using MLP
- Function approximation using MLP-predicting Boston house prices
- Tuning hyperparameters
- Higher-level APIs-Keras
4: Convolutional Neural Networks
- Introduction
- Creating a ConvNet to classify handwritten MNIST numbers
- Creating a ConvNet to classify CIFAR-10
- Transferring style with VGG19 for image repainting
- Using a pretrained VGG16 net for transfer learning
- Creating a DeepDream network
5: Advanced Convolutional Neural Networks
- Introduction
- Creating a ConvNet for Sentiment Analysis
- Inspecting what filters a VGG pre-built network has learned
- Classifying images with VGGNet, ResNet, Inception, and Xception
- Recycling pre-built Deep Learning models for extracting features
- Very deep InceptionV3 Net used for Transfer Learning
- Generating music with dilated ConvNets, WaveNet, and NSynth
- Classifying videos with pre-trained nets in six different ways
6: Recurrent Neural Networks
- Introduction
- Neural machine translation - training a seq2seq RNN
- Neural machine translation - inference on a seq2seq RNN
- All you need is attention - another example of a seq2seq RNN
- Learning to write as Shakespeare with RNNs
- Learning to predict future Bitcoin value with RNNs
- Many-to-one and many-to-many RNN examples
7: Unsupervised Learning
- Introduction
- Principal component analysis
- k-means clustering
- Self-organizing maps
- Restricted Boltzmann Machine
- Recommender system using RBM
- DBN for Emotion Detection
8: Autoencoders
- Introduction
- Vanilla autoencoders
- Sparse autoencoder
- Denoising autoencoder
- Convolutional autoencoders
- Stacked autoencoder
9: Reinforcement Learning
- Introduction
- Learning OpenAI Gym
- Implementing neural network agent to play Pac-Man
- Q learning to balance Cart-Pole
- Game of Atari using Deep Q Networks
- Policy gradients to play the game of Pong
10: Mobile Computation
- Introduction
- Installing TensorFlow mobile for macOS and Android
- Playing with TensorFlow and Android examples
- Installing TensorFlow mobile for macOS and iPhone
- Optimizing a TensorFlow graph for mobile devices
- Profiling a TensorFlow graph for mobile devices
- Transforming a TensorFlow graph for mobile devices
11: Generative Models and CapsNet
- Introduction
- Learning to forge MNIST images with simple GANs
- Learning to forge MNIST images with DCGANs
- Learning to forge Celebrity Faces and other datasets with DCGAN
- Implementing Variational Autoencoders
- Learning to beat the previous MNIST state-of-the-art results with Capsule Networks
12: Distributed TensorFlow and Cloud Deep Learning
- Introduction
- Working with TensorFlow and GPUs
- Playing with Distributed TensorFlow: multiple GPUs and one CPU
- Playing with Distributed TensorFlow: multiple servers
- Training a Distributed TensorFlow MNIST classifier
- Working with TensorFlow Serving and Docker
- Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
- Running Distributed TensorFlow on Google CloudML
- Running Distributed TensorFlow on Microsoft Azure
- Running Distributed TensorFlow on Amazon AWS
13: Learning to Learn with AutoML (Meta-Learning)
- Meta-learning with recurrent networks and with reinforcement learning
- Meta-learning blocks
- Meta-learning novel tasks
- Siamese Network
14: TensorFlow Processing Units
- Components of TPUs
SKU | 031039SE |
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Weight | 0.0000 |
Coming Soon | No |
Days of Training | No |
Audience | Student |
Product Family | Partnerware |
Product Type | Digital Courseware |
Electronic | Yes |
ISBN | No |
Language | English |
Page Count | No |
Curriculum Library | TensorFlow |
Year | No |
Manufacturer's Product Code | No |
Current Revision | 1.0 |
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Revision Notes | No Revision Information Available |
Original Publication Date | 2018-10-17 00:00:00 |