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
Preview in Mapt

Deep Learning with TensorFlow

Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

2 customer reviews
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide
Mapt Subscription
FREE
$29.99/m after trial
eBook
$28.00
RRP $39.99
Save 29%
Print + eBook
$49.99
RRP $49.99
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$28.00
$49.99
$29.99 p/m after trial
RRP $39.99
RRP $49.99
Subscription
eBook
Print + eBook
Start 30 Day Trial

Frequently bought together


Deep Learning with TensorFlow Book Cover
Deep Learning with TensorFlow
$ 39.99
$ 28.00
Hands-On Deep Learning with TensorFlow Book Cover
Hands-On Deep Learning with TensorFlow
$ 27.99
$ 14.00
Buy 2 for $31.50
Save $36.48
Add to Cart

Book Details

ISBN 139781786469786
Paperback320 pages

Book Description

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.

Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.

After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

Table of Contents

Chapter 1: Getting Started with Deep Learning
Introducing machine learning
What is deep learning?
Neural networks
How does an artificial neural network learn?
Neural network architectures
Autoencoders
Recurrent Neural Networks
Deep learning framework comparisons
Summary
Chapter 2: First Look at TensorFlow
General overview
Installing TensorFlow on Linux
Requirements for running TensorFlow with GPU from NVIDIA
How to install TensorFlow
Installing TensorFlow on Windows
Computational graphs
Why a computational graph?
The programming model
Data model
TensorBoard
Implementing a single input neuron
Source code for the single input neuron
Migrating to TensorFlow 1.x
Summary
Chapter 3: Using TensorFlow on a Feed-Forward Neural Network
Introducing feed-forward neural networks
Classification of handwritten digits
Exploring the MNIST dataset
Softmax classifier
How to save and restore a TensorFlow model
Implementing a five-layer neural network
ReLU classifier
Visualization
Dropout optimization
Visualization
Summary
Chapter 4: TensorFlow on a Convolutional Neural Network
Introducing CNNs
CNN architecture
Building your first CNN
Emotion recognition with CNNs
Summary
Chapter 5: Optimizing TensorFlow Autoencoders
Introducing autoencoders
Implementing an autoencoder
Improving autoencoder robustness
Building a denoising autoencoder
Convolutional autoencoders
Summary
Chapter 6: Recurrent Neural Networks
RNNs basic concepts
RNNs at work
Unfolding an RNN
The vanishing gradient problem
LSTM networks
An image classifier with RNNs
Bidirectional RNNs
Text prediction
Summary
Chapter 7: GPU Computing
GPGPU computing
GPGPU history
The CUDA architecture
GPU programming model
TensorFlow GPU set up
TensorFlow GPU management
GPU memory management
Assigning a single GPU on a multi-GPU system
Using multiple GPUs
Summary
Chapter 8: Advanced TensorFlow Programming
Introducing Keras
Building deep learning models
Sentiment classification of movie reviews
Adding a convolutional layer
Pretty Tensor
Digit classifier
TFLearn
Titanic survival predictor
Summary
Chapter 9: Advanced Multimedia Programming with TensorFlow
Introduction to multimedia analysis
Deep learning for Scalable Object Detection
Accelerated Linear Algebra
TensorFlow and Keras
Deep learning on Android
Summary
Chapter 10: Reinforcement Learning
Basic concepts of Reinforcement Learning
Q-learning algorithm
Introducing the OpenAI Gym framework
FrozenLake-v0 implementation problem
Q-learning with TensorFlow
Source code for the Q-learning neural network
Summary

What You Will Learn

  • Learn about machine learning landscapes along with the historical development and progress of deep learning
  • Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
  • Access public datasets and utilize them using TensorFlow to load, process, and transform data
  • Use TensorFlow on real-world datasets, including images, text, and more
  • Learn how to evaluate the performance of your deep learning models
  • Using deep learning for scalable object detection and mobile computing
  • Train machines quickly to learn from data by exploring reinforcement learning techniques
  • Explore active areas of deep learning research and applications

Authors

Table of Contents

Chapter 1: Getting Started with Deep Learning
Introducing machine learning
What is deep learning?
Neural networks
How does an artificial neural network learn?
Neural network architectures
Autoencoders
Recurrent Neural Networks
Deep learning framework comparisons
Summary
Chapter 2: First Look at TensorFlow
General overview
Installing TensorFlow on Linux
Requirements for running TensorFlow with GPU from NVIDIA
How to install TensorFlow
Installing TensorFlow on Windows
Computational graphs
Why a computational graph?
The programming model
Data model
TensorBoard
Implementing a single input neuron
Source code for the single input neuron
Migrating to TensorFlow 1.x
Summary
Chapter 3: Using TensorFlow on a Feed-Forward Neural Network
Introducing feed-forward neural networks
Classification of handwritten digits
Exploring the MNIST dataset
Softmax classifier
How to save and restore a TensorFlow model
Implementing a five-layer neural network
ReLU classifier
Visualization
Dropout optimization
Visualization
Summary
Chapter 4: TensorFlow on a Convolutional Neural Network
Introducing CNNs
CNN architecture
Building your first CNN
Emotion recognition with CNNs
Summary
Chapter 5: Optimizing TensorFlow Autoencoders
Introducing autoencoders
Implementing an autoencoder
Improving autoencoder robustness
Building a denoising autoencoder
Convolutional autoencoders
Summary
Chapter 6: Recurrent Neural Networks
RNNs basic concepts
RNNs at work
Unfolding an RNN
The vanishing gradient problem
LSTM networks
An image classifier with RNNs
Bidirectional RNNs
Text prediction
Summary
Chapter 7: GPU Computing
GPGPU computing
GPGPU history
The CUDA architecture
GPU programming model
TensorFlow GPU set up
TensorFlow GPU management
GPU memory management
Assigning a single GPU on a multi-GPU system
Using multiple GPUs
Summary
Chapter 8: Advanced TensorFlow Programming
Introducing Keras
Building deep learning models
Sentiment classification of movie reviews
Adding a convolutional layer
Pretty Tensor
Digit classifier
TFLearn
Titanic survival predictor
Summary
Chapter 9: Advanced Multimedia Programming with TensorFlow
Introduction to multimedia analysis
Deep learning for Scalable Object Detection
Accelerated Linear Algebra
TensorFlow and Keras
Deep learning on Android
Summary
Chapter 10: Reinforcement Learning
Basic concepts of Reinforcement Learning
Q-learning algorithm
Introducing the OpenAI Gym framework
FrozenLake-v0 implementation problem
Q-learning with TensorFlow
Source code for the Q-learning neural network
Summary

Book Details

ISBN 139781786469786
Paperback320 pages
Read More
From 2 reviews

Read More Reviews

Recommended for You

Deep Learning with Keras Book Cover
Deep Learning with Keras
$ 39.99
$ 28.00
Deep Learning: Practical Neural Networks with Java Book Cover
Deep Learning: Practical Neural Networks with Java
$ 67.99
$ 47.60
Eder Santana's Deep Learning with Python Book Cover
Eder Santana's Deep Learning with Python
$ 27.99
$ 19.60
Hands-On Deep Learning with TensorFlow Book Cover
Hands-On Deep Learning with TensorFlow
$ 27.99
$ 14.00
Mastering Python Scientific Computing Book Cover
Mastering Python Scientific Computing
$ 31.99
$ 22.40
Mastering Blockchain Book Cover
Mastering Blockchain
$ 39.99
$ 28.00