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TensorFlow Deep Learning Projects

By : Rajalingappaa Shanmugamani, Alexey Grigorev, Srinivas Kulkarni

2 (1)

Buy this Book

TensorFlow Deep Learning Projects

2 (1)

By: Rajalingappaa Shanmugamani, Alexey Grigorev, Srinivas Kulkarni

Buy this Book

Overview of this book

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

Recognizing traffic signs using Convnets

The dataset

The CNN network

Image preprocessing

Train the model and make predictions

Follow-up questions

Summary

Annotating Images with Object Detection API

The Microsoft common objects in context

The TensorFlow object detection API

Presenting our project plan

Provisioning of the project code

Acknowledgements

Summary

Caption Generation for Images

What is caption generation?

Exploring image captioning datasets

Converting words into embeddings

Image captioning approaches

Implementing a caption generation model

Summary

Building GANs for Conditional Image Creation

Introducing GANs

The project

Putting CGAN to work on some examples

Resorting to Amazon Web Service

Acknowledgements

Summary

Stock Price Prediction with LSTM

Input datasets – cosine and stock price

Format the dataset

Using regression to predict the future prices of a stock

Long short-term memory – LSTM 101

Stock price prediction with LSTM

Possible follow - up questions

Summary

Create and Train Machine Translation Systems

A walkthrough of the architecture

Preprocessing of the corpora

Training the machine translator

Test and translate

Summary

Train and Set up a Chatbot, Able to Discuss Like a Human

Introduction to the project

The input corpus

Creating the training dataset

Training the chatbot

Chatbox API

Summary

Detecting Duplicate Quora Questions

Presenting the dataset

Starting with basic feature engineering

Creating fuzzy features

Resorting to TF-IDF and SVD features

Mapping with Word2vec embeddings

Testing machine learning models

Building a TensorFlow model

Processing before deep neural networks

Deep neural networks building blocks

Designing the learning architecture

Summary

Building a TensorFlow Recommender System

Recommender systems

Matrix factorization for recommender systems

RNN for recommender systems

Summary

Video Games by Reinforcement Learning

The game legacy

The OpenAI version

Installing OpenAI on Linux (Ubuntu 14.04 or 16.04)

Exploring reinforcement learning through deep learning

Starting the project

Acknowledgements

Summary

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The Microsoft common objects in context

Advances in application of deep learning in computer vision are often highly focalized on the kind of classification problems that can be summarized by challenges such as ImageNet (but also, for instance, PASCAL VOC - http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) and the ConvNets suitable to crack it (Xception, VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, just to quote the ones available in the well-known package Keras: https://keras.io/applications/).

Though deep learning networks based on ImageNet data are the actual state of the art, such networks can experience difficulties when faced with real-world applications. In fact, in practical applications, we have to process images that are quite different from the examples provided by ImageNet. In ImageNet the elements to be classified are clearly the only clear element present...

Tech Concepts

Programming languages

Tech Tools