Python Deep Learning Projects / Lamons, Matthew.

Format:

Book

Author/Creator:

Lamons, Matthew, author.

Kumar, Rahul (Artificial intelligence scientist), author.

Nagaraja, Abhishek, author.

Language:

English

Subjects (All):

Python (Computer program language).

Machine learning.

Neural networks (Computer science).

Artificial intelligence.

Physical Description:

1 online resource (472 pages)

Edition:

1st edition

Place of Publication:

Packt Publishing, 2018.

System Details:

text file

Summary:

Insightful projects to master deep learning and neural network architectures using Python and Keras Key Features Explore deep learning across computer vision, natural language processing (NLP), and image processing Discover best practices for the training of deep neural networks and their deployment Access popular deep learning models as well as widely used neural network architectures Book Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You'll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you'll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you'll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learn Set up a deep learning development environment on Amazon Web Services (AWS) Apply GPU-powered instances as well as the deep learning AMI Implement seq-to-seq networks for modeling natural language processing (NLP) Develop an end-to-end speech recognition system Build a system for pixel-wise semantic labeling of an image Create a system that generates images and their regions Who this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: Building Deep Learning Environments

Building a common DL environment

Get focused and into the code!

DL environment setup locally

Downloading and installing Anaconda

Installing DL libraries

Setting up a DL environment in the cloud

Cloud platforms for deployment

Prerequisites

Setting up the GCP

Automating the setup process

Summary

Chapter 2: Training NN for Prediction Using Regression

Building a regression model for prediction using an MLP deep neural network

Exploring the MNIST dataset

Intuition and preparation

Defining regression

Defining the project structure

Let's code the implementation!

Defining hyperparameters

Model definition

Building the training loop Generated by AI.

Notes:

Online resource; Title from title page (viewed October 31, 2018)

Part of the metadata in this record was created by AI, based on the text of the resource.

OCLC:

1089256248