Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks

Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks

By 作者: Ahmed Menshawy

ISBN-10 书号: 1788399900
ISBN-13 书号: 9781788399906
Release Finelybook 出版日期: 2018-02-28
pages 页数: 450

$39.99


Book Description to Finelybook sorting

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic.
This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book.
By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.
Contents
1: DATA SCIENCE – A BIRDS’ EYE VIEW
2: DATA MODELING IN ACTION – THE TITANIC EXAMPLE
3: FEATURE ENGINEERING AND MODEL COMPLEXITY – THE TITANIC EXAMPLE REVISITED
4: GET UP AND RUNNING WITH TENSORFLOW
5: TENSORFLOW IN ACTION – SOME BASIC EXAMPLES
6: DEEP FEED-FORWARD NEURAL NETWORKS – IMPLEMENTING DIGIT CLASSIFICATION
7: INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS
8: OBJECT DETECTION – CIFAR-10 EXAMPLE
9: OBJECT DETECTION – TRANSFER LEARNING WITH CNNS
10: RECURRENT-TYPE NEURAL NETWORKS – LANGUAGE MODELING
11: REPRESENTATION LEARNING – IMPLEMENTING WORD EMBEDDINGS
12: NEURAL SENTIMENT ANALYSIS
13: AUTOENCODERS – FEATURE EXTRACTION AND DENOISING
14: GENERATIVE ADVERSARIAL NETWORKS
15: FACE GENERATION AND HANDLING MISSING LABELS
16: IMPLEMENTING FISH RECOGNITION
What You Will Learn
Understand the fundamentals of deep learning and how it is different from machine learning
Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning
Increase the predictive power of your model using feature engineering
Understand the basics of deep learning by solving a digit classification problem of MNIST
Demonstrate face generation based on the CelebA database, a promising application of generative models
Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation
Authors
Ahmed Menshawy
Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of ML and NLP. He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as ML, Image Processing, and so on. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. He was the main ML specialist at the Industrial research and development lab at IST Networks, based in Egypt.

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Deep Learning By Example
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