Deep Learning Essentials - ebook

Get to grips with the essentials of deep learning by leveraging the power of Python

Key Features

• Your one-stop solution to get started with the essentials of deep learning and neural network modeling
• Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more
• Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner

Book Description

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master.

This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more.

This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

What you will learn

• Get to grips with the core concepts of deep learning and neural networks
• Set up deep learning library such as TensorFlow
• Fine-tune your deep learning models for NLP and Computer Vision applications
• Unify different information sources, such as images, text, and speech through deep learning
• Optimize and fine-tune your deep learning models for better performance
• Train a deep reinforcement learning model that plays a game better than humans
• Learn how to make your models get the best out of your GPU or CPU

Who this book is for

Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Wei Di Wei Di is a data scientist with years of experience in machine learning and artificial intelligence. She is passionate about creating smart and scalable intelligent solutions that can impact millions of individuals and empower successful business. Currently, she works a staff data scientist in LinkedIn. She was previously associated with eBay Human Language Technology team and eBay Research Labs. Prior to that, she was with ancestry, working on large-scale data mining in the areas of record linkage. She received her PhD from Purdue University in 2011. Anurag Bhardwaj Anurag Bhardwaj currently leads data science efforts at Wiser Solutions, where he focuses on structuring large scale eCommerce inventory. He is particularly interested in using machine learning to solve problems on product category classification, product matching and various related problems in eCommerce. Previously, he worked on image understanding at eBay Research Labs. Anurag received his PhD and MS from the State University of New York at Buffalo and holds a BTech in computer engineering from the National Institute of Technology, Kurukshetra, India. Jianing Wei Jianing Wei is a senior software engineer at Google Research. He works in the area of computer vision and computational imaging. Prior to joining Google in 2013, Jianing worked at Sony US Research Center for four years in the area of 3D computer vision and image processing. Jianing obtained his Ph.D. in Electrical and Computer Engineering from Purdue University in 2010.