Deep Learning for Beginners

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Deep Learning for Beginners

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內容描述

Key Features

Understand the fundamental machine learning concepts useful in deep learning
Learn the underlying mathematical and statistical concepts as you implement smart deep learning models from scratch
Explore easy-to-understand examples and use cases that will help you build a solid foundation in DL

Book Description
With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning (DL). This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and already have the basic mathematical and programming knowledge required to get started.
The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples and even build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book.
By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.
What you will learn

Implement recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in image classification and NLP
Understand the mathematical terminology associated with DL algorithms
Explore the role of convolutional neural networks (CNNs) in computer vision and signal processing
Understand the ethical implications of DL modeling
Code a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent space
Implement visualization techniques to compare deep and variational autoencoders

Who This Book Is For
This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.

作者介紹

Pablo Rivas
Dr. Pablo Rivas is an Assistant Professor of Computer Science at Marist College in Poughkeepsie, New York. He worked in the industry for a decade as a software engineer before becoming an academic. He is a Senior Member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center, and at Baylor University performing post-doctoral research and teaching. He considers himself an ally of women in technology, a deep learning evangelist, machine learning ethicist, and is a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning courses with applications in natural language processing and computer vision. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics; he recently became a certified online instructor; and he is also a machine learning consultant of the New York State Cloud Computing and Analytics Center. Prof. Rivas prefers Vim over Emacs and spaces over tabs.