Demystifying Deep Learning
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Demystifying Deep Learning
Author: Douglas J. Santry
language: en
Publisher: John Wiley & Sons
Release Date: 2023-12-06
DEMYSTIFYING DEEP LEARNING Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere—on the news, in think tanks, and occupies government policy makers all over the world —and ANNs often provide the backbone for AI. Relying on an informal and succinct approach, Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to natural language processing, image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning readers will also find: A volume that emphasizes the importance of classification Discussion of why ANN libraries, such as Tensor Flow and Pytorch, are written in C++ rather than Python Each chapter concludes with a “Projects” page to promote students experimenting with real code A supporting library of software to accompany the book at https://github.com/nom-de-guerre/RANT An approachable explanation of how generative AI, such as generative adversarial networks (GAN), really work. An accessible motivation and elucidation of how transformers, the basis of large language models (LLM) such as ChatGPT, work. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work. It is also a helpful text for advanced undergraduates to get a solid grounding on the topic.
Python Deep Learning Projects
Author: Matthew Lamons
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-10-31
Insightful projects to master deep learning and neural network architectures using Python and Keras Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook 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 learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho 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
Machine Intelligence
Artificial intelligence and machine learning are considered as hot technologies of this century. As these technologies move from research labs to enterprise data centers, the need for skilled professionals is continuously on the rise. This book is intended for IT and business professionals looking to gain proficiency in these technologies but are turned off by the complex mathematical equations. This book is also useful for students in the area of artificial intelligence and machine learning to gain a conceptual understanding of the algorithms and get an industry perspective. This book is an ideal place to start your journey as • Core concepts of machine learning algorithms are explained in plain English using illustrations, data tables and examples • Intuitive meaning of the mathematics behind popular machine learning algorithms explained • Covers classical machine learning, neural networks and deep learning algorithms At a time when the IT industry is focusing on reskilling its vast human resources, Machine intelligence is a very timely publication. It has a simple approach that builds up from basics, which would help software engineers and students looking to learn about the field as well as those who might have started off without the benefit of a structured introduction or sound basics. Highly recommended. - Siddhartha S, Founder and CEO of Intain - Financial technology startup Suresh has written a very accessible book for practitioners. The book has depth yet avoids excessive mathematics. The coverage of the subject is very good and has most of the concepts required for understanding machine learning if someone is looking for depth. For senior management, it will provide a good overview. It is well written. I highly recommend it. - Whee Teck ONG, CEO of Trusted Source and VP of Singapore Computer Society