Publisher: Packt Publishing
Cuantum Technologies LLC
Dive into the world of deep learning with this comprehensive guide that bridges theory and practice. From foundational neural networks to advanced architectures like CNNs, RNNs, and Transformers, this book equips you with the tools to build, train, and optimize AI models using TensorFlow, Keras, and PyTorch. Clear explanations of key concepts such as gradient descent, loss functions, and backpropagation are combined with hands-on exercises to ensure practical understanding.Explore cutting-edge AI frameworks, including generative adversarial networks (GANs) and autoencoders, while mastering real-world applications like image classification, text generation, and natural language processing. Detailed chapters cover transfer learning, fine-tuning pretrained models, and deployment strategies for cloud and edge computing. Practical exercises and projects further solidify your skills as you implement AI solutions for diverse challenges.Whether you're deploying AI models on cloud platforms like AWS or optimizing them for edge devices with TensorFlow Lite, this book provides step-by-step guidance. Designed for developers, AI enthusiasts, and data scientists, it balances theoretical depth with actionable insights, making it the ultimate resource for mastering modern deep learning frameworks and advancing your career in AI
Cher Simon
Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that’ll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you’ll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis.This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you’ll get equipped with XAI and anomaly detection knowledge that’ll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you’ll learn how to quantify and assess their explainability.By the end of this deep learning book, you’ll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.
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.
Wei Di, Jianing Wei, Anurag Bhardwaj
Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. 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 CNN, RNN, 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 using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets.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.
Dr. Pablo Rivas
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. 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 you 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 learn how to 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.
Rajalingappaa Shanmugamani
Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Upendra Kumar Devisetty
Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you’ll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets.By the end of this book, you’ll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.
Karthiek Reddy Bokka, Shubhangi Hora , Tanuj...
Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts by highlighting the basic building blocks of the natural language processing domain.The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.By the end of this book, you will not only have sound knowledge of natural language processing, but also be able to select the best text preprocessing and neural network models to solve a number of NLP issues.