Deep Learning for Natural Language Processing
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as¿recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You'll start...
Neural Networks and Deep Learning
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, s...
Neural Networks and Deep Learning
Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks.
Trends in Deep Learning Methodologies
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, ...
Hands-On Neural Networks with TensorFlow 2.0
A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0 Key Features Understand the basics of machine learning and discover the power of neural networks and deep learning Explore the structure of the TensorFlow fram...
Advanced Deep Learning with Python
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep lea...
Deep Learning with R for Beginners
Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the fundamentals of deep learning and neural networks Use R 3.5 and its libraries and APIs to build deep learning m...
Deep Learning Networks
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.
MATLAB Deep Learning
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundament...
Graph Neural Networks in Action
A hands-on guide to powerful graph-based deep learning models! Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. You will learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. Ideal for Python programmers, you will also explore common graph neural network architectures and cutting-edge libraries, all clearly illustrated with well-annotated Python code. The main features include: * Train and deploy a graph neural network * Generate node embeddings * Use GNNs at scale for very large datasets * Build a graph data pipeline * Create a graph data schema * Understand the taxonomy of GNNs * Manipulate graph data with NetworkX Go hands-on and explore relevant real-world projects as you dive into graph neural networks perfect for node prediction, link prediction, and graph
Hands-On Neural Network Programming with C#
Create and unleash the power of neural networks by implementing C# and .Net code Key Features Get a strong foundation of neural networks with access to various machine learning and deep learning libraries Real-world case studies illustrating vario...
Advanced Deep Learning with Keras
Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existence Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement deep neural networks, auto...
Python Deep Learning
Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniq...
Deep Learning for Search
Description Deep Learning for Search teaches readers how to leverage neural networks, NLP, and deep learning techniques to improve search performance. ¿ Deep Learning for Search teaches readers how to improve the effectiveness of your searc...
Deep Learning Neural Networks: Design And Case Studies
Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architect...
Neural Networks and Learning Machines
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. ¿ Neural Networks and Learning Machines, Third Edition is renow...
Hands-On Deep Learning Architectures with Python
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key Features Explore advanced deep learning architectures using various datasets and frameworks Implement deep architectures for neural network models such as...
Neural Network Learning
This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classificatio...
Neural Network Learning
This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classificatio...
Deep Learning with R
Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer visio...
Deep Learning for Biology
Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. * Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection * Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders * Use Python and interactive notebooks for hands-on learning *
Deep Learning for Medical Image Analysis
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts...
DEEP Learning Using Matlab. Neural Network APPLICATIONS
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In a simple case, there might be two sets of neurons: ones that receive an input signal and ones that send an output ...
Automated Deep Learning Using Neural Network Intelligence
Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides te...
Deep Learning from Scratch
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers...
Deep Learning from Scratch
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers...
Hands-On Q-Learning with Python
Leverage the power of reward-based training for your deep learning models with Python Key Features Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) Study practical deep reinforcement learning using Q-Ne...
Neural Network Projects with Python
Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Bui...
Deep Learning in Computational Mechanics
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of cu...
Deep Learning in Computational Mechanics
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of cu...
Deep Learning in Computational Mechanics
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of cu...
Advanced Forecasting with Python
Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Faceboo...
Hands-On Generative Adversarial Networks with PyTorch 1.x
Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key Features Implement GAN architectures to generate images, text, audio, 3D models, and more Understand how GANs work and beco...
Rust Programming Cookbook
Practical solutions to overcome challenges in creating console and web applications and working with systems-level and embedded code, network programming, deep neural networks, and much more. Key Features Work through recipes featuring advanced co...
Recurrent Neural Networks with Python Quick Start Guide
Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key Features Train and deplo...
Little Learner
A highly accessible, step-by-step introduction to deep learning, written in an engaging, question-and-answer style. The Little Learner introduces deep learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites The Little Schemer and The Little Typer, this kindred text explains the workings of deep neural networks by constructing them incrementally from first principles using little programs that build on one another. Starting from scratch, the reader is led through a complete implementation of a substantial application: a recognizer for noisy Morse code signals. Example-driven and highly accessible, The Little Learner covers all of the concepts necessary to develop an intuitive understanding of the workings of deep neural networks, including tensors, extended operators, gradient descent algorithms, artificial neurons, dense networks, convolutional networks, residual networks, and automatic
Deep Learning and Convolutional Neural Networks for Medical Image Computing
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the appl...
From Deep Learning to Rational Machines
This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks ("deep learning") to achieve artificial intelligence. Specifically, it links recent breakthroug...
Fundamentals of Deep Learning
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear...