D.O - Neural Networks 1

For those who want to build controlled, reproducible AI systems entirely within your own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on ext……続きを見る

***Complex, Hypercomplex, and Fuzzy-Valued Neural Networks***are extensions of classical neural networks to higher dimensions. In recent decades, this theory has emerged as a forefront in neural net……続きを見る

This book explores the intersection of quantum computing and network science. It bridges the theoretical foundations of quantum walk algorithms with their applications in the structural exploration ……続きを見る

Are you ready to fine-tune your own LLMs? This book is a practical guide to fine-tuning Large Language Models (LLMs), combining high-level concepts with step-by-step instructions to train these powe……続きを見る

This book addresses the challenges posed by adopting and developing new AI technologies and how they impact people. Ethics, the scope, and the impact of technology on people are vital. The book star……続きを見る

Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features • Implement machine learning techniques and algorithms in graph dat……続きを見る

**Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own** In the world's top research lab……続きを見る

This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the k……続きを見る

Un libro para divertirse y convertirse en super expertos en el Cerebro descubriendo las respuestas a las siguientes preguntas: ¿Cómo puedo pensar? ¿Cómo funciona el cerebro? ¿Quién manda : yo o mi c……続きを見る

Revised for PyTorch 2.x! Why this book? Are you looking for a book where you can learn about deep learning and PyTorch without having to spend hours deciphering cryptic text and code? A technical bo……続きを見る

Revised for PyTorch 2.x! Why this book? Are you looking for a book where you can learn about deep learning and PyTorch without having to spend hours deciphering cryptic text and code? A technical bo……続きを見る

Revised for PyTorch 2.x! Why this book? Are you looking for a book where you can learn about deep learning and PyTorch without having to spend hours deciphering cryptic text and code? A technical bo……続きを見る

The objective of the series has always been to provide a forum in which leading contributors to an area can write about significant bodies of research in which they are involved. The operating proce……続きを見る

Build, scale, and deploy deep neural network models using the star libraries in Python About This Book • Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras •……続きを見る

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a ba……続きを見る

From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure. It is ……続きを見る

Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGL Free with……続きを見る

Un libro para divertirse y convertirse en super expertos en el Cerebro descubriendo las respuestas a las siguientes preguntas: ¿Cómo puedo pensar? ¿Cómo funciona el cerebro? ¿Quién manda : yo o mi c……続きを見る

A rogue planet is speeding its way towards the inner solar system, destined to become a hot Jupiter. It is a scenario that has played out countless times throughout the galaxy, and the result is alw……続きを見る

Demystify NLP, the secret sauce behind chatbots like Alexa and Siri. This book cuts through the jargon and empowers you to harness the power of Python to build your own intelligent assistants, capab……続きを見る

Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, ……続きを見る

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 Artific……続きを見る

Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with ease Key Features Understand 3D data processing w……続きを見る

Amidst the rampant use of algorithmization enabled by AI, the common theme of AI systems is the human factor. Humans play an essential role in designing, developing, and operationalizing AI systems.……続きを見る

The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary ad……続きを見る

An insightful investigation into the mechanisms underlying the predictive functions of neural networksーand their ability to chart a new path for AI. Prediction is a cognitive advantage like few oth……続きを見る

The Internet of Edges is a new paradigm whose objective is to keep data and processing close to the user. This book presents three different levels of Edge networking: MEC (Multi-access Edge Computi……続きを見る

Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-……続きを見る

Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key Features Learn how you can speed up the deep learning process with one-shot learning Use Python and PyTor……続きを見る

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and h……続きを見る