Modern Deep Learning Theory And Practice
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Modern Deep learning: Theory and Practice
Author: Dr. Tejinder Kaur
language: en
Publisher: Expert Notes, Publisher
Release Date: 2025-05-08
Welcome to From Modern Deep learning: Theory and Practice &, with the title “Modern deep learning: Theory and Practice”— a Deep learning is a type of machine learning where computers learn to recognize patterns and make decisions by processing data through layered structures called neural networks. Just as our brain processes information through layers of neurons, deep learning models use artificial neurons organized in multiple layers to learn complex patterns in data.A well-posed learning problem: “A computer program is asked to learn from experience E on some class of tasks T and a performance measure P if its performance on task T, as measured by P, improves with experience E.” (Tom Mitchell) Machine learning (ML) is an algorithm that learns from experience and the use of resulting information. It is seen as a piece of AI. ML computations assemble a model based on example information (data), called “training data or information”, to settle on predictions or choices. AI calculations are used in a variety of applications, for example, in medicine, email sifting, speech-recognition, and computer vision, where it is difficult or impractical to stimulate traditional models to perform the desired tasks. What makes this book special is the collaborative spirit behind it. It brings together the voices of experienced mentors, industry experts, and enterprising students who have dared to dream and do. Their shared experiences and wisdom create a rich and relatable resource that encourages learning through both inspiration and experience. To students and budding founders, this book is your roadmap to ideation, execution, and impact. Whether you're starting with a spark of curiosity or already pursuing a venture, the chapters ahead are filled with knowledge, motivation, and direction. We extend our heartfelt gratitude to everyone who contributed to this work — from the relentless energy of our student authors to the unwavering support of our mentors, peers, and academic leaders. This book stands as a testament to what can be achieved when creativity, collaboration, and commitment come together. As you turn each page, we hope it fuels your entrepreneurial spirit, challenges your thinking, and empowers you to transform your vision into lasting impact. Thank you for choosing this book — and here’s to your journey from idea to impact.
Exploring Machine Learning: Theory, Practice, and Innovations
Author: Dr. Vanitha Kakollu
language: en
Publisher: Academic Guru Publishing House
Release Date: 2024-12-23
“Exploring Machine Learning: Theory, Practice, and Innovations” is a thoughtfully curated resource that bridges the gap between foundational concepts and advanced methodologies in machine learning. With its systematic structure and practical orientation, the book caters to both beginners and experienced professionals in the field. The content is meticulously organised to align with the learner’s journey in understanding machine learning. The first chapter lays the groundwork by distinguishing human learning from machine learning, elucidating key concepts, and highlighting the potential and limitations of machine learning applications. A dedicated section on data preparation ensures readers grasp the significance of data preprocessing, quality enhancement, and exploration, setting the stage for successful modeling. The book’s core chapters address model selection, training, evaluation, and optimisation while introducing pivotal feature engineering techniques. Readers are guided through Bayes’ Theorem and its role in concept learning, followed by an exploration of supervised and unsupervised learning methods. Advanced algorithms, including decision trees, neural networks, and clustering techniques, are explained with clarity and context. Deep learning and neural networks are given special attention, with a focus on architecture, activation functions, and learning processes. The inclusion of contemporary topics such as ensemble learning and regularisation highlights the text’s relevance in modern machine learning landscapes. Practical insights are enriched by case studies across diverse applications, showcasing how theory translates into innovation. “Exploring Machine Learning” serves as a comprehensive, accessible, and indispensable guide for navigating the dynamic world of machine learning.
TensorFlow for Deep Learning
Author: Bharath Ramsundar
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-03-01
Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units