Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

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This illustrated and color-coded book will teach you how to use the full potential of deep learning, guiding you from foundational principles to advanced applications with clarity and precision.

Dive into the core of deep learning and machine learning with this hands-on guide that provides a solid foundation for anyone from data scientists to AI enthusiasts. This book, meticulously structured for clarity and depth, unravels the mysteries of neural networks, large language models (LLMs), and generative AI. With clear explanations and a focus on practical applications, it’s your ultimate resource for mastering machine learning with Python.

What You’ll Learn Inside:

Foundations of Machine Learning and Deep Learning
Discover why machines learn the way they do and understand the algorithms that power modern machine learning models. Explore the evolution of AI, from basic network structures to sophisticated LLMs and RAG (retrieval-augmented generation) techniques.

Practical Model Building with PyTorch and TensorFlow
Get hands-on experience with Python programming, PyTorch, and TensorFlow—the most powerful tools in machine learning system design. Learn to build and optimize models that solve real-world problems, from NLP (Natural Language Processing) with Transformers to generative deep learning for image synthesis.

Advanced Techniques for Model Optimization and System Design
Master the art of hyperparameter tuning, data preprocessing, and system design for deep learning. This book also introduces GitHub and version control for efficient model management, essential for any data-driven project.

Real-World Applications
Whether you’re interested in algorithmic trading, hands-on machine learning with scikit-learn, Keras, and TensorFlow, or understanding deep learning for natural language processing, this book covers it all. See how deep learning with PyTorch and machine learning with Python apply across fields, from data science to cutting-edge generative AI.

Perfect for readers who want to build expertise in machine learning engineering, this guide also delves into the math behind neural networks, numpy, and Python pandas—everything you need to build robust learning systems from scratch. Whether you’re a seasoned programmer or new to AI, Understanding Deep Learning will equip you with the tools and knowledge to make an impact in the world of AI.

Ready to take the next step? Get your copy today and start building the future of machine learning with Python and deep learning technologies!

From the Publisher

Master the Fundamentals of Deep Learning with Ease

From Basics to Advanced Techniques, All in One Place

This book is your complete guide to deep learning. Dive into the concepts that power artificial intelligence, neural networks, and modern machine learning systems. Packed with clear, color-coded illustrations and hands-on exercises, this resource is designed to make complex ideas accessible and memorable.

Comprehensive and Practical

Whether you’re a student, professional, or tech enthusiast, this book bridges the gap between theory and real-world applications. Learn to implement cutting-edge models with frameworks like TensorFlow and PyTorch, develop a strong understanding of neural networks, and gain the skills to work with large datasets.

Why This Book Stands Out

Illustrated and Color-Coded: Complex topics made simple with diagrams and color-coded snippets.Hands-On Approach: Practical exercises with TensorFlow and PyTorch.For All Levels: Ideal for beginners, advanced learners, and professionals.Theory Meets Practice: Covers foundational concepts and advanced models.Expertly Written: Clear and comprehensive, created by industry professionals.

Who Should Read This Book? Data Scientists and AI/ML Engineers Software Developers Researchers and Academics Tech Enthusiasts Professionals seeking AI integration insights Job Seekers

Gain Expertise in Model Architectures

Explore advanced network architectures that drive modern AI applications

In-depth Analysis of Neural Network Layers Explore neural network layers, from fully connected to specialized ones like convolutional and recurrent. Learn how each layer contributes to feature extraction, sequence modeling, and data compression for various AI applications.Optimization and Regularization Techniques Master optimization methods like SGD, Adam, and RMSprop for effective loss minimization. Understand regularization strategies such as Dropout, Batch Normalization, and L2 Regularization to control overfitting and stabilize training.Building and Training Custom Models with TensorFlow and PyTorch Gain expertise in constructing and training custom models in TensorFlow and PyTorch. Define architectures, customize activation functions, and integrate complex layers to create models suited for specific industry needs.

Fine-Tune for Maximum Efficiency

Advanced techniques for selecting hyperparameters that maximize your model’s accuracy and speed

Understanding the Impact of Hyperparameters on Model Performance Explore hyperparameters like learning rate, batch size, and epochs. See how fine-tuning affects convergence, stability, and model accuracy on test data.Techniques for Systematic Hyperparameter Tuning Learn methods like Grid Search, Random Search, and Bayesian Optimization to tune hyperparameters. Understand how each approach suits different models, improving resource efficiency and iteration speed.Automated Hyperparameter Optimization with Optuna and Hyperopt Automate hyperparameter tuning with Optuna and Hyperopt. Use these tools to optimize models for peak performance without manual intervention.

Adapt Pre-Trained Models for Custom Applications

Harness the power of transfer learning to adapt large models for your specific needs

Customizing Pre-Trained Models for Specialized Tasks Adapt models like ResNet, VGG, and BERT for niche applications. Explore layer customization by freezing lower layers and modifying upper layers for feature extraction and tuning to specific tasks.Fine-Tuning Techniques for Optimal Performance Master fine-tuning techniques like unfreezing layers, adjusting learning rates, and recalibrating batch sizes to maximize performance, especially in limited data settings.Managing Transfer Learning Challenges: Domain Shift & Overfitting Gain strategies for domain adaptation and managing overfitting in transfer learning. Address distribution shifts, apply data augmentation, and perform domain-specific tuning for robust adaptation.

Deep Learning with Detailed, Color-Coded Visuals

Deep learning with clear, color-coded illustrations that simplify complex concepts. From neural network architectures to data processing techniques, every page is packed with visuals to support your learning. Code snippets are thoughtfully formatted, making it easy to follow along and implement real-world applications. Perfect for visual learners and professionals seeking practical insights.

ASIN ‏ : ‎ B0DMP1XC3P
Publisher ‏ : ‎ Independently published (November 10, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 397 pages
ISBN-13 ‏ : ‎ 979-8346172659
Item Weight ‏ : ‎ 1.89 pounds
Dimensions ‏ : ‎ 7 x 0.9 x 10 inches