Tag: DNN

Price: $74.99 – $61.09
(as of Dec 24,2024 01:19:52 UTC – Details)

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

Deep learning has revolutionized the field of artificial intelligence and machine learning in recent years, with its ability to solve complex problems and make predictions with remarkable accuracy. Understanding how deep learning works and how to build machine learning systems using popular frameworks like PyTorch and TensorFlow is essential for anyone looking to work in this rapidly growing field.

In this post, we will explore the fundamentals of deep learning, including neural networks such as Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Graph Neural Networks (GNN), Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Generative Adversarial Networks (GAN). We will also delve into the exciting world of Natural Language Processing (NLP) and how deep learning is used to analyze and understand human language.

PyTorch and TensorFlow are two of the most popular deep learning frameworks used by researchers and developers to build powerful machine learning models. These frameworks provide a wide range of tools and libraries for building and training neural networks, as well as pre-trained models that can be fine-tuned for specific tasks.

Whether you are a beginner looking to learn the basics of deep learning or an experienced data scientist looking to expand your skills, understanding how to build machine learning systems with PyTorch and TensorFlow is essential. Stay tuned for more in-depth discussions on each of these topics and how you can apply them to real-world problems in the field of artificial intelligence.
#Understanding #Deep #Learning #Building #Machine #Learning #Systems #PyTorch #TensorFlow #Neural #Networks #CNN #DNN #GNN #RNN #ANN #LSTM #GAN #Natural #Language #Processing #NLP