Tag: GNN

Price: $2.99
(as of Nov 24,2024 06:40:19 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 ‏ : ‎ B0DLLM3W8T
Publication date ‏ : ‎ October 30, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 11416 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 527 pages

Deep learning has revolutionized the field of artificial intelligence and machine learning, allowing machines to learn complex patterns and make decisions in a way that mimics the human brain. In this post, we will delve into the world of deep learning and explore how to build machine learning systems using popular frameworks like PyTorch and TensorFlow.

At the core of deep learning are neural networks, which are computational models inspired by the structure and function of the human brain. There are various types of neural networks, including 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) networks, and Generative Adversarial Networks (GAN).

CNNs are commonly used for image recognition tasks, DNNs for general-purpose machine learning tasks, GNNs for graph data, RNNs for sequential data, ANNs for general learning tasks, LSTMs for sequence prediction, and GANs for generating new data samples.

In addition to neural networks, deep learning is also widely used in Natural Language Processing (NLP), which focuses on the interaction between computers and human language. NLP tasks include sentiment analysis, machine translation, text generation, and more. PyTorch and TensorFlow provide powerful tools and libraries for building deep learning models for NLP tasks.

By understanding the fundamentals of deep learning and mastering frameworks like PyTorch and TensorFlow, you can unlock the potential of building intelligent machine learning systems that can learn from data and make informed decisions. Stay tuned for more in-depth articles on each type of neural network and NLP tasks in the upcoming posts.
#Understanding #Deep #Learning #Building #Machine #Learning #Systems #PyTorch #TensorFlow #Neural #Networks #CNN #DNN #GNN #RNN #ANN #LSTM #GAN #Natural #Language #Processing #NLP