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Tag: CNN
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)
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 inchesCustomers say
Customers find the book’s comprehensive coverage of deep learning concepts and clear explanations make it an invaluable resource for both novices and experienced practitioners. The vibrant illustrations and full-color diagrams make complex topics easier to grasp. Readers describe the book as well-worth their time and money, providing practical guidance and a valuable resource for anyone interested in building AI models.
AI-generated from the text of customer reviews
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
Deep Learning Crash Course for Beginners with Python: Theory and Applications of Artificial Neural Networks, CNN, RNN, LSTM and Autoencoders using … Learning & Data Science for Beginners)
Price:$24.99– $20.50
(as of Dec 17,2024 02:12:54 UTC – Details)
Welcome to our Deep Learning Crash Course for Beginners with Python! In this post, we will cover the theory and applications of artificial neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM) networks, and autoencoders using TensorFlow, Keras, and other popular libraries.Deep learning is a subset of machine learning that focuses on learning representations of data through multiple layers of neural networks. It has revolutionized many industries, from healthcare to finance to autonomous driving, and is a powerful tool for pattern recognition, natural language processing, computer vision, and many other tasks.
In this crash course, we will start by introducing the basics of neural networks and how they work. We will then dive into more advanced topics such as CNNs for image classification, RNNs for sequential data, LSTMs for time series analysis, and autoencoders for unsupervised learning.
Throughout the course, we will use Python as our programming language of choice, along with popular libraries like TensorFlow and Keras. We will also cover the basics of data preprocessing, model training, evaluation, and deployment.
Whether you are a complete beginner or have some experience with machine learning, this crash course will provide you with a solid foundation in deep learning and data science. Stay tuned for our upcoming posts, where we will dive deeper into specific topics and applications of deep learning. Happy learning!
#Deep #Learning #Crash #Beginners #Python #Theory #Applications #Artificial #Neural #Networks #CNN #RNN #LSTM #Autoencoders #Learning #Data #Science #Beginners
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)
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 pagesCustomers say
Customers find the book comprehensive, bridging theory and practice. They say it provides a balanced introduction to both PyTorch and TensorFlow. Readers also appreciate the vibrant illustrations and well-designed book.
AI-generated from the text of customer reviews
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
