Image Analysis And Text Classification Using Cnns In Pytorch
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Image Analysis and Text Classification Using CNNs in PyTorch
"This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the-art performance. The course is designed for the software engineer looking to get started with deep learning and for the AI researcher with TensorFlow or Theano experience who wants a smooth transition into PyTorch. Prerequisites include an understanding of algebra, basic calculus, and basic Python skills. Learners should download and install PyTorch before starting class."--Resource description page.
Learning PyTorch 2.0, Second Edition
"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch. The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference. Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Regardless of whether the objective is to fine-tune models or to deploy them on a large scale, this second edition is designed to ensure maximum efficiency and speed, with practical PyTorch scripting at the forefront of each chapter. Key Learnings Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries. Build feedforward, convolutional, and recurrent neural networks from scratch. Implement transformer models for modern natural language processing tasks. Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference. Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning. Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility. Optimize neural network architectures using torch.compile() for improved speed and efficiency. Utilize PyTorch's Quantization API to reduce model size and speed up inference. Setup custom layers and architectures for neural networks to tackle domain-specific problems. Monitor and log model performance in real-time using TorchServe's built-in tools and configurations. Table of Content Introduction To PyTorch 2.3 and CUDA 12 Getting Started with Tensors Building Neural Networks with PyTorch Training Neural Networks Advanced Neural Network Architectures Quantization and Model Optimization Migrating TensorFlow to PyTorch Deploying PyTorch Models with TorchServe
Grok 3 AI A Practical Guide to Building Intelligent Systems.
Ready to truly grok Artificial Intelligence? "Grok 3 AI" takes you beyond surface level understanding and empowers you to build your own intelligent systems. This comprehensive ebook covers the three core domains of AI Machine Learning, Deep Learning, and Natural Language Processing through a practical, hands-on approach. Starting with essential math and programming, you'll progress through core techniques like supervised and unsupervised learning, delve into the power of neural networks, and explore advanced topics like reinforcement learning and generative AI. Packed with real-world examples, hands-on projects, and ethical considerations, "Grok 3 AI" is your roadmap to mastering this transformative technology. Whether you're a beginner or looking to deepen your existing knowledge, this book will equip you with the skills and intuition to create your own AI solutions and navigate the exciting future of the field. artificial intelligence, AI, machine learning, deep learning, natural language processing, NLP, AI development, AI applications, learn AI, AI ebook