Practical Deep Learning with PyTorch PyTorch implementation for computer vision, NLP, audio, and language translation (Deepak Gowda)（Z-Library）
Deep learning is revolutionizing how we solve complex problems, and PyTorch has emerged as a leading framework for its ease of use and flexibility. This book is designed to bridge the gap between theory and practice, providing a hands-on approach to understanding deep learning with PyTorch. It covers fundamental and advanced topics, including object detection, NLP, GANs, and time series forecasting. The book begins with foundational deep learning concepts and guides you through setting up PyTorch. You will learn to manipulate tensors, load data, build models, and understand computer vision with multi-object detection using YOLO to enhance image recognition through transfer learning techniques. You will also analyze generative models with GANs for data augmentation and venture into audio processing with text-to-speech and speech-to-text using TorchAudio. Learn NLP tasks like text classification, summarization, sentiment analysis, and question answering with pre-trained models like BERT. Finally, learn to tackle time series forecasting using RNNs, LSTMs, CNNs, and transformers. By the end of this book, you will be equipped with the practical skills and knowledge to confidently build and deploy deep learning solutions across various domains, helping you innovate in the ever-evolving field of artificial intelligence. What you will learn ● Implement deep learning models for image, text, and speech tasks. ● Build and optimize AI workflows using PyTorch efficiently. ● Apply transfer learning techniques for improved model performance. ● Develop GANs for generating high-quality synthetic data. ● Use NLP techniques for language processing and sentiment analysis. ● Forecast time series data using LSTMs and deep learning models.
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