Deep Learning Crash Course

□ I. THÔNG TIN SẢN PHẨM
□ Mã sản phẩm : STT1721
□ Nhà xuất bản : No Starch Press
□ Tác giả : Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, and Carlo Manzo
□ Ngôn ngữ : Tiếng Anh
□ ISBN : 9781718503922
□ Số trang : 680 trang
□ Hình thức : Bìa Mềm, RUỘT IN ĐEN TRẮNG, BÌA IN MẪU LASER GIẤY C300 CÓ CÁN
□ Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
□ Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và highlight thoải mái.
□ Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

□ II. MÔ TẢ SẢN PHẨM
□ 1.Mô tả sản phẩm đầy đủ
Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch, with no prior experience in deep learning or a PhD required. Designed specifically for programmers, scientists, and professional developers who may be new to the field, this book offers a practical, hands-on experience rather than an abstract exploration of mathematical theory. Using the industry-standard PyTorch framework along with real-world datasets, readers quickly progress from their first simple neural network to sophisticated architectures including convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project is designed to be run on accessible hardware or in the cloud, with fully annotated code available via GitHub to ensure a smooth learning curve.

Throughout the book, you will build and train models to solve a diverse array of complex problems. You will learn to classify and analyze images, sequences, and time series data; generate and transform high-dimensional data using autoencoders, generative adversarial networks (GANs), and diffusion models; and process natural language using recurrent neural networks and transformers. The curriculum also extends into specialized domains, teaching you how to model molecules and physical systems with graph neural networks, implement continuous improvement through reinforcement and active learning, and even predict chaotic systems using reservoir computing. 

What sets this guide apart is its focus on the 'how' and 'why' of model building, enabling you to move beyond simply using AI tools to actually creating them. It bridges the gap between basic coding and advanced AI implementation, providing the fluency and confidence needed to apply deep learning to ambitious, real-world problems. Whether you are looking to enhance your career or solve intricate scientific challenges, this comprehensive 680-page resource serves as a definitive roadmap to mastering the technology that defines modern artificial intelligence.

□ 2. Tác giả
Giovanni Volpe is a professor in the Physics Department of the University of Gothenburg, Sweden, where he leads the Soft Matter Lab. His diverse research interests encompass deep learning, brain connectivity, statistical mechanics, and soft matter. He has authored over 200 scientific articles and reviews and has co-authored multiple books. Beyond academia, Volpe has developed several influential open-source software packages, including DeepTrack for microscopy, Deeplay for deep learning, and BRAPH for brain connectivity analysis. He is also a recipient of the prestigious Göran Gustafsson Prize in Physics.