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AI projects in PyTorchhands-on proje...

Chaubal, Siddhesh Prashant.

AI projects in PyTorchhands-on projects in vision, text, and generative models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	AI projects in PyTorchby Siddhesh Prashant Chaubal.
其他題名:	hands-on projects in vision, text, and generative models /
作者:	Chaubal, Siddhesh Prashant.
出版者:	Berkeley, CA :Apress :2025.
面頁冊數:	xxi, 346 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence.
電子資源:	https://doi.org/10.1007/979-8-8688-2117-2
ISBN:	9798868821172$q(electronic bk.)

AI projects in PyTorchhands-on projects in vision, text, and generative models /
Chaubal, Siddhesh Prashant.

AI projects in PyTorchhands-on projects in vision, text, and generative models /[electronic resource] :by Siddhesh Prashant Chaubal. - Berkeley, CA :Apress :2025. - xxi, 346 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Machine Learning -- Chapter 2: Tensors in PyTorch -- Chapter 3: Image Classification using Convolutional Neural Networks -- Chapter 4: Introduction to Natural Language Processing: Building a Text Classifier -- Chapter 5: Practical Natural Language Processing with Hugging Face -- Chapter 6: Building a Language Model for Storytelling -- Chapter 7: Audio Classification with PyTorch -- Chapter 8: Recommender Systems with PyTorch -- Chapter 9: Image Captioning.

Dive into computer vision, natural language processing, and recommender systems by building end-to-end projects in PyTorch - one of the most widely used deep learning frameworks among researchers and engineers worldwide. This book takes you from the fundamentals to complete, hands-on projects, giving you the confidence to start creating your own AI solutions. The book begins with a chapter on the fundamentals of machine learning, laying the groundwork by introducing key aspects of an ML project such as data preprocessing, feature engineering, model training, and evaluation, along with essential concepts like overfitting and underfitting. The following chapter, "Tensors in PyTorch," explores data handling in PyTorch -- from basic tensor operations to advanced gradient computations -- providing a deeper understanding of data transformations. With the foundations in place, the book moves on to hands-on projects. Chapter 3 introduces you to the world of computer vision, where you will build an image classifier using convolutional neural networks. The next three chapters immerse you in natural language processing: beginning with text classification (Chapter 4), tackling a range of NLP tasks with Hugging Face (Chapter 5), and culminating in the creation of a storytelling language model (Chapter 6). The focus then shifts to other key AI domains - you will tackle an audio classification task (Chapter 7), build a recommender system in PyTorch (Chapter 8), and finish with a multi-modal project that combines computer vision and natural language processing to build an image captioning system (Chapter 9). Whether you're a software engineer looking to break into the world of AI or a beginner with basic Python skills, "AI Projects with PyTorch" offers practical guidance and hands-on experience to start building your own AI applications with confidence. What you will learn: Master the core principles of machine learning and gain confidence with the typical PyTorch project workflow. Build a solid understanding of data handling in PyTorch - including tensors, datasets, data loaders, and gradient computations. Build natural language processing models, from text classification to storytelling language models. Work on multiple natural language processing tasks with Hugging Face libraries. Combine vision and language to build an image captioning system.

ISBN: 9798868821172$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-2117-2doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 005.133

AI projects in PyTorchhands-on projects in vision, text, and generative models /
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