Deep learning
"Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge...
Đã lưu trong:
| Tác giả chính: | |
|---|---|
| Định dạng: | Sách |
| Ngôn ngữ: | Undetermined |
| Được phát hành: |
Cambridge, Massachusetts
The MIT Press
2016
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| Những chủ đề: | |
| Các nhãn: |
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| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Cần Thơ |
|---|
| LEADER | 02313nam a2200229Ia 4500 | ||
|---|---|---|---|
| 001 | CTU_223894 | ||
| 008 | 210402s9999 xx 000 0 und d | ||
| 020 | |c 3197000 | ||
| 082 | |a 006.31 | ||
| 082 | |b G651 | ||
| 100 | |a Goodfellow, Ian | ||
| 245 | 0 | |a Deep learning | |
| 245 | 0 | |c Ian Goodfellow, Yoshua Bengio and Aaron Courville. | |
| 260 | |a Cambridge, Massachusetts | ||
| 260 | |b The MIT Press | ||
| 260 | |c 2016 | ||
| 520 | |a "Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors" | ||
| 526 | |a Mạng nơ-ron nhân tạo | ||
| 526 | |b CT384 | ||
| 650 | |a Machine learning,Máy học | ||
| 910 | |b vdbang | ||
| 980 | |a Trung tâm Học liệu Trường Đại học Cần Thơ | ||