Deep learning: a visual approach
A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI.
Read More ↓Title Statements
- Full title :Deep learning : a visual approach / Andrew Glassner
Summary
A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI.
Read More ↓Edition Statements
- Publication Date:2021
Publication Statements
- Publisher(s):No Starch Press, Inc
- Place of Publication:San Francisco, CA
- Language:eng
- Publication Date:2021
Notes
FOUNDATIONAL IDEAS 1) An overview of machine learning, 2) Essential statistics, 3) Measuring performance, 4) Bayes' rule, 5) Curves and surfaces, 6) Information theory, BASIC MACHINE LEARNING 7) Classification, 8) Training and testing, 9) Overfitting and underfitting, 10) Data preparation, 11) Classifiers, 12) Ensembles, DEEP LEARNING BASICS 13) Neural networks, 14) Backpropagation, 15) Optimizers, BEYOND THE BASICS 16) Convolutional neural networks, 17) Convnets in practice, 18) Autoencoders, 19) Recurrent neural networks, 20) Attention and transformers, 21) Reinforcement learning, 22) Generative adversarial networks, 23) Creative applications
The Physical Object
- Description:736 pages : : color illustrations ; : 24 cm
Edition Identifiers
- ISBN:9781718500723, 1718500726
- oclc:on1264250779
- LC Classification:Q325.5
- Dewey Decimal:006.3 1
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