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"--
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| Format: | Book |
| Language: | English |
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San Francisco :
No Starch Press,
[2021]
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Table of Contents:
- Part I: Foundational ideas
- An overview of machine learning
- Essential statistics
- Measuring performance
- Bayes' rule
- Curves and surfaces
- Information theory
- Part II: Basic machine learning
- Classification
- Training and testing
- Overfitting and underfitting
- Data preparation
- Classifiers
- Ensembles
- Part III: Deep learning basics
- Neural networks
- Backpropagation
- Optimizers
- Part IV: Beyond the basics
- Convolutional neural networks
- Convnets in practice
- Autoencoders
- Recurrent neural networks
- Attention and transformers
- Reinforcement learning
- Generative adversarial networks
- Creative applications.