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About Background Theory of Deep Learning: 2.1 Origin
This chapter introduces the basic concepts of deep learning including neural network building blocks and common network topologies like recurrent and convolutional neural networks. It discusses the origin of deep learning from artificial intelligence and biological neurons. It also covers the differences between supervised and unsupervised learning as well as classification and regression problems. Finally, it provides an overview of common activation functions used in neural networks like ReLU, sigmoid, tanh, and softmax.
Detailed Information
| Author: | ['takis'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 18 |
| Language: | English |
| Format: | |
| Price: | FREE |
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