07. Deep learning foundations

Chapter 7. Deep Learning Foundations

• 7.1. Biological and artifical neurons
• 7.2. ANN and its layers
• 7.3. Exploring activation functions
• 7.4. Forward and backward propagation in ANN
  • 7.4.1. Forward propgation in ANN
  • 7.4.2. Backward propagation in ANN
• 7.5. Building neural network from scratch
• 7.6. Recurrent neural networks
  • 7.6.1. Difference between feedforward networks and RNN
  • 7.6.2. Forward propagation in RNN
  • 7.6.3. Backpropagation through time
• 7.7. LSTM-RNN
  • 7.7.1. Understanding the LSTM cells
• 7.8. Convolutional neural networks
  • 7.8.1. Understanding every layers in CNN
  • 7.8.2. Architecture of CNN
• 7.9. Generative adversarial networks
  • 7.9.1. Understanding generator
  • 7.9.2. Understanding discriminator
  • 7.9.3. Architecture of GAN
  • 9.9.4. Understanding the loss function