Book Content
chapters • 9h4m total length
1. The Building Blocks of Deep Learning
2. Using Deep Learning To Solve Regression Problems
3. Monitoring Network Training Using Tensor Board
4. Using Deep Learning To Solve Binary Classification Problems
5. Using Keras To Solve MultiClass Classification Problems
6. HyperParameter Optimization
7. Training a CNN From Scratch
8. Transfer Learning with Pretrained CNNs
9. Training an RNN from scratch
10. Training LSTMs with Word Embeddings From Scratch
11. Training Seq2Seq Models
12. Using Deep Reinforcement Learning
13. Deep Convolutional Generative Adversarial Networks
