1. Preparing yourself for Mastering Keras Journey

a). Keras and TensorFlow Functionality

b). Setting Up the Environment and Teaching Methodology

2. Working with the Keras API Functionality

a). MLPs and Simple Data Analytics

b). CNNs and Basic Image Classification

c). RNNs and Sequential Data Analysis

d). AEs and Denoising Data

3. Developing and Implementing Deep Generative Models

a). What can the generative model do for me

b). Implementing GANs in Keras

c). Implementing VAEs in Keras

4. Advanced CNNs

a). Working with Inception Networks/Layers

b). Residual Networks/Layers

c). Dense Networks

d). Transfer Learning: Applying Advanced Architectures on Real Data

5. Object Detection

a). Introduction to Object Detection Networks

b). Fast RCNNs

c). Region Proposal Networks and Faster RCNNs

6. Deep Reinforcement learning

a). Reinforcement Learning Basics

b). (Epsilon) Q-Learning and Deep Q-Networks

c). Dealing with Instability (1): Experiential Replay

d). Dealing with Instability (2): Double Deep Q-Networks

e). Advantage Networks

f). Actor-Critic Networks

g). Asynchronous Agents and A3C Networks

h). Implementing a Deep Reinforcement Learning

i). Discussion of Real-World Applications

7. One-Shot and Deep Semi-Supervised Learning

a). Significance of One-Shot Learning Methods

b). Siamese Networks

c). Semi-Supervised Learning

d). Semi-supervised learning Using Ladder Networks

e). Semi-Supervised Learning Using GANs