Generative Adversarial Networks (GANs): Introduction and Example

The initial lectures series on this topic can find in the below links: Introduction to Anomaly Detection https://www.youtube.com/watch?v=IFHX4HUAo1w&list=PLpW3QouFxOnM6YWVOrcUBaQiy8EWi05pi&index=37

How to implement an anomaly detector (1/2) https://www.youtube.com/watch?v=DN0H2Qz3Rxg How to implement an anomaly detector (2/2) https://www.youtube.com/watch?v=nYZuQg5K22Y How to deploy a real-time anomaly detector https://www.youtube.com/watch?v=LnPrT-IkzNw Introduction to Time Series Forecasting https://www.youtube.com/watch?v=G7_uNCOFEzE Stateful vs. Stateless LSTMs https://www.youtube.com/watch?v=R7CwkhZYJdU Batch Size! which batch size is to choose? https://www.youtube.com/watch?v=wfyErdPsZPI Number of Time Steps, Epochs, Training and Validation https://www.youtube.com/watch?v=tsprdX9RkRg Batch size and Trainin Set Size https://www.youtube.com/watch?v=5kLLKhNJlEY Input and Output Data Construction https://www.youtube.com/watch?v=zCHrQRlu688 Designing the LSTM network in Keras https://www.youtube.com/watch?v=Y3ApYArvBr8 Anatomy of a LSTM Node https://www.youtube.com/watch?v=WrA3LlKAbf0 Number of Parameters:How LSTM Parmeter Num is Computed. https://www.youtube.com/watch?v=oXNBR0U1A54 Training and loading a saved model. https://www.youtube.com/watch?v=jKjm1cX-mtM ... https://www.youtube.com/watch?v=UL5O7la_UJI