What you will learn

• Analyze and implement Recurrent Neural Networks (RNNs) to process sequence data and solve tasks like time series prediction and language modeling.

• Explore autoencoders for data compression, feature extraction, and anomaly detection, along with their applications in diverse fields.

• Develop and evaluate generative models, such as GANs, understanding their mathematical foundations and deployment challenges.

• Apply reinforcement learning techniques using Markov Chains and deep neural networks to tackle complex decision-making problems.