Hi,
these are the topics i would need to learn, please check thoroughly
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NO EDUCATIONAL CONSULTANCIES MUST APPROACH

The
course focuses on both theory and applications. I will need to learn the quantitative models and metrics
that drive the success of popular Generative AI applications such as Recurrent Neural Networks (RNN), Long
Short-term Memory Model (LSTM), Attention Mechanism, Transformer Models, Convolutional Neural
Networks (CNN), Generative Adversarial Networks (GAN) and Reinforcement Learning and Deep Q Learning,
etc. will also develop hands-on Generative AI projects using popular deep learning cloud
computing tools that include TensorFlow and PyTorch. Lectures and coding lab sessions are enriched with
case studies and examples ranging from trading, image synthesis, text translation and generation, etc. to
showcase the application of generative AI in various fields such as natural language processing, trading,
finance, entertainments, etc. The course includes eight chapters: Lecture 1 How Generative AI differs from
traditional machine learning methods. Lecture 2 Recurrent Neural Networks (RNN). Lecture 3 Long Short-
term Memory Model (LSTM). Lecture 4 Attention Mechanisms. Lecture 5 Transformer and Large Language
Model. Lecture 6 Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN).
Lecture 7 Reinforcement Learning and Deep Q Learning. Lecture 8 Principles of Responsible AI.