Learn advanced deep learning concepts through this comprehensive video tutorial covering transformer architectures, BERT and GPT models, prompt engineering techniques, reinforcement learning fundamentals, and neural network optimization strategies including regularization and RNN architectures for various applications.

Syllabus

Embeddings
Transformer Architecture
Encoders and BERT Models
Generative AI: Decoders and GPT Models
Prompt Engineering Part 1
Prompt Engineering Part 2
LLM Limitations and Hallucinations
Introduction to Reinforcement Learning
Deep Reinforcement Learning
Applications of Reinforcement Learning
Training Challenges in Deep Learning
Regularization in Neural Networks
Understanding RNNs
RNN Architectures for Different Use Cases