Deep Learning | Coursera

Deep Learning Specialization Overview

• Program Goal: A foundational program designed to teach the capabilities, challenges, and consequences of deep learning to prepare students for AI development.
• Core Technologies: Python and TensorFlow.
• Neural Network Architectures Covered:
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • LSTMs and GRUs
  • Transformers
• Optimization & Training Strategies: Dropout, BatchNorm, Xavier/He initialization, bias/variance analysis, and optimization algorithms.
• Practical Applications:
  • Speech recognition and music synthesis
  • Chatbots and machine translation
  • Natural Language Processing (NLP)
  • Visual detection and recognition
  • Neural style transfer
  • Named Entity Recognition and Question Answering
• Key Learning Outcomes:
  • Building and training deep neural networks with vectorized implementations.
  • Applying end-to-end, transfer, and multi-task learning.
  • Working with HuggingFace tokenizers and transformers.
  • Developing best practices for test sets and error reduction in ML systems.
• Career Support: Includes career advice from experts in both industry and academia.