Deep Learning Overview

This section explores neural networks and advanced deep learning architectures, covering foundational models, training techniques, frameworks, and applications.

Key Topics Covered:

• Foundational Models: Core neural network architectures.
  • Neural Network Basics
  • CNNs (Convolutional Neural Networks)
  • RNNs (Recurrent Neural Networks)
  • LSTMs (Long Short-Term Memory)
  • Transformers
  • GANs & VAEs
• Advanced Techniques: Methods for improving model training and performance.
  • Data Preparation: Augmentation, Normalization.
  • Training Techniques: Tuning, Optimization, Regularization, Transfer Learning.
  • Model Evaluation: Metrics, Cross-Validation, ROC Curves.
  • Special Topics: Attention, RAG, PEFT, Prompt Engineering, Agents.
• Frameworks and Libraries: Popular tools like PyTorch, TensorFlow, Keras, JAX.
• Practical Applications: Deployment strategies (MLOps, Serving) and industry use cases.

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