Deep Learning-Syllabus
Module 1: Introduction to Deep Learning
- Overview of Machine Learning and Artificial Intelligence
- Historical development and milestones in Deep Learning
- Basic concepts of neural networks and deep learning
Module 2: Neural Network Architecture
- Perceptrons and multi-layer perceptrons (MLP)
- Activation functions
- Backpropagation algorithm for training neural networks
Module 3: Convolutional Neural Networks (CNN)
- Architecture and applications of CNNs
- Image classification and object detection
- Transfer learning with pre-trained models
Module 4: Recurrent Neural Networks (RNN)
- Architecture and applications of RNNs
- Sequence modeling and natural language processing
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks
Module 5: Deep Learning for Natural Language Processing (NLP)
- Word embeddings and word2vec
- Sentiment analysis
- Named Entity Recognition (NER)
Module 6: Generative Adversarial Networks (GANs)
- Introduction to GANs
- GAN architecture and training
- Applications of GANs, including image generation and style transfer
Module 7: Reinforcement Learning
- Basics of reinforcement learning
- Q-learning and policy gradient methods
- Applications of reinforcement learning in deep learning
Module 8: Deep Learning Frameworks
- Overview of popular deep learning frameworks (TensorFlow, PyTorch)
- Hands-on exercises with a deep learning framework
- Model deployment and serving
Module 9: Ethical and Responsible AI
- Considerations for ethical AI development
- Bias and fairness in machine learning models
- Responsible AI practices and guidelines
