Unit 899: Deep Learning Fundamentals and Applications

1. Introduction to Deep Learning

• Definition and scope of deep learning
• Historical context and evolution from traditional machine learning
• Key differences between traditional machine learning and deep learning
• Applications across industries (e.g., healthcare, autonomous driving, NLP)

2. Artificial Neural Networks (ANN)

2.1 Structure of ANNs

• Neurons and layers: input, hidden, output
• Weights and biases

2.2 Activation Functions

• Common activation functions: sigmoid, tanh, ReLU
• Role in introducing non-linearity

2.3 Feedforward Process

• Forward propagation mechanics
• Output generation

3. Convolutional Neural Networks (CNN)

3.1 CNN Architecture

• Convolutional layers: filters/kernels, stride, padding
• Pooling layers: max pooling, average pooling
• Fully connected layers

3.2 CNN in Computer Vision

• Feature extraction hierarchy
• Use cases: image classification, object detection, segmentation

3.3 Key Concepts

• Receptive field
• Parameter sharing

4. Recurrent Neural Networks (RNN)

4.1 Sequential Data Handling

• Recurrent connections and temporal dynamics
• Use cases: speech recognition, language modeling

4.2 Challenges

• Vanishing and exploding gradients

4.3 Advanced RNN Variants

• Long Short-Term Memory (LSTM): gates and memory cells
• Gated Recurrent Unit (GRU): simplified gating mechanism

5. Optimization Techniques in Deep Learning

5.1 Gradient Descent Algorithms

• Batch gradient descent
• Stochastic gradient descent (SGD)
• Mini-batch gradient descent

5.2 Advanced Optimizers

• Adam optimizer and its advantages

5.3 Hyperparameters

• Learning rate tuning
• Impact of batch size

6. Regularization and Dropout in Neural Networks

6.1 Overfitting and Underfitting

• Identification and impact on model performance

6.2 Regularization Techniques

• L1 and L2 regularization
• Early stopping

6.3 Dropout Layers

• Mechanism and effects on training

6.4 Batch Normalization

• Purpose and implementation

7. Transfer Learning and Fine-Tuning

• Concept of transfer learning
• Using pre-trained models (e.g., VGG, ResNet)
• Fine-tuning for specific tasks
• Benefits and limitations

8. Generative Adversarial Networks (GANs)

8.1 GAN Architecture

• Generator and discriminator networks
• Adversarial training process

8.2 Applications

• Synthetic data generation
• Image-to-image translation

8.3 Challenges

• Training instability
• Mode collapse

9. Ethical Considerations in Deep Learning

• Bias in datasets and models
• Privacy concerns
• Fairness and transparency
• Strategies for ethical AI deployment

10. Case Studies in Deep Learning

• Image recognition systems
• Natural language processing applications (e.g., chatbots, translation)
• Autonomous vehicles and sensor fusion
• Healthcare diagnostics