Advanced Neural Network Concepts – Just a heads up for now
1. Advanced Neural Network Concepts – Quick Glance
1. Activation Functions Beyond the Basics
- Leaky ReLU, ELU, Swish, GELU – Fix vanishing gradients or improve convergence
- Use case: Transformer architectures use GELU (e.g., BERT)
2. Weight Initialization Techniques
- Xavier Initialization (for tanh)
- He Initialization (for ReLU)
- Helps avoid exploding/vanishing gradients from the start
3. Batch Normalization
- Normalizes input of each layer to stabilize training
- Acts like a regularizer (sometimes replaces dropout)
4. Residual Connections / Skip Connections
- Introduced in ResNet
- Allow gradients to flow through very deep networks
5. Attention Mechanism
- Core idea: Let the network “focus” on important parts
- Used in: Transformers, Vision Transformers (ViT), BERT
6. Transformers
- Entirely attention-based, no convolutions or recurrence
- Backbone of modern NLP and even vision models
- Example: GPT, BERT, T5
7. Recurrent Neural Networks (RNNs) & Variants
- For sequence/time-series data
- Variants: LSTM, GRU
- Handle long-term dependencies (vs. simple RNNs)
8. Autoencoders & Variational Autoencoders (VAE)
- Learn compressed latent representations
- Use cases: Image denoising, anomaly detection, generative modeling
9. Generative Adversarial Networks (GANs)
- Two-player game: Generator vs Discriminator
- Use cases: Image generation, style transfer, data augmentation
10. Transfer Learning
- Reuse pre-trained models (like ResNet, BERT) for new tasks
- Save training time + perform better on small datasets
11. Neural Architecture Search (NAS)
- Let algorithms design the best network architecture
- Advanced AutoML technique
12. Self-Supervised Learning
- Learn from unlabeled data using pretext tasks
- Foundation of GPT, SimCLR, BYOL
13. Optimization Tricks
- Learning rate scheduling (cosine, exponential decay)
- Warm restarts, Gradient Clipping
- AdamW, Lookahead, RAdam optimizers
14. Explainability & Interpretability
- SHAP, LIME, Integrated Gradients
- Helps in model trust, debugging, and compliance (especially in healthcare/finance)
15. Model Compression & Deployment
- Techniques: Quantization, Pruning, Knowledge Distillation
- Tools: TensorFlow Lite, ONNX, CoreML, NVIDIA TensorRT