Master the fundamentals of deep architectures, training, and optimization techniques.

• Multi-layer perceptrons
• Backpropagation in depth
• Activation functions
• Weight initialization
• Batch normalization
• Dropout and regularization
• Gradient flow analysis
• Universal approximation

Learn CNN architectures for computer vision and image processing applications.

• Convolution operations
• Pooling layers
• CNN architectures
• Feature maps visualization
• Transfer learning
• Object detection
• Image segmentation
• Advanced CNN variants

Explore RNNs, LSTMs, and GRUs for sequential data and time series analysis.

• Vanilla RNNs
• Vanishing gradient problem
• LSTM architecture
• GRU networks
• Bidirectional RNNs
• Sequence-to-sequence models
• Attention mechanisms
• Advanced RNN variants

Master the revolutionary transformer architecture and attention-based models.

• Self-attention mechanism
• Multi-head attention
• Positional encoding
• Transformer blocks
• BERT and variants
• GPT family
• Vision transformers
• Efficient transformers

Study advanced generative techniques including GANs, VAEs, and diffusion models.

• Generative Adversarial Networks
• Variational Autoencoders
• Autoregressive models
• Flow-based models
• Diffusion models
• Style transfer
• Conditional generation
• Evaluation metrics

Advanced optimization techniques and training strategies for deep neural networks.

• Advanced optimizers
• Learning rate scheduling
• Gradient clipping
• Mixed precision training
• Distributed training
• Model parallelism
• Hyperparameter tuning
• Training diagnostics

Combine deep learning with reinforcement learning for complex decision-making tasks.

• Deep Q-Networks (DQN)
• Policy gradient methods
• Actor-Critic algorithms
• Proximal Policy Optimization
• Multi-agent RL
• Model-based RL
• Hierarchical RL
• Meta-learning in RL

Advanced NLP techniques using deep learning for language understanding and generation.

• Word embeddings
• Contextual embeddings
• Language modeling
• Machine translation
• Question answering
• Text summarization
• Dialogue systems
• Multilingual models

State-of-the-art computer vision techniques and applications using deep learning.

• Advanced object detection
• Instance segmentation
• 3D vision
• Video analysis
• Neural rendering
• Self-supervised learning
• Vision-language models
• Medical imaging

Learn how to build models that can quickly adapt to new tasks with minimal data.

• Learning to learn
• Model-Agnostic Meta-Learning
• Prototypical networks
• Matching networks
• Memory-augmented networks
• Gradient-based meta-learning
• Few-shot classification
• Zero-shot learning

Automated methods for discovering optimal neural network architectures.

• Architecture search spaces
• Evolutionary methods
• Reinforcement learning based
• Differentiable architecture search
• Efficient NAS methods
• Transfer NAS
• Hardware-aware NAS
• AutoML pipelines

Explore cutting-edge research directions and emerging trends in deep learning.

• Neural scaling laws
• Foundation models
• Multimodal learning
• Causal representation learning
• Neurosymbolic AI
• Quantum machine learning
• Responsible AI
• Future directions