Mastering Advanced Techniques and Models
Focus on cutting-edge models and techniques within your chosen specialization. Learn state-of-the-art architectures, optimization methods, and advanced training strategies.
Learning Objectives
- ✓Implement and understand state-of-the-art model architectures
- ✓Master advanced training techniques for improved performance
- ✓Optimize models for production deployment and efficiency
- ✓Apply transfer learning and fine-tuning strategies effectively
🎯 Core Advanced Techniques
State-of-the-Art Model Architectures
Master the latest breakthrough architectures that are pushing the boundaries of AI performance.
Key Topics:
- •Vision Transformers (ViTs) and Hybrid Architectures
- •Efficient Transformer Variants (MobileBERT, DistilBERT)
- •Neural Architecture Search (NAS)
🚀 Practical Exercise
Implement a Vision Transformer from Scratch
Advanced Training Techniques
Learn cutting-edge training strategies that improve model performance and efficiency.
Key Topics:
- •Gradient Accumulation and Mixed Precision Training
- •Learning Rate Scheduling and Warm-up Strategies
- •Regularization Techniques (Dropout, DropConnect)
🚀 Practical Exercise
Optimize Training Pipeline Performance
Model Optimization and Efficiency
Optimize models for production deployment with minimal performance loss.
Key Topics:
- •Model Compression and Pruning Strategies
- •Quantization (INT8, INT4) and Calibration
- •Knowledge Distillation and Teacher-Student Models
🚀 Practical Exercise
Deploy Optimized Model to Mobile
Transfer Learning and Fine-tuning
Master advanced transfer learning techniques for rapid adaptation to new domains.
Key Topics:
- •Few-Shot and Zero-Shot Learning
- •Domain Adaptation and Domain Generalization
- •Meta-Learning and Model-Agnostic Meta-Learning (MAML)
🚀 Practical Exercise
Build a Few-Shot Learning System
State-of-the-Art Model Architectures
Master the latest breakthrough architectures that are pushing the boundaries of AI performance.
🎯 Key Topics
- •Vision Transformers (ViTs) and Hybrid Architectures
- •Efficient Transformer Variants (MobileBERT, DistilBERT)
- •Neural Architecture Search (NAS)
- •Mixture of Experts (MoE) Models
- •Graph Neural Networks (GNNs)
- •Multimodal Architectures (CLIP, DALL-E 2)
🛠️ Implementation Techniques
- •Self-attention mechanisms and positional encoding
- •Layer normalization and residual connections
- •Depthwise separable convolutions
- •Knowledge distillation for model compression
- •Pruning and quantization techniques
- •Cross-modal attention and fusion
🚀 Hands-On Exercise
Implement a Vision Transformer from Scratch
Build and train a ViT model for image classification, understanding every component.
Learning Outcomes:
- • Deep understanding of implementation details
- • Hands-on experience with cutting-edge techniques
- • Portfolio-ready project implementation
Advanced Training Techniques
Learn cutting-edge training strategies that improve model performance and efficiency.
🎯 Key Topics
- •Gradient Accumulation and Mixed Precision Training
- •Learning Rate Scheduling and Warm-up Strategies
- •Regularization Techniques (Dropout, DropConnect)
- •Data Augmentation and Synthetic Data Generation
- •Curriculum Learning and Progressive Training
- •Adversarial Training and Robustness
🛠️ Implementation Techniques
- •Automatic Mixed Precision (AMP) with FP16
- •Cosine annealing and cyclic learning rates
- •Stochastic Weight Averaging (SWA)
- •Label smoothing and mixup augmentation
- •Progressive resizing and test-time augmentation
- •Fast Gradient Sign Method (FGSM) for adversarial examples
🚀 Hands-On Exercise
Optimize Training Pipeline Performance
Implement advanced training techniques to achieve 2x speedup with maintained accuracy.
Learning Outcomes:
- • Deep understanding of implementation details
- • Hands-on experience with cutting-edge techniques
- • Portfolio-ready project implementation
Model Optimization and Efficiency
Optimize models for production deployment with minimal performance loss.
🎯 Key Topics
- •Model Compression and Pruning Strategies
- •Quantization (INT8, INT4) and Calibration
- •Knowledge Distillation and Teacher-Student Models
- •Neural Architecture Search for Efficiency
- •Hardware-Aware Optimization
- •Edge Deployment and Mobile Optimization
🛠️ Implementation Techniques
- •Structured and unstructured pruning
- •Post-training and quantization-aware training
- •Progressive knowledge distillation
- •ONNX optimization and TensorRT acceleration
- •Model parallelism and pipeline parallelism
- •Core ML and TensorFlow Lite conversion
🚀 Hands-On Exercise
Deploy Optimized Model to Mobile
Compress a large model by 10x and deploy it to run efficiently on mobile devices.
Learning Outcomes:
- • Deep understanding of implementation details
- • Hands-on experience with cutting-edge techniques
- • Portfolio-ready project implementation
Transfer Learning and Fine-tuning
Master advanced transfer learning techniques for rapid adaptation to new domains.
🎯 Key Topics
- •Few-Shot and Zero-Shot Learning
- •Domain Adaptation and Domain Generalization
- •Meta-Learning and Model-Agnostic Meta-Learning (MAML)
- •Parameter-Efficient Fine-tuning (LoRA, Adapters)
- •Continual Learning and Catastrophic Forgetting
- •Cross-lingual and Cross-modal Transfer
🛠️ Implementation Techniques
- •Prototypical networks and matching networks
- •Adversarial domain adaptation
- •Gradient-based meta-learning algorithms
- •Low-Rank Adaptation (LoRA) and prefix tuning
- •Elastic Weight Consolidation (EWC)
- •Multilingual BERT and cross-lingual embeddings
🚀 Hands-On Exercise
Build a Few-Shot Learning System
Create a system that can learn new classes with just 5 examples per class.
Learning Outcomes:
- • Deep understanding of implementation details
- • Hands-on experience with cutting-edge techniques
- • Portfolio-ready project implementation
🏆 Master-Level Projects
Challenge yourself with these comprehensive projects that combine multiple advanced techniques.
Multi-Modal AI Assistant
Build an AI system that can understand and generate text, images, and audio simultaneously.
Technologies:
Key Skills:
Efficient Edge AI Pipeline
Create a complete pipeline for deploying optimized models to edge devices with real-time inference.
Technologies:
Key Skills:
Self-Supervised Learning Framework
Implement a framework for learning representations from unlabeled data using contrastive learning.
Technologies:
Key Skills:
📚 Advanced Learning Resources
Research Papers
- • "Attention Is All You Need" (Transformer)
- • "An Image is Worth 16x16 Words" (ViT)
- • "BERT: Pre-training of Deep Bidirectional Transformers"
- • "LoRA: Low-Rank Adaptation of Large Language Models"
- • "Model-Agnostic Meta-Learning for Fast Adaptation"
Implementation Guides
- • Hugging Face Transformers Documentation
- • PyTorch Lightning Advanced Tutorials
- • TensorFlow Model Optimization Toolkit
- • NVIDIA TensorRT Developer Guide
- • Papers with Code Implementation Reviews
Tools & Frameworks
- • Weights & Biases for experiment tracking
- • Optuna for hyperparameter optimization
- • Ray Tune for distributed training
- • DeepSpeed for large model training
- • Triton for custom GPU kernels