Advanced AI/ML: Deep Learning, LLMs & Production Systems
Master deep learning, LLMs, and deploying ML systems to production.
What you'll learn
Build and train neural networks, apply transfer learning and transformers/LLMs, and deploy, monitor, and scale ML systems in production (MLOps).
Who it's for
Experienced ML practitioners.
Prerequisites
Solid ML foundations and Python proficiency.
Deep learning, LLMs, and the MLOps practices that put models into production.
Curriculum
- 1
Advanced Weight Initialization Strategies
4 min - 2
Normalization Techniques at Scale
3 min - 3
High-Dimensional Optimization Landscapes
4 min - 4
Residual Connections and Gradient Stability
4 min - 5
Gating Units and Activation Functions
4 min - 6
Implementing Multi-Head Attention
4 min - 7
Positional Encoding Architectures
4 min - 8
Transformer Encoder-Decoder Design
3 min - 9
Project Milestone: Custom Architecture Setup
3 min - 10
Tokenization Strategies for LLMs
3 min - 11
Scaling Laws and Compute Budgets
4 min - 12
Data Parallelism Strategies
3 min - 13
Tensor and Pipeline Parallelism
4 min - 14
Efficient Dataset Loading and Prefetching
4 min - 15
Fine-tuning Methodologies Overview
4 min - 16
Parameter-Efficient Fine-Tuning (LoRA)
4 min - 17
Quantized LoRA (QLoRA)
4 min - 18
Alignment with RLHF
4 min - 19
Direct Preference Optimization (DPO)
4 min - 20
Project Milestone: Domain-Specific Fine-Tuning
3 min - 21
Vector Databases and Similarity Search
4 min - 22
Retrieval Strategies for RAG
3 min - 23
Context Management and Windowing
4 min - 24
Agentic Tool Use and Function Calling
4 min - 25
Chain-of-Thought and Multi-Step Reasoning
4 min - 26
Self-Correction and Iterative Refinement
4 min - 27
Project Milestone: RAG and Agent Integration
3 min - 28
Post-Training Quantization (PTQ)
4 min - 29
Model Pruning Techniques
4 min - 30
Knowledge Distillation
4 min - 31
Optimized Inference Runtimes (vLLM)
4 min - 32
TensorRT-LLM for High-Performance Serving
3 min - 33
ONNX Runtime for Cross-Platform Inference
3 min - 34
Project Milestone: Inference Optimization
3 min - 35
CI/CD for ML (MLOps)
4 min - 36
Continuous Training (CT) Pipelines
4 min - 37
Observability and Logging
4 min - 38
Drift Detection and Data Monitoring
4 min - 39
LLM-as-a-Judge for Evaluation
4 min - 40
Scaling Deployments with Kubernetes
4 min - 41
GPU Resource Allocation and Scheduling
3 min - 42
Project Milestone: Production Deployment
3 min - 43
Advanced Activation Checkpointing
4 min - 44
Mixed Precision Training (FP8/BF16)
4 min - 45
Distributed Optimizer States
4 min - 46
Gradient Accumulation and Batch Sizing
4 min - 47
Multi-Modal Model Architectures
4 min - 48
Mixture-of-Experts (MoE) Layers
4 min