2025

• 04 Aug Neural Architecture Search Part 5: Hardware-Aware NAS and Co-Design
• 30 Jul Neural Architecture Search Part 4: Efficient Estimation Strategies
• 25 Jul Neural Architecture Search Part 3: Applications and Real-World Impact
• 20 Jul Neural Architecture Search Part 2: Search Spaces and Strategies
• 15 Jul Neural Architecture Search Part 1: Foundations and Building Blocks
• 10 Jul Quantization Part 8: Mixed-Precision Quantization
• 05 Jul Quantization Part 7: Binary and Ternary Quantization
• 30 Jun Quantization Part 6: Quantization-Aware Training
• 25 Jun Quantization Part 5: Post-Training Quantization Techniques
• 20 Jun Quantization Part 4: Quantized Neural Network Operations
• 15 Jun Quantization Part 3: Linear Quantization Methods
• 10 Jun Quantization Part 2: K-Means Based Weight Quantization
• 05 Jun Quantization Part 1: Understanding Numeric Data Types
• 01 Jun Neural Network Pruning Part 5: Lottery Ticket Hypothesis and Training Sparse Networks
• 25 May Mastering LLM Inference Parameters - Part 3: Advanced Parameters and Practical Applications
• 25 May Neural Network Pruning Part 4: Advanced Techniques and Practical Applications
• 20 May Mastering LLM Inference Parameters - Part 2B: Advanced Sampling Methods
• 20 May Neural Network Pruning Part 3: Pruning Criteria
• 15 May Mastering LLM Inference Parameters - Part 2A: Basic Decoding Strategies
• 15 May Neural Network Pruning Part 2: Pruning Granularities
• 12 May Neural Network Pruning Part 1: Why Pruning Matters
• 10 May Mastering LLM Inference Parameters - Part 1: Temperature and Randomness Control
• 05 May Transformers from Scratch - Part 6: Training and Applications
• 28 Apr Transformers from Scratch - Part 5: The Decoder and Output Generation
• 21 Apr Transformers from Scratch - Part 4: Layer Norm and Feed-Forward Networks
• 14 Apr Transformers from Scratch - Part 3: Multi-Head Attention Mechanism
• 07 Apr Transformers from Scratch - Part 2: Architecture and Embeddings
• 01 Apr Transformers from Scratch - Part 1: From RNNs to Attention
• 25 Mar Principal Component Analysis (PCA) in Machine Learning

2024

• 21 Dec GPU Memory Hierarchy Part 3: Optimization and Best Practices
• 18 Dec GPU Memory Hierarchy Part 2: Global and Specialized Memory Types
• 15 Dec GPU Memory Hierarchy Part 1: Understanding the Foundations
• 10 Dec CPU Memory Architecture: Foundations and GPU Differences
• 19 Nov Efficiency Metrics Part 3: Computation Metrics (MACs and FLOPs)
• 16 Nov Efficiency Metrics Part 2: Memory Metrics for Deep Learning
• 13 Nov Efficiency Metrics Part 1: Performance Metrics for Deep Learning
• 12 Nov Why Do We Need Efficient Deep Learning Computing?