Deep Learning

Table of Contents Introduction Why Real‑Time Inference Is Hard for LLMs TensorRT: A Primer Quantization Techniques for LLMs End‑to‑End Workflow: From PyTorch to TensorRT 5.1 Exporting to ONNX 5.2 Building an INT8 TensorRT Engine 5.3 Running Inference Practical Example: Optimizing a 7‑B GPT‑NeoX Model Performance Benchmarks & Analysis Best Practices, Common Pitfalls, and Debugging Tips Advanced Topics 9.1 [Dynamic Shapes & Variable‑Length Prompts] 9.2 [Multi‑GPU & Tensor Parallelism] 9.3 Custom Plugins for Flash‑Attention Future Directions in LLM Inference Acceleration Conclusion Resources Introduction Large language models (LLMs) such as GPT‑3, LLaMA, and Falcon have reshaped natural‑language processing, but their sheer size (tens to hundreds of billions of parameters) makes real‑time inference a daunting engineering challenge. Deployments that demand sub‑100 ms latency—interactive chatbots, code assistants, or on‑device AI—cannot afford the raw latency of a vanilla PyTorch or TensorFlow forward pass on a single GPU. ...

Introduction Transformer models have become the de‑facto standard for natural language processing (NLP), computer vision, and many other AI domains. While training these models often requires massive compute clusters, inference—especially at production scale—poses a different set of challenges. Real‑time applications such as chatbots, recommendation engines, or on‑device language assistants demand low latency, high throughput, and predictable resource usage. The dominant cost during inference is the matrix multiplication (often called GEMM – General Matrix‑Multiply) that underlies the attention mechanism and the feed‑forward layers. Modern CPUs, GPUs, TPUs, FPGAs, and purpose‑built ASICs provide hardware primitives that can accelerate these operations dramatically. However, out‑of‑the‑box kernels shipped with deep‑learning frameworks are rarely tuned for the exact shapes and precision requirements of a specific transformer workload. ...

Table of Contents Introduction The Evolution of Pose Detection Technology Understanding WiFi-Based Pose Estimation How WiFi DensePose Works Technical Architecture and Components Real-World Applications Privacy Advantages Over Traditional Systems Performance Metrics and Capabilities Challenges and Limitations The Future of Wireless Human Sensing Conclusion Resources Introduction Imagine a world where your WiFi router can track your movements, monitor your health, and detect falls—all without a single camera pointed at you. This isn’t science fiction; it’s the reality of WiFi-based human pose estimation, a transformative technology that’s reshaping how we think about motion detection, privacy, and ambient sensing[1][2]. ...

Large Language Models (LLMs) like GPT-2 and BERT have revolutionized natural language processing, and TensorFlow provides powerful tools to build, train, and deploy them. This detailed guide walks you through using TensorFlow and Keras for LLMs—from basics to advanced transformer architectures, fine-tuning pipelines, and on-device deployment.[1][2][4] Whether you’re prototyping a sentiment analyzer or fine-tuning GPT-2 for custom tasks, TensorFlow’s high-level Keras API simplifies complex workflows while offering low-level control for optimization.[1][2] ...

Table of Contents Introduction Part 1: Mathematical Foundations Part 2: Neural Network Fundamentals Part 3: Understanding Transformers Part 4: Large Language Models Explained Part 5: Training and Fine-Tuning LLMs Part 6: Practical Implementation Resources and Learning Paths Conclusion Introduction The rise of Large Language Models (LLMs) has revolutionized artificial intelligence and natural language processing. From ChatGPT to Claude to Gemini, these powerful systems can understand context, generate human-like text, and solve complex problems across domains. But how do they work? And more importantly, how can you learn to build them from scratch? ...