Neural Networks

Table of Contents Introduction From Chatbots to Agentic Systems Liquid Neural Networks: A Primer 3.1 Historical Context 3.2 Core Mechanics 3.3 Why “Liquid” Matters Open‑Source Landscape for Liquid Neural Networks Designing Agentic Workflows with Liquid NNs 5.1 Defining the Agentic Loop 5.2 State Representation & Memory 5.3 Action Generation & Execution Practical Example: Autonomous Data‑Enrichment Pipeline 6.1 Problem Statement 6.2 System Architecture 6.3 Implementation Walk‑through 6.4 Running the Pipeline Evaluation: Metrics and Benchmarks Operational Considerations 8.1 Scalability & Latency 8.2 Safety & Alignment 8.3 Monitoring & Observability Challenges, Limitations, and Future Directions Conclusion Resources Introduction Artificial intelligence has long been synonymous with chatbots—systems designed to converse with humans using natural language. While conversational agents remain valuable, the AI community is rapidly shifting toward agentic workflows, where autonomous agents not only talk but act in dynamic environments. These agents can plan, execute, and adapt without explicit human supervision, opening doors to applications ranging from automated DevOps to self‑optimizing recommendation engines. ...

Demystifying Zono-Conformal Prediction: Smarter AI Uncertainty with Zonotopes Explained Imagine you’re driving a self-driving car on a foggy highway. Your AI system predicts the road ahead, but how do you know if it’s confident? Traditional AI spits out a single number—like “the car in front is 50 meters away”—but what if it’s wrong? Zono-conformal prediction, from a groundbreaking new paper, upgrades this to a range of possibilities, like saying “the car is between 45-55 meters, with a 95% guarantee it’s correct.” This isn’t just safer; it’s revolutionizing how AI handles uncertainty in real-world tasks from medical diagnosis to stock trading.[1] ...

Table of Contents Introduction From Rule‑Based Chatbots to Agentic Systems What Are Liquid Neural Networks? 3.1 Core Concepts: Continuous‑Time Dynamics 3.2 Liquid Time‑Constant (LTC) Cells Why Liquid Networks Enable Agentic Workflows Open‑Source Implementations Worth Knowing Designing an Agentic Workflow with Liquid NNs 6.1 Defining the Agentic Loop 6.2 State Representation & Memory 6.3 Action Generation & Execution Practical Example 1: Real‑Time Anomaly Detection in IoT Streams Practical Example 2: Adaptive Customer‑Support Assistant Deployment Considerations 9.1 Hardware Acceleration 9.2 Model Versioning & Monitoring Performance Benchmarking & Metrics Challenges, Pitfalls, and Future Directions Conclusion Resources Introduction The last decade has witnessed a dramatic shift in how we think about conversational AI. Early rule‑based chatbots gave way to large language models (LLMs) that can generate human‑like text, and today we stand on the cusp of the next evolution: agentic workflows—systems that not only converse but act autonomously in dynamic environments. ...

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? ...

Modern large language models (LLMs) like GPT-4, Llama, and Claude look magical—but they are built on concepts that have matured over decades: neural networks, gradient descent, and clever architectural choices. This tutorial walks you step by step from classic neural networks all the way to LLMs. You’ll see how each idea builds on the previous one, and you’ll get practical code examples along the way. Table of Contents Foundations: What Is a Neural Network? 1.1 The Perceptron 1.2 From Perceptron to Multi-Layer Networks 1.3 Activation Functions ...