machine-learning

Gradient descent is a fundamental optimization algorithm in machine learning. It's a way for models to learn from data and improve their accuracy by gradually adjusting their internal settings. Think of it like carefully descending a hill to find the lowest point, each small step you take brings you closer to the best possible solution.

Explaining gradient descent in machine learning with a simple analogy

An exploration of the cyclical nature of AI development, tracing the rise and fall of new technologies within the field, and how this pattern has repeated throughout history.

A grand unified theory of the AI hype cycle

Learn how to fine-tune LLaMA large language models efficiently using PEFT LoRA for cost-effective, private AI customization with step-by-step guidance and open-source tools.

Exploring machine learning approaches for fine tuning Llama models

An introduction to Q-learning, a model-free reinforcement learning algorithm used to learn optimal policies in Markov Decision Processes.

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