From Gradient Descent to Adam: A Visual Guide to Optimization

🧠 AI, Machine Learning, and Data Science made simple. Machine Learning Pills turns complex concepts into short, animated explanations with clear visuals and practical examples. Understand how things work, why they matter, and when to use them. 🎯 For curious beginners, students, developers, and data professionals who want to build their intuition and deepen their understanding. 📚 Topics include machine learning, statistics, probability, causal inference, time series, forecasting, deep learning, generative AI, LLMs, AI agents, and algorithmic trading. 💊 Subscribe for bite-sized explanations that help the concepts stick. 📩 Get more AI and ML insights in the newsletter: https://mlpills.substack.com Gradient descent is the core engine behind AI model training. Learn how algorithms find the best parameters to minimize errors. This video breaks down how models learn by taking steps downhill on a loss function to find the optimal solution. We explore how the learning rate controls the size of these steps and why choosing the right one is critical to preventing your model from missing the target or moving too slowly. We also compare different optimization approaches, including batch, stochastic, and mini-batch methods. You will see how each strategy balances computational efficiency and stability, helping you understand how to choose the right training path for your machine learning projects. Which machine learning concept should we cover next? Let me know in the comments. Subscribe to Machine Learning Pills for clear, visual explanations of machine learning, statistics, and AI. Get more AI and ML insights in the newsletter: https://mlpills.substack.com 0:00 The Core Mechanism Behind AI Learning 1:11 Decoding the Gradient and Update Rules 2:23 Mastering the Learning Rate Balance 3:37 Modern Optimizers and Advanced Tricks