Courage to learn ML: Demystifying L1 & L2 Regularization (part 1)

Courage to learn ML: Demystifying L1 & L2 Regularization (part 1) AI News, AI, AI tools, Amy Ma, Innovation, itinai.com, LLM, t.me/itinai, Towards Data Science - Medium 🔹 Comprehend the underlying purpose of L1 and L2 regularization 🔹 Welcome to "Courage to learn ML," where we explore L1 and L2 regularization. In this series, we simplify complex ML concepts to help you understand them better. Today, we'll go beyond formulas and properties to uncover why these methods are used in ML. Get ready for some enlightening insights! 🔹 What is regularization and why do we need it? 🔹 Regularization is a technique that prevents models from overfitting. Overfitting happens when a model becomes too complex and learns from noise in the training data, causing poor performance on unseen data. Regularization strikes a balance between complexity and generalization, improving the model's ability to perform well on new data. 🔹 What are L1 and L2 regularization? 🔹 L1 and L2 regularization are methods that prevent overfitting by adding a penalty term to the model's loss function. This penalty discourages the model from assigning too much importance to any single feature, simplifying the model. These techniques keep the model balanced and focused on the true patterns in the data. 🔹 Why do we penalize large coefficients and how do they increase model complexity? 🔹 Large coefficients amplify both useful information and unwanted noise in the data. This makes the model sensitive to small changes and overemphasizes noise. Smaller coefficients help the model focus on broader patterns, reducing sensitivity to minor fluctuations. This promotes better generalization and improves performance on new, unseen data. 🔹 Why are there multiple combinations of weights and biases in a neural network? 🔹 Neural networks have complex landscapes with multiple local minima in their loss function. Each combination of weights and biases represents a potential solution to minimize the loss. The non-linear activation functions in the network enable it to approximate the underlying function of the data in various ways, resulting in redundancy in network design. 🔹 Why aren't bias terms penalized in L1 and L2 regularization? 🔹 Biases have a relatively modest impact on model complexity compared to weights. They mainly serve to shift the model's output independently of the input features. Regularization techniques primarily focus on preventing overfitting by regulating the magnitude of the weights, which have a larger influence on the model's complexity. Regularization is a key technique in ML to prevent overfitting and improve model generalization. Understanding L1 and L2 regularization concepts helps you build models that strike the right balance between complexity and performance. Join us in part two of the series to dive deeper into L1 and L2 regularization, where we'll unravel their layers with an intuitive understanding using Lagrange multipliers. 🚀 AI Solutions to Evolve Your Company 🚀 If you want to evolve your company with AI and stay competitive, here are some practical solutions: 🔸 Identify Automation Opportunities: Discover key customer interaction points that can benefit from AI. 🔸 Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 🔸 Select an AI Solution: Choose tools that align with your needs and provide customization. 🔸 Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram channel t.me/itinainews or follow us on Twitter @itinaicom. 🔦 Spotlight on a Practical AI Solution: AI Sales Bot 🔦 Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram @aiscrumbot – free consultation 🔹 "Courage to learn ML: Demystifying L1 & L2 Regularization" (part 1) on Towards Data Science – Medium 🔹 Twitter – @itinaicom