📌 How to Crack Machine Learning System-Design Interviews
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-14 | ⏱️ Read time: 15 min read
Ace your machine learning system design interviews at top tech companies. This comprehensive guide provides a deep dive into the interview process at Meta, Apple, Reddit, Amazon, Google, and Snap, equipping you with the strategies needed to succeed in these high-stakes technical assessments.
#MachineLearning #SystemDesign #TechInterview #AI
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-14 | ⏱️ Read time: 15 min read
Ace your machine learning system design interviews at top tech companies. This comprehensive guide provides a deep dive into the interview process at Meta, Apple, Reddit, Amazon, Google, and Snap, equipping you with the strategies needed to succeed in these high-stakes technical assessments.
#MachineLearning #SystemDesign #TechInterview #AI
📌 I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-15 | ⏱️ Read time: 9 min read
A deep dive into the mechanics of neural network training. This detailed analysis meticulously measures key training metrics every 5 steps over 10,000 iterations, providing a high-resolution view of the learning process. The findings offer granular insights into model convergence and the subtle dynamics often missed by standard monitoring, making it a valuable read for ML practitioners and researchers seeking to better understand how models learn.
#NeuralNetworks #MachineLearning #DeepLearning #DataAnalysis #ModelTraining
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-15 | ⏱️ Read time: 9 min read
A deep dive into the mechanics of neural network training. This detailed analysis meticulously measures key training metrics every 5 steps over 10,000 iterations, providing a high-resolution view of the learning process. The findings offer granular insights into model convergence and the subtle dynamics often missed by standard monitoring, making it a valuable read for ML practitioners and researchers seeking to better understand how models learn.
#NeuralNetworks #MachineLearning #DeepLearning #DataAnalysis #ModelTraining
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📌 Understanding Convolutional Neural Networks (CNNs) Through Excel
🗂 Category: DEEP LEARNING
🕒 Date: 2025-11-17 | ⏱️ Read time: 12 min read
Demystify the 'black box' of deep learning by exploring Convolutional Neural Networks (CNNs) with a surprising tool: Microsoft Excel. This hands-on approach breaks down the fundamental operations of CNNs, such as convolution and pooling layers, into understandable spreadsheet calculations. By visualizing the mechanics step-by-step, this method offers a uniquely intuitive and accessible way to grasp how these powerful neural networks learn and process information, making complex AI concepts tangible for developers and data scientists at any level.
#DeepLearning #CNN #MachineLearning #Excel #AI
🗂 Category: DEEP LEARNING
🕒 Date: 2025-11-17 | ⏱️ Read time: 12 min read
Demystify the 'black box' of deep learning by exploring Convolutional Neural Networks (CNNs) with a surprising tool: Microsoft Excel. This hands-on approach breaks down the fundamental operations of CNNs, such as convolution and pooling layers, into understandable spreadsheet calculations. By visualizing the mechanics step-by-step, this method offers a uniquely intuitive and accessible way to grasp how these powerful neural networks learn and process information, making complex AI concepts tangible for developers and data scientists at any level.
#DeepLearning #CNN #MachineLearning #Excel #AI
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📌 Introducing ShaTS: A Shapley-Based Method for Time-Series Models
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-17 | ⏱️ Read time: 9 min read
Explaining time-series models with standard tabular Shapley methods can be misleading as they ignore crucial temporal dependencies. A new method, ShaTS (Shapley-based Time-Series), is introduced to solve this problem. Specifically designed for sequential data, ShaTS provides more accurate and reliable interpretations for time-series model predictions, addressing a critical gap in explainable AI for this data type.
#ExplainableAI #TimeSeries #ShapleyValues #MachineLearning
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-17 | ⏱️ Read time: 9 min read
Explaining time-series models with standard tabular Shapley methods can be misleading as they ignore crucial temporal dependencies. A new method, ShaTS (Shapley-based Time-Series), is introduced to solve this problem. Specifically designed for sequential data, ShaTS provides more accurate and reliable interpretations for time-series model predictions, addressing a critical gap in explainable AI for this data type.
#ExplainableAI #TimeSeries #ShapleyValues #MachineLearning
📌 How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-18 | ⏱️ Read time: 14 min read
Automatic plant leaf recognition leverages deep feature embeddings to transform leaf images into dense numerical vectors in a high-dimensional space. By calculating the Euclidean similarity between these vector representations, machine learning models can accurately identify and classify plant species. This computer vision technique provides a powerful and scalable solution for botanical and agricultural applications, moving beyond traditional manual identification methods.
#ComputerVision #MachineLearning #DeepLearning #FeatureEmbeddings #ImageRecognition
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-18 | ⏱️ Read time: 14 min read
Automatic plant leaf recognition leverages deep feature embeddings to transform leaf images into dense numerical vectors in a high-dimensional space. By calculating the Euclidean similarity between these vector representations, machine learning models can accurately identify and classify plant species. This computer vision technique provides a powerful and scalable solution for botanical and agricultural applications, moving beyond traditional manual identification methods.
#ComputerVision #MachineLearning #DeepLearning #FeatureEmbeddings #ImageRecognition
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📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch
🗂 Category: DEEP LEARNING
🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read
Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.
#PyTorch #MachineLearning #NeuralNetwork #Regression #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read
Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.
#PyTorch #MachineLearning #NeuralNetwork #Regression #Python
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📌 Making Smarter Bets: Towards a Winning AI Strategy with Probabilistic Thinking
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-11-19 | ⏱️ Read time: 11 min read
Craft a winning AI strategy by embracing probabilistic thinking. This approach provides practical guidance on identifying high-value opportunities, managing your product portfolio, and overcoming behavioral biases. Learn to make smarter, data-driven bets to navigate uncertainty and gain a competitive advantage in the rapidly evolving AI landscape.
#AIStrategy #ProductManagement #DecisionMaking #MachineLearning
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-11-19 | ⏱️ Read time: 11 min read
Craft a winning AI strategy by embracing probabilistic thinking. This approach provides practical guidance on identifying high-value opportunities, managing your product portfolio, and overcoming behavioral biases. Learn to make smarter, data-driven bets to navigate uncertainty and gain a competitive advantage in the rapidly evolving AI landscape.
#AIStrategy #ProductManagement #DecisionMaking #MachineLearning
📌 Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-22 | ⏱️ Read time: 4 min read
Mastering the bias-variance trade-off is key to effective machine learning. Overfitting creates models that memorize training data noise and fail to generalize, while underfitting results in models too simple to find patterns. The optimal model exists in a "sweet spot," balancing complexity to perform well on new, unseen data. This involves learning just the right amount from the training set—not too much, and not too little—to achieve strong predictive power.
#MachineLearning #DataScience #Overfitting #BiasVariance
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-22 | ⏱️ Read time: 4 min read
Mastering the bias-variance trade-off is key to effective machine learning. Overfitting creates models that memorize training data noise and fail to generalize, while underfitting results in models too simple to find patterns. The optimal model exists in a "sweet spot," balancing complexity to perform well on new, unseen data. This involves learning just the right amount from the training set—not too much, and not too little—to achieve strong predictive power.
#MachineLearning #DataScience #Overfitting #BiasVariance
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📌 Learning Triton One Kernel at a Time: Softmax
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-23 | ⏱️ Read time: 10 min read
Explore a step-by-step guide to implementing a fast, readable, and PyTorch-ready softmax kernel with Triton. This tutorial breaks down how to write efficient GPU code for a crucial machine learning function, offering developers practical insights into high-performance computing and AI model optimization.
#Triton #GPUProgramming #PyTorch #MachineLearning
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-23 | ⏱️ Read time: 10 min read
Explore a step-by-step guide to implementing a fast, readable, and PyTorch-ready softmax kernel with Triton. This tutorial breaks down how to write efficient GPU code for a crucial machine learning function, offering developers practical insights into high-performance computing and AI model optimization.
#Triton #GPUProgramming #PyTorch #MachineLearning
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📌 Struggling with Data Science? 5 Common Beginner Mistakes
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-24 | ⏱️ Read time: 6 min read
New to data science? Accelerate your career growth by steering clear of common beginner pitfalls. The journey into data science is challenging, but understanding and avoiding five frequent mistakes can significantly streamline your learning curve and set you on a faster path to success. This guide highlights the key errors to watch out for as you build your skills and advance in the field.
#DataScience #MachineLearning #CareerAdvice #DataAnalytics
🗂 Category: DATA SCIENCE
🕒 Date: 2025-11-24 | ⏱️ Read time: 6 min read
New to data science? Accelerate your career growth by steering clear of common beginner pitfalls. The journey into data science is challenging, but understanding and avoiding five frequent mistakes can significantly streamline your learning curve and set you on a faster path to success. This guide highlights the key errors to watch out for as you build your skills and advance in the field.
#DataScience #MachineLearning #CareerAdvice #DataAnalytics
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📌 The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
#MachineLearning #DeepLearning #Excel #DataScience
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
#MachineLearning #DeepLearning #Excel #DataScience
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📌 The Greedy Boruta Algorithm: Faster Feature Selection Without Sacrificing Recall
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 19 min read
The Greedy Boruta algorithm offers a significant performance enhancement for feature selection. As a modification of the standard Boruta method, it dramatically reduces computation time. This speed increase is achieved without sacrificing recall, ensuring high sensitivity in identifying all relevant features. It's a powerful optimization for data scientists seeking to accelerate their machine learning workflows while preserving model quality.
#FeatureSelection #MachineLearning #DataScience #Algorithms
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-30 | ⏱️ Read time: 19 min read
The Greedy Boruta algorithm offers a significant performance enhancement for feature selection. As a modification of the standard Boruta method, it dramatically reduces computation time. This speed increase is achieved without sacrificing recall, ensuring high sensitivity in identifying all relevant features. It's a powerful optimization for data scientists seeking to accelerate their machine learning workflows while preserving model quality.
#FeatureSelection #MachineLearning #DataScience #Algorithms
📌 Learning, Hacking, and Shipping ML
🗂 Category: AUTHOR SPOTLIGHTS
🕒 Date: 2025-12-01 | ⏱️ Read time: 11 min read
Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.
#MachineLearning #AI #DataScience #MLOps #Hackathon
🗂 Category: AUTHOR SPOTLIGHTS
🕒 Date: 2025-12-01 | ⏱️ Read time: 11 min read
Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.
#MachineLearning #AI #DataScience #MLOps #Hackathon
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📌 The Machine Learning Lessons I’ve Learned This Month
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 4 min read
Discover key machine learning lessons from recent hands-on experience. This monthly review covers the real-world costs and trade-offs of using AI assistants like Copilot, the critical importance of intentionality in project choices (as even a non-choice has consequences), and an exploration of finding unexpected "Christmas connections" within data. A concise look at practical, hard-won insights for ML practitioners.
#MachineLearning #Copilot #AIStrategy #DataScience
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 4 min read
Discover key machine learning lessons from recent hands-on experience. This monthly review covers the real-world costs and trade-offs of using AI assistants like Copilot, the critical importance of intentionality in project choices (as even a non-choice has consequences), and an exploration of finding unexpected "Christmas connections" within data. A concise look at practical, hard-won insights for ML practitioners.
#MachineLearning #Copilot #AIStrategy #DataScience
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📌 The Machine Learning “Advent Calendar” Day 1: k-NN Regressor in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
#MachineLearning #kNN #Excel #DataScience #Regression
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-01 | ⏱️ Read time: 16 min read
Kick off a Machine Learning Advent Calendar series with a practical guide to the k-NN regressor. This first installment demonstrates how to implement this fundamental, distance-based model using only Microsoft Excel. It's a great hands-on approach for understanding core ML concepts from scratch, without the need for a complex coding environment.
#MachineLearning #kNN #Excel #DataScience #Regression
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📌 The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-02 | ⏱️ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
#MachineLearning #kNN #Excel #DataScience
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-02 | ⏱️ Read time: 9 min read
Discover how to implement the k-Nearest Neighbors (k-NN) classifier directly in Excel. This article, part of a Machine Learning "Advent Calendar" series, explores the popular classification algorithm along with its variants and improvements. It offers a practical, hands-on approach to understanding a fundamental ML concept within a familiar spreadsheet environment, making it accessible even without a dedicated coding setup.
#MachineLearning #kNN #Excel #DataScience
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📌 The Machine Learning “Advent Calendar” Day 3: GNB, LDA and QDA in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
#MachineLearning #Excel #DataScience #LDA #Statistics
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read
Day 3 of the Machine Learning "Advent Calendar" series explores Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). This guide uniquely demonstrates how to implement these powerful classification algorithms directly within Excel, offering a practical, code-free approach. Learn the core concepts behind these models, transitioning from simple local distance metrics to a more robust global probability framework, making advanced statistical methods accessible to a wider audience.
#MachineLearning #Excel #DataScience #LDA #Statistics
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📌 The Machine Learning “Advent Calendar” Day 5: GMM in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-05 | ⏱️ Read time: 6 min read
Explore Gaussian Mixture Models (GMM), a powerful clustering algorithm that serves as a natural extension and improvement over k-Means. This guide, part of a Machine Learning Advent Calendar series, uniquely demonstrates how to implement and understand GMMs entirely within Microsoft Excel. It's a practical approach for grasping core ML concepts without requiring a dedicated coding environment, making advanced data science techniques more accessible.
#MachineLearning #GMM #Excel #DataScience #Clustering
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-05 | ⏱️ Read time: 6 min read
Explore Gaussian Mixture Models (GMM), a powerful clustering algorithm that serves as a natural extension and improvement over k-Means. This guide, part of a Machine Learning Advent Calendar series, uniquely demonstrates how to implement and understand GMMs entirely within Microsoft Excel. It's a practical approach for grasping core ML concepts without requiring a dedicated coding environment, making advanced data science techniques more accessible.
#MachineLearning #GMM #Excel #DataScience #Clustering
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📌 The Machine Learning “Advent Calendar” Day 4: k-Means in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read
Discover how to implement the k-Means clustering algorithm, a fundamental machine learning technique, using only Microsoft Excel. This guide, part of a "Machine Learning Advent Calendar" series, walks through building a training algorithm from scratch in a familiar spreadsheet environment, demystifying what "real" ML looks like in practice.
#MachineLearning #kMeans #Excel #DataScience #Tutorial
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-04 | ⏱️ Read time: 7 min read
Discover how to implement the k-Means clustering algorithm, a fundamental machine learning technique, using only Microsoft Excel. This guide, part of a "Machine Learning Advent Calendar" series, walks through building a training algorithm from scratch in a familiar spreadsheet environment, demystifying what "real" ML looks like in practice.
#MachineLearning #kMeans #Excel #DataScience #Tutorial
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⚡️ How does regularization prevent overfitting?
📈 #machinelearning algorithms have revolutionized the way we solve complex problems and make predictions. These algorithms, however, are prone to a common pitfall known as #overfitting. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. As a result, the model performs poorly on unseen data, leading to inaccurate predictions.
📈 To combat overfitting, #regularization techniques have been developed. Regularization is a method that adds a penalty term to the loss function during the training process. This penalty term discourages the model from fitting the training data too closely, promoting better generalization and preventing overfitting.
📈 There are different types of regularization techniques, but two of the most commonly used ones are L1 regularization (#Lasso) and L2 regularization (#Ridge). Both techniques aim to reduce the complexity of the model, but they achieve this in different ways.
📈 L1 regularization adds the sum of absolute values of the model's weights to the loss function. This additional term encourages the model to reduce the magnitude of less important features' weights to zero. In other words, L1 regularization performs feature selection by eliminating irrelevant features. By doing so, it helps prevent overfitting by reducing the complexity of the model and focusing only on the most important features.
📈 On the other hand, L2 regularization adds the sum of squared values of the model's weights to the loss function. Unlike L1 regularization, L2 regularization does not force any weights to become exactly zero. Instead, it shrinks all weights towards zero, making them smaller and less likely to overfit noisy or irrelevant features. L2 regularization helps prevent overfitting by reducing the impact of individual features while still considering their overall importance.
📈 Regularization techniques strike a balance between fitting the training data well and keeping the model's weights small. By adding a regularization term to the loss function, these techniques introduce a trade-off that prevents the model from being overly complex and overly sensitive to the training data. This trade-off helps the model generalize better and perform well on unseen data.
📈 Regularization techniques have become an essential tool in the machine learning toolbox. They provide a means to prevent overfitting and improve the generalization capabilities of models. By striking a balance between fitting the training data and reducing complexity, regularization techniques help create models that can make accurate predictions on unseen data.
📚 Reference: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
https://t.iss.one/DataScienceM⛈ ⚡️ ⚡️ ⚡️ ⚡️
📈 #machinelearning algorithms have revolutionized the way we solve complex problems and make predictions. These algorithms, however, are prone to a common pitfall known as #overfitting. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. As a result, the model performs poorly on unseen data, leading to inaccurate predictions.
📈 To combat overfitting, #regularization techniques have been developed. Regularization is a method that adds a penalty term to the loss function during the training process. This penalty term discourages the model from fitting the training data too closely, promoting better generalization and preventing overfitting.
📈 There are different types of regularization techniques, but two of the most commonly used ones are L1 regularization (#Lasso) and L2 regularization (#Ridge). Both techniques aim to reduce the complexity of the model, but they achieve this in different ways.
📈 L1 regularization adds the sum of absolute values of the model's weights to the loss function. This additional term encourages the model to reduce the magnitude of less important features' weights to zero. In other words, L1 regularization performs feature selection by eliminating irrelevant features. By doing so, it helps prevent overfitting by reducing the complexity of the model and focusing only on the most important features.
📈 On the other hand, L2 regularization adds the sum of squared values of the model's weights to the loss function. Unlike L1 regularization, L2 regularization does not force any weights to become exactly zero. Instead, it shrinks all weights towards zero, making them smaller and less likely to overfit noisy or irrelevant features. L2 regularization helps prevent overfitting by reducing the impact of individual features while still considering their overall importance.
📈 Regularization techniques strike a balance between fitting the training data well and keeping the model's weights small. By adding a regularization term to the loss function, these techniques introduce a trade-off that prevents the model from being overly complex and overly sensitive to the training data. This trade-off helps the model generalize better and perform well on unseen data.
📈 Regularization techniques have become an essential tool in the machine learning toolbox. They provide a means to prevent overfitting and improve the generalization capabilities of models. By striking a balance between fitting the training data and reducing complexity, regularization techniques help create models that can make accurate predictions on unseen data.
📚 Reference: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
https://t.iss.one/DataScienceM
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