Sep 8, 2025 · 5 min read · The following is an edited version of a synthesis written by Grok. Addressing Overfitting in Gradient Descent Training: A Stochastic Exposure Approach In neural network training via gradient descent, a common issue is overconfidence, where models out...
Join discussionJul 26, 2025 · 7 min read · In this part of the series, we will dive into one of the most foundational concepts in machine learning — Linear Regression. We'll explore its core ideas and intuitively walk through the mathematics involved. Using only Python and NumPy, we'll implem...
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Mar 10, 2025 · 4 min read · At the start of this year, I embarked on my journey to learn AI/ML with the 30 days Kaggle challenge organized by ML Kolkata and in this blog, I will share the lessons learned everyday, share resources, problems faced along with how I solved those. D...
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Feb 14, 2025 · 1 min read · I bring thee good news. There are ways of mitigating overfitting! Collect more training data. (but, but, but sometimes, collecting more data can be challenge, especially when they are not available) Next thing we might want to consider is to reduce...
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Feb 13, 2025 · 1 min read · So I have not been feeling well, but thankfully I just recovered. I'm just going to give a summary of this. Underfitting: This occurs when the model doesn't fit the training set well. We can also say that there's a high bias thingy going on "Normal...
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Dec 29, 2024 · 3 min read · In our previous post we briefed about the Machine Learning development lifecycle and touched upon ML models performance evaluation. In continuation to that, we will see how and why model fit is important aspect of model evaluation. If you recall, we ...
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Oct 14, 2024 · 5 min read · Neural networks and deep learning are at the forefront of artificial intelligence (AI) and machine learning (ML), revolutionizing how machines perceive, interpret, and interact with data. This article provides an in-depth look at the fundamental arch...
Join discussionAug 11, 2024 · 2 min read · 1. Model Complexity Model Complexity ⬆️ Bias ⬇️ Variance ⬆️ Overfitting ⬆️ Accuracy ⬇️ (if too complex) 2. Regularization Regularization Strength ⬆️ Model Complexity ⬇️ Bias ⬆️ Variance ⬇️ Overfitting ⬇️ Accuracy ⬇️ (if to...
Join discussionJun 21, 2024 · 4 min read · Have you ever wondered what's the right moment to stop your training process? How to ensure the models generalize well and save time and computational power at the same time? Hard questions, right? I got you! I want to help you find the answers becau...
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Jun 10, 2024 · 6 min read · Have you ever spent days training a deep learning model, only to find out it performs poorly on new data? This is a common frustration for many data scientists and machine learning engineers. Imagine pouring countless hours into fine-tuning your neur...
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