Machine Learning : Divide & Rule works well in ML too. All Classification Algorithms doesn't easily classify MultiClass Classification problems naturally like binary. Knn like algos work well in both the cases. Continue ... #MachineLearning #Python #AirdropCrypto #javascript

🚀 Deep Learning Part — 9: Optimizers Are What You Need! Optimizers are the unsung heroes of neural networks — they decide how weights learn, how fast we converge, and how smooth training feels. From Gradient Descent → Adam, here’s what’s inside 👇 🔹 GD : the classic

🚀 Deep Learning — 7: Optimize your Neural Networks through Dropouts & Regularization Deeper networks are powerful, but they can easily overfit. Here’s how dropout, L₁/L₂ regularization, and architecture design can make your models more robust & generalizable. Medium Blog:

🚀 Just deployed my Enhanced MNIST Digit Recognizer: trained with PyTorch & deployed on Hugging Face Spaces! ✅ 5-Fold CV with OneCycleLR + AMP ✅ >99.4% accuracy ✅ Draw or upload digits live 🖊️ Try it here 👇 🔗 https://t.co/nyF2t6hTUm #DeepLearning #PyTorch #AI

⚡ Deep Learning — Part 6: Activation Functions in Neural Networks From Sigmoid → tanh → ReLU → Leaky ReLU → GeLU, activation functions have powered how networks learn & converge. 🧠 Learn how: • Sigmoid / tanh caused vanishing gradients • ReLU sparked the deep learning

🧠 Deep Learning — Part 5: How to Train Your Neural Networks Training a neural net = math + structure + intuition. From the Perceptron to deep multi-layer models — this post breaks down: 🔹 Forward & Backward Propagation 🔹 Chain Rule + Memoization (Backprop essence) 🔹

🚀 Deep Learning: Multi-Layered Perceptron (MLP) 🧠 Stacking neurons — the foundation of Deep Learning. A single neuron (Perceptron) learns simple patterns. Stack millions and suddenly you have the power to learn speech, vision, and language. Here’s why 👇 1️⃣ MLPs connect

🤯 Perceptron vs Logistic Regression, the OG connection that started Deep Learning! Before Transformers and GPUs, Deep Learning began with one neuroscience-inspired question: “Can we mimic human behavior in machines?” 🧠 From that spark came the Perceptron, a single “neuron”

🧠➡️🤖 Ever wondered how the human brain inspired #DeepLearning? In my latest blog, I explore how a single biological neuron became the blueprint for artificial neural networks from dendrites to the Perceptron! Read here 👉 https://t.co/FLRg5oFqGm @towards_AI @OpenAI

Exploring the Origins of Deep Learning: From a Single Neuron to Modern AI Did you know that deep learning began with a single neuron in 1957? 🧠 In my latest blog, I delve into the fascinating evolution of deep learning, tracing its roots from Frank Rosenblatt's Perceptron to

🔥 Built an AI app that predicts customer subscriptions with a Decision Tree! 🚀 💡 Trained a model on banking data, balanced with SMOTE, and hit 89% accuracy & 0.87 F1-score. 📊 Visualized the tree + feature importance with Plotly. 🌐 Wrapped it in a sleek Flask app with

🚀 #SVM in Action: Sentiment Analysis meets Amazon-style UI! 🛒💬 Just built & deployed a full Amazon Review Sentiment System powered by Linear SVM, with an interactive web UI, all live from #GoogleColab via Flask + ngrok. 🔍 Pipeline: Data cleaned & prepped TF-IDF features

🚨 Phishing Shield AI: Catch phishing emails with Naive Bayes! 🛡️📧 Built a sleek Streamlit app that detects phishing emails using MultinomialNB, GaussianNB, and a Stacking Ensemble model. Lightweight, fast, and powerful! 💪 🔑 Features: Smart preprocessing: TF-IDF + keyword

K-Nearest Neighbors in Action 🌺 Built an interactive Iris Classifier with Streamlit + scikit-learn to show how even “simple” ML can shine: 🔹 K-NN with Euclidean & Manhattan metrics 🔹 10-fold CV for robust accuracy 🔹 Interactive sliders & visuals (decision boundaries,

🔹 UMAP : Uniform Manifold Approximation and Projection Balances local & global structure, scalable & robust. ✨ Insights • Best clustering: Silhouette 0.35 (2D), 0.33 (3D) • Trustworthiness ~0.95 • Runtime ~28 s for 10k digits 🏆 UMAP gives the clearest separation of

2️⃣ t-SNE : t-distributed Stochastic Neighborhood 🔹 t-SNE on MNIST Non-linear embedding for local structure. ✨ Insights • Clear digit clusters in 2D/3D • Silhouette: 0.22 (2D), 0.15 (3D) • Trustworthiness ~0.96 • Runtime ~23 s for 10k digits 💡 Great for visualizing

1️⃣ PCA : Principal Component Analysis 🔹 PCA on MNIST (3k–10k digits) Linear dimensionality reduction capturing global variance. ✨ Insights • Top 3 PCs explain ~14.5% variance (PC1 ≈ 6.17%, PC2 ≈ 4.33%) • Silhouette ≈ 0 → weak clustering (as expected for linear models)

🚀 Exploring MNIST with Dimensionality Reduction: Interactive Web App I built a FastAPI app to visualize PCA, t-SNE, and UMAP on MNIST (3k–10k digits) in 2D & 3D. 🔍 Highlights • UMAP 2D has the best clustering (Silhouette ≈ 0.35) • PCA’s top 3 comps explain ~14.5% variance

Project 7 : 🎯 Just built a smart plagiarism detection tool! It doesn’t just catch exact copy-paste lines—it also spots near matches, helping you stay original and avoid accidental plagiarism. Perfect for students, writers, and creators who care about authenticity. ✨ ⚡ Built

Project 6 : Just built GitHub Vibe Check! Now you can compare your GitHub profile with others — followers, repos, contributions, and more — all in one place. Perfect for devs who want to see where they stand, get motivated, or just have some friendly competition. 😎 💻