Building Neural Network from Scratch with Python and CuPy

Day 6/20 my AI/ML journey 🚀 One thing I’m starting to appreciate more is how much work happens before machine learning models are even involved. This week I spent time working on reading and exploring datasets using Python. Simple things like: Understanding the structure of the dataset Checking the data types of each column Looking for missing values Inspecting how different features are distributed At first it seemed basic, but the more I explore datasets the more I realize how important this stage is. If you don’t understand your dataset, you can’t build a reliable model. Data first. Models later. #africaagility #learninginpublic #AI #MachineLearning #DataScience #Python

Mastering Image Transformation is one of the fundamental steps in Computer Vision. Whether you are building a simple filter or a complex deep-learning model, these operations are essential for data preprocessing. In this carousel, I’ve broken down 6 core OpenCV techniques: ✅ Resizing & Scaling ✅ Slicing/Cropping ✅ Rotation & Flipping ✅ Adding Text & Drawing Shapes #OpenCV #ComputerVision #Python #ImageProcessing #MachineLearning #DeepLearning #DataScience #AI

This week has been about a Python library to use S3-compatible stores, the Connections puzzle on top of DuckDB, a Zed fork without AI and an article on the impact of AI on learning to code. https://lnkd.in/e_gq_aMQ

I’ve been diving deep into how models actually "learn" by implementing Gradient Descent from scratch in Python. While libraries like PyTorch and TensorFlow handle this under the hood, building it manually helped me grasp the importance of: - The Cost Function: Quantifying error to guide the model. - Learning Rate Selection: Balancing the risk of "overshooting" vs. the inefficiency of slow convergence. - Partial Derivatives: Using the chain rule to calculate gradients and update weights. Understanding these fundamentals is crucial for debugging complex Deep Learning architectures. Next stop: Stochastic Gradient Descent (SGD) and Momentum! #MachineLearning #DeepLearning #Python #Mathematics #Optimization

Built a Machine Learning project to classify Muffin vs Cupcake using SVM, Decision Tree, and KNN. Explored data, trained models, and evaluated performance. 🍰📊 #MachineLearning #Python #DataScience #AI https://lnkd.in/d8Z5EiDc

📘 Starting My Machine Learning Journey 🚀 Today, I am sharing an overview of important Machine Learning Algorithms. 🔹 Supervised Learning • Classification • Regression 🔹 Unsupervised Learning 🔹 Reinforcement Learning This document contains simple and easy explanations for each algorithm. 📌 From tomorrow, I will start a daily series: 👉 Day 1 – Theory of Algorithm 👉 Day 2 – Python Implementation Stay tuned! #MachineLearning #DataScience #AI #Python #LearningJourney

Decoding the Book of Soyga, Version II: Unveiling a Verifiable Structure in a 16th-Century Grimoire Through Python, AI, and Manuscript Analysis. Read my Article here : https://lnkd.in/d7XXtt5C #Soyga #AI #Decoding

There’s a growing shift in AI: optimize inference, not just training. InferScale documentation is a great example of this shift. It enables better LLM outputs using inference-time scaling—making open-source models far more competitive. The docs are concise and actionable, making it easy to experiment and integrate into your stack. If you're building with LLMs, don’t skip this. https://lnkd.in/gRmY5Gc8 #AITrends #LLM #OpenSourceAI #Python #AIEngineering #GenAI #FutureOfAI

Recently completed a presentation on Jupyter Notebook for Machine Learning. In this, I covered: Basics and key features of Jupyter Notebook How it helps in building ML models step by step A simple Linear Regression example Data visualization using Python It is a powerful tool for learning, experimenting, and understanding machine learning concepts in a practical way. Looking forward to exploring more in Machine Learning and AI. #MachineLearning #JupyterNotebook #Python #AI #Learning

🤖 Machine Learning is shaping the future. From data to decisions, from code to intelligence. The world is moving towards automation and smart systems. Learning technologies like Python and Machine Learning is no longer optional — it’s the future. 🚀 Start today, stay ahead tomorrow. #MachineLearning #AI #Python #Technology #Future #Learning