NumPy

Build a Deep Learning Library with Python & NumPy

This project offers a comprehensive guide to building a deep learning library from scratch using Python and NumPy, aiming to demystify the complexities of modern frameworks. Key components include creating an autograd engine for automatic differentiation, constructing neural network modules with layers and activations, implementing optimizers like SGD and Adam, and developing a training loop for model persistence and dataset handling. Additionally, it covers the construction and training of Convolutional Neural Networks (CNNs), providing a conceptual and educational resource rather than a production-ready framework. Understanding these foundational elements is crucial for anyone looking to deepen their knowledge of deep learning and its underlying mechanics.

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Posted on

Jan 1, 2026

by

TechWithoutHype

in

Deep Dives, Learning

Topics: Python, Deep Learning, neural networks

BareGPT: A Numpy-Based Transformer with Live Attention

BareGPT is a new transformer model similar to NanoGPT, implemented entirely in Numpy, offering a unique approach to machine learning with live attention visualization. This development showcases the versatility of Numpy in creating efficient machine learning models without relying on more complex frameworks. The transformer model provides insights into attention mechanisms, which are crucial for understanding how models process and prioritize input data. This matters because it highlights the potential for simpler, more accessible tools in machine learning, making advanced techniques more approachable for a broader audience.

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Posted on

Dec 29, 2025

by

GeekOptimizer

in

Deep Dives, Learning

Topics: machine learning, Python, accessibility

TensorFlow 2.17 Updates

TensorFlow 2.17 introduces significant updates, including a CUDA update that enhances performance on Ada-Generation GPUs like NVIDIA RTX 40**, L4, and L40, while dropping support for older Maxwell GPUs to keep Python wheel sizes manageable. The release also prepares for the upcoming TensorFlow 2.18, which will support Numpy 2.0, potentially affecting some edge cases in API usage. Additionally, TensorFlow 2.17 marks the last version to include TensorRT support, as future releases will no longer support it. These changes reflect ongoing efforts to optimize TensorFlow for modern hardware and software environments, ensuring better performance and compatibility.

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Topics: machine learning, Nvidia, performance

PixelBank: ML Coding Practice Platform

PixelBank is a new hands-on coding practice platform tailored for Machine Learning and AI, addressing the gap left by platforms like LeetCode which focus on data structures and algorithms but not on ML-specific coding skills. It allows users to practice writing PyTorch models, perform NumPy operations, and work on computer vision algorithms with instant feedback. The platform offers a variety of features including daily challenges, beautifully rendered math equations, hints, solutions, and progress tracking, with a free-to-use model and optional premium features for additional problems. PixelBank aims to help users build consistency and proficiency in ML coding through an organized, interactive learning experience. Why this matters: PixelBank provides a much-needed resource for aspiring ML engineers to practice and refine their skills in a practical, feedback-driven environment, bridging the gap between theoretical knowledge and real-world application.