Writing AlexNet from Scratch in PyTorch
Learn how to build the AlexNet architecture from scratch using PyTorch. This step-by-step guide covers each layer in detail, helping you understand and implement this classic convolutional neural network.
Padding In Convolutional Neural Networks
In this article, we explore how and why we use padding in CNNs in computer vision tasks. We’ll then jump into a full coding demo showing the utility of padding.
Face Generation with GANs
A primer for developing a custom neural network to learn to generate novel facial images using Deep Convolutional generative adversarial networks.
Understanding Feedforward and Feedback Networks (or recurrent) neural network
Explore the key differences between feedforward and feedback neural networks, how they work, and where each type is best applied in AI and machine learning.
Constructing Neural Networks From Scratch: Part 1
Learn how to construct neural networks from scratch with NumPy, and simultaneously see how the internal mechanisms behind popular libraries like PyTorch and Keras are implemented.
Building sharp regression models with K-Means Clustering + SVR
Discover how combining K-Means clustering with SVR improves regression accuracy, especially for complex or unevenly distributed datasets.
Energy-Efficient Deep Learning — How Precision Scaling Reduces Carbon Footprint
Learn how precision scaling lowers compute needs, cuts energy use, and reduces the carbon footprint of deep learning models for sustainable AI.
Checkpointing in TensorFlow
Follow this guide to learn how to directly monitor and checkpoint your models during the training process!
Understanding LLM Poisoning
Discover how LLM poisoning works, why even 0.01% poisoned data can compromise AI systems, and the steps to prevent backdoor attacks in models.
Pooling In Convolutional Neural Networks
Explore the whys and the hows behind the process of pooling in CNN architectures, and compare 2 common techniques: max and average pooling.
