Deep Learning
Neural networks are the backbone of modern artificial intelligence, powering everything from image recognition to self-driving cars. But not all neural networks are the same. Depending on the task, different types of neural networks are used to optimize performance and accuracy. 1. Feedforward Neural Network (FNN) – The Basics The Feedforward Neural Network is the … Read more
Deep learning is exciting, but let’s be honest—it’s also overwhelming. Most people immediately jump into TensorFlow or PyTorch (which are great), but there’s an entire hidden ecosystem of tools that can seriously boost your efficiency, debugging, and workflow. In this post, I’ll introduce you to five lesser-known yet powerful tools that can make your deep … Read more
Getting started with deep learning can feel overwhelming. You’ve probably heard about neural networks, training data, GPUs, and all the fancy algorithms—but where do you actually begin? The good news: you don’t need a supercomputer or a PhD to start. You just need the right tools. In this guide, we’ll walk through five essential tools … Read more
Neural networks power everything from voice assistants to self-driving cars, yet their inner workings can feel like magic. If you’ve ever wondered what makes them tick, you’re in the right place. Let’s break down the fundamental concepts of neural networks in a way that actually makes sense—without drowning in math-heavy jargon. 1. Neurons: The Building … Read more
The introduction of deep convolutional neural networks (CNNs) has dramatically improved image recognition capabilities. Among the seminal architectures, the VGG (Visual Geometry Group) Network, proposed by Karen Simonyan and Andrew Zisserman in 2014, was a breakthrough. It demonstrated the effectiveness of deep, simple, and uniform layer structures in achieving state-of-the-art performance on the ImageNet dataset. … Read more