Deep learning explained in simple terms—neural networks with multiple layers, how training works, and where deep learning is used in vision, language, generative AI, and other fields.
Start Here: What Is Deep Learning? A Beginner’s Guide →
Learn how transfer learning reuses pretrained models, how fine-tuning changes them, and how to compare both with training from scratch using one clear example.
Transfer Learning Explained: Reusing and Fine-Tuning a Model Read More »
Learn how convolutional neural networks use filters, feature maps and layers, with a simple worked example and clear links to image classification.
Convolutional Neural Networks Explained Read More »
Deep learning models do not simply “get smarter” each time they see data. They are trained through a controlled loop: process a batch, measure a loss, compute gradients, update parameters, and repeat—then evaluate on data that was not used for those updates. This beginner-friendly Lesson 9 explains that complete workflow, from the first training batch
How Deep Learning Works (Beginner-Friendly Guide) Read More »
Deep learning and machine learning are not separate competing fields. Deep learning is a subset of machine learning that uses multilayer neural networks. The practical question is therefore not usually “machine learning or deep learning?” It is whether a particular problem benefits from a deep neural-network approach or from another machine-learning method. Last reviewed and
Deep Learning vs Machine Learning: Key Differences Explained Read More »
Deep learning is a branch of machine learning built around neural networks with multiple layers of learned representations. It is responsible for many of the advances behind modern computer vision, speech recognition, language models, generative AI, and multimodal systems. The word deep refers to the use of multiple computational layers—not to a system having deeper
What Is Deep Learning? A Beginner’s Guide Read More »
A neural network is a machine-learning model built from layers of connected computational units. These units transform numerical inputs using learned weights, biases, and nonlinear functions so the network can approximate useful relationships in data. Google’s current Machine Learning Glossary provides a useful technical reference for neural-network, neuron, activation-function, and backpropagation terminology. Neural networks power
Neural Networks Explained: How They Work and Why They Matter Read More »
