Deep Learning
Machine learning using neural networks with many layers — the basis of modern AI.
What is Deep Learning?
Deep learning is a subset of ML built on neural networks that have many "layers" of processing. Each layer transforms the input data and passes it to the next, allowing the network to learn increasingly abstract representations.
Deep learning is what makes modern AI possible — image recognition, speech synthesis, language models, and self-driving cars all rely on it. The two most important architectures in 2026 are **convolutional neural networks (CNNs)** for images and **transformers** for language.
Training deep learning models requires significant compute (GPUs) and data. This is why most companies use pre-trained models (e.g., from HuggingFace or OpenAI) and fine-tune them rather than training from scratch.
Every modern AI system you have used — ChatGPT, Google Translate, Spotify recommendations — uses deep learning at its core. Understanding it opens doors to senior ML roles.
A Hyderabad-based healthcare startup uses a CNN to detect tuberculosis from chest X-rays. The model is trained on 50,000 annotated images from Indian hospitals and achieves 94% accuracy — comparable to radiologists, deployed in remote clinics.
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