Course Content
Deep Learning Essentials
13 sections · 61 lessons
What are the applications of Deep Learning?
What you need to know
A strong answer does not just list areas. It groups them by data type and says why deep learning fits each one.
| Data type | Typical tasks | Common architecture |
|---|---|---|
| Images and video | Classification, detection, segmentation, OCR | CNNs, vision transformers |
| Text | Translation, summarisation, search, chat | Transformers |
| Audio | Speech recognition, keyword spotting, text-to-speech | CNNs on spectrograms, transformers |
| User behaviour | Recommendations, ranking | Embedding models, two-tower networks |
| Sequences over time | Forecasting, anomaly detection | RNNs, LSTMs, transformers |
| Generation | Images, code, audio | Diffusion models, LLMs |
The reason deep learning dominates these areas is the same in each case: the useful signal lives in patterns across many raw values, and the network can learn those patterns if it sees enough examples. Where data is a small table, classical ML is usually still the tool.
A good way to show depth is to name the task type too, because it decides the output layer and loss:
- Classification — one label per input (leaf disease, spoken command).
- Detection and segmentation — where objects are, not just what.
- Sequence-to-sequence — input and output are both sequences (translation).
- Generation — produce new content (images, text).
- Anomaly detection — flag inputs that look unlike normal ones (fraud).
A real-life example
Think of a single phone in India on a normal day:
- Google Lens reads a Hindi signboard and translates it: vision + language.
- "Hey Google, set an alarm for 6" is recognised by a small keyword-spotting network that listens for the wake word on-device, then a bigger speech model transcribes the rest.
- A UPI payment app scores each transaction for fraud in milliseconds, using a model that learns from sequences of past transactions: sequence models and anomaly detection.
- The shopping app ranks products using user and product embeddings: recommendation.
- The camera's portrait mode blurs the background using a segmentation network.
Each one uses deep learning for the same reason: the input is raw pixels, sound or long behaviour sequences.
Follow-up questions to expect
- "Where would you not use deep learning?" — Small tabular problems, tasks needing a fully explainable decision, or when there is too little labelled data and no pretrained model to transfer from.
- "Which application have you built?" — Pick one and describe the input, the model, the metric and one problem you hit. A specific story beats a long list.
- "What made LLMs possible?" — The transformer architecture, self-supervised pretraining on huge text corpora, and enough compute to scale both.