Deep Learning
Deep Learning is a specialised subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to learn complex patterns from large amounts of data. It powers image…
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Definition
Deep Learning is a specialised subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to learn complex patterns from large amounts of data. It powers image recognition, voice assistants, language translation, and autonomous vehicles. Deep learning has enabled breakthroughs that were impossible with traditional ML — from Google Translate's quality to medical image diagnosis.
Key Points
- Uses neural networks with multiple hidden layers
- Requires large datasets and significant computational power (GPUs/TPUs)
- Key architectures: CNNs (images), RNNs/LSTMs (sequences), Transformers (language)
- Powers modern AI applications: ChatGPT, image generation, autonomous driving
Frequently Asked Questions
Traditional ML algorithms need manual feature engineering — you tell the algorithm what patterns to look for. Deep learning automatically discovers features from raw data through multiple neural network layers. This makes it more powerful for complex tasks like image recognition but requires much more data and compute.
Deep learning training typically requires GPUs (NVIDIA RTX 3090 or better for serious work) or cloud computing (AWS, GCP). Inference (running trained models) can often run on standard hardware. Indian developers commonly use Google Colab (free GPU) for learning and AWS/GCP for production.
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