Deep Learning is a specialized subset of Machine Learning inspired by the structure of the human brain. It uses multi-layered neural networks to learn from vast amounts of unstructured data like images, audio, and text.
Why is it called "Deep" Learning?
The "Deep" in Deep Learning refers to the number of layers in the neural network. Traditional neural networks might have 2-3 layers. Deep learning models can have hundreds or thousands. This depth allows the model to learn a hierarchy of features—from simple edges and textures to complex shapes and objects.
What is Automatic Feature Extraction?
In traditional ML, humans had to manually select features (e.g., "does this image have ears?"). In Deep Learning, the network performs automatic feature extraction. It learns what features are important directly from the raw pixels or text.
Why is Deep Learning important now?
Deep Learning is the technology behind self-driving cars, voice assistants, facial recognition, and the recent generative AI boom. It thrives on scale—more data and more compute usually lead to better performance.
Key Architectures
- CNNs (Convolutional Neural Networks): The kings of computer vision.
- RNNs (Recurrent Neural Networks): Good for time-series and sequential data.
- Transformers: The state-of-the-art for natural language processing (NLP).
Tune the Hyperparameters
Experiment with different settings to see how they affect the training process of a deep learning model.
Quest: The Deployment
Train a model to classify data with >90% accuracy. Tune the hyperparameters (Epochs, Learning Rate, Layers) to avoid underfitting or overfitting.
Frequently Asked Questions
Is Deep Learning the same as Neural Networks?
Deep Learning is essentially the use of *deep* neural networks. So all Deep Learning involves neural networks, but not all neural networks are 'deep' (though in modern context, the terms are often used interchangeably).
Why does it require GPUs?
Deep Learning involves a massive number of matrix multiplications. GPUs (Graphics Processing Units) are designed to handle these parallel operations much faster than CPUs.
How much data do I need?
Generally, Deep Learning requires massive datasets to perform well. For smaller datasets, traditional Machine Learning algorithms often outperform Deep Learning.
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