Deep Learning is a subfield of Machine Learning, which is in turn a subfield of Artificial Intelligence (AI). It involves training artificial neural networks, which are algorithms inspired by the structure and function of the human brain, to learn and make intelligent decisions on their own.
Definition:
Deep Learning is a machine learning technique that teaches computers to learn by example, just like humans do. It uses artificial neural networks with multiple layers (hence "deep") to progressively extract higher-level features from raw input data. By doing so, deep learning models can learn complex patterns and make intelligent predictions or decisions.History:
The concept of artificial neural networks dates back to the 1940s, but deep learning as we know it today really took off in the 2000s due to advancements in computing power, large datasets, and new algorithms. Some key milestones include:- 1958 - Perceptron algorithm invented, the first artificial neural network
- 1980s - Backpropagation algorithm popularized for training neural networks
- 2006 - Deep Belief Networks introduced by Geoffrey Hinton
- 2012 - AlexNet wins ImageNet competition, kickstarting the deep learning revolution
- 2016 - AlphaGo beats world champion at Go, showing deep learning's potential
- Artificial Neural Networks: Deep learning models are based on artificial neural networks with multiple layers. Each layer contains interconnected "nodes" that process data.
- Training by Example: Deep neural networks learn from large amounts of labeled training data. They adjust their internal parameters to map inputs to the correct outputs.
- Feature Hierarchy: With multiple layers, deep neural networks learn a hierarchy of features. Lower layers learn simple features (e.g. edges in an image), while higher layers combine these into more complex features (e.g. shapes, objects).
- End-to-End Learning: Traditional machine learning relies on manual feature engineering by experts. Deep learning operates directly on raw data, learning the features itself in an end-to-end fashion.
- Architecture: A deep neural network is constructed with an input layer, multiple hidden layers, and an output layer. The number and size of layers depends on the complexity of the problem.
- Forward Propagation: Training data is fed into the network. Each node performs a weighted sum of its inputs, applies a non-linear activation function, and passes the result to the next layer. This continues until the output layer makes a prediction.
- Loss Function: The network's prediction is compared to the true label using a loss function that quantifies the error. The goal is to minimize this loss.
- Backpropagation: The error is "propagated backwards" through the network. Using calculus, the contribution of each parameter to the error is calculated. The parameters are then adjusted slightly in the direction that reduces the error.
- Optimization: The forward and backward passes are repeated many times on the training data, gradually optimizing the network's parameters to map inputs to outputs correctly. Techniques like gradient descent and stochastic optimization are used.
- Inference: Once trained, the network can accept new, unseen inputs and make intelligent predictions or decisions.
Deep learning has revolutionized AI, achieving state-of-the-art results in fields like computer vision, natural language processing, and robotics. With continued research and ever-growing computational resources, it's a field with immense potential to transform many industries and domains.