deep learning

Deep learning is a subfield of machine learning in which models built from many stacked layers of neural network computation learn their own representations of raw data at successive levels of abstraction. The model works out for itself which features of the data matter, instead of relying on ones that a practitioner designed by hand.

The depth in the name refers to the number of layers. Each layer transforms the output of the one below it through a weighted combination and a nonlinear activation function. Early layers pick up local detail such as edges or character patterns, and later layers compose that detail into higher-level structure.

Because every layer is differentiable, training propagates error backward through the whole stack, a procedure called backpropagation, and updates all parameters by gradient descent. Learning those features rather than receiving them is what makes deep learning a form of representation learning.

The network below steps through one example at a time, first forward to a prediction and then backward as the error reaches every layer. Training it continuously shows the updates bending that prediction around the data.

Architecture families differ by data type. Convolutional networks handle images and other grid-shaped input, recurrent networks handle sequences, and the transformer captures long-range dependencies, which makes it the basis of current large language models.

Layered networks and backpropagation date to the 1980s, but the approach became dominant in the 2010s once large datasets and GPU hardware made deep models practical to train. A 2015 review by LeCun, Bengio, and Hinton marks that turn.

Deep models need far more data and compute than classical methods, and their learned representations resist inspection, which complicates debugging and accountability.

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Python AI: How to Build a Neural Network & Make Predictions

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For additional information on related topics, take a look at the following resources:

• PyTorch vs TensorFlow for Your Python Deep Learning Project (Tutorial)
• Building a Neural Network & Making Predictions With Python AI (Course)
• Hugging Face Transformers: Leverage Open-Source AI in Python (Tutorial)
• Use TorchAudio to Prepare Audio Data for Deep Learning (Tutorial)
• Python Deep Learning: PyTorch vs Tensorflow (Course)
• Setting Up Python for Machine Learning on Windows (Tutorial)
• Python AI: How to Build a Neural Network & Make Predictions (Quiz)
• Hugging Face Transformers (Quiz)
• Use TorchAudio to Prepare Audio Data for Deep Learning (Quiz)
• Setting Up Python for Machine Learning on Windows (Quiz)