What is Deep Learning?
Deep Learning is a subset of Machine Learning based on multi-layered Artificial Neural Networks (ANNs). While traditional machine learning algorithms (like Random Forests or Linear Regression) are effective at analyzing structured, tabular data (rows and columns in a database), Deep Learning algorithms are especially good at processing large volumes of complex, unstructured data, such as high-resolution images, raw audio files, and large bodies of natural language text.
The “Deep” in Deep Learning refers to the architectural depth of the neural network. While a simple neural network might have one or two layers of processing nodes, modern Deep Learning architectures (like Convolutional Neural Networks for vision, or Transformers for language) contain dozens, or even hundreds, of interconnected layers. The largest of these networks have billions of parameters, allowing them to learn abstract, nuanced patterns that traditional algorithms struggle to capture.
The Architecture of Neural Networks
A Deep Learning model loosely mimics the biological structure of the human brain, utilizing interconnected nodes (artificial neurons).
Layers of Abstraction
A Deep Neural Network is divided into three primary sections: the Input Layer, the Hidden Layers (where the “deep” learning occurs), and the Output Layer.
Imagine training a network to recognize a picture of a cat.
- The Input Layer: The network takes in the raw image as a grid of pixel values.
- The Hidden Layers: As the data passes through the first hidden layer, the network identifies basic features (like sudden changes in pixel contrast, identifying edges). As the data progresses deeper into the network, the learned features become more complex. A middle layer might recognize simple shapes and textures. A deeper layer might combine those shapes to recognize a cat’s eye or a pointed ear.
- The Output Layer: The final layer combines these features and outputs a probability that the image is a cat.
Backpropagation and Optimization
During training, the network frequently makes mistakes. If it predicts an image is a dog, but the label says “cat,” training uses an algorithm called Backpropagation, which applies the chain rule of calculus. It traces the error backward through the hidden layers to compute how each weight contributed, and an optimizer (like gradient descent) adjusts the weights slightly so the network makes a more accurate prediction next time.
The Hardware and Data Requirements
Deep Learning is resource-intensive and usually needs two things to work well:
- Large Volumes of Data: Traditional machine learning algorithms often plateau; past a certain point, adding more rows of data yields little additional accuracy. Deep Learning models tend to keep improving with more data. They often need large datasets (frequently stored in Data Lakehouses) to tune their many parameters without overfitting.
- GPU Acceleration: The mathematics behind Deep Learning is dominated by large matrix multiplications. Central Processing Units (CPUs) have relatively few cores and run this workload slowly. Large Deep Learning models are trained on clusters of Graphics Processing Units (GPUs, like Nvidia H100s) or similar accelerators, which have thousands of cores designed for parallel math.
Summary of Technical Value
Deep Learning transformed Artificial Intelligence by making unstructured data usable. By using multi-layered neural networks and GPU acceleration, Deep Learning allows computers to “see” images, “hear” audio, and process human language, in some tasks approaching human accuracy. It is the foundational architecture behind driver-assistance systems, facial recognition, and Generative AI.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.