You've likely encountered the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) used frequently, sometimes interchangeably. It's helpful to understand how they relate. Think of them as nested concepts: AI is the broadest field, encompassing any technique that enables computers to mimic human intelligence. Machine Learning is a subset of AI that focuses on systems learning patterns from data without being explicitly programmed for every task. Deep Learning, in turn, is a specific subset of Machine Learning.
The relationship between Artificial Intelligence, Machine Learning, and Deep Learning. Deep Learning is a specialized field within Machine Learning, which itself is a part of the broader domain of Artificial Intelligence.
So, what makes Deep Learning "deep"? The term refers to the use of Artificial Neural Networks (ANNs) with multiple layers, sometimes hundreds or even thousands, stacked between the input and output. As introduced in the chapter context, neural networks are inspired by the structure of the human brain, composed of interconnected nodes or 'neurons'.
In traditional Machine Learning, feature engineering often requires significant domain expertise and effort. You manually identify and extract the most relevant features from the raw data to feed into your learning algorithm. For example, in an image classification task, you might engineer features like edge detectors or specific shape templates.
Deep Learning takes a different approach. Instead of relying on manually crafted features, deep neural networks learn features directly from the data in a hierarchical manner. The initial layers might learn simple patterns, like edges or textures in an image. Subsequent layers combine these simpler patterns to learn more complex features, such as shapes or object parts. The final layers combine these high-level features to make predictions, for instance, identifying a specific object in the image.
This ability to automatically learn intricate patterns and representations from large volumes of raw data is what has driven Deep Learning's success in various complex tasks, including:
The computational intensity of training these deep networks, involving potentially millions of parameters (weights and biases), has become feasible due to advancements in algorithms (like refined versions of backpropagation), the availability of massive datasets, and the parallel processing power provided by modern hardware, especially Graphics Processing Units (GPUs).
Keras, the library central to this course, provides a high-level, user-friendly interface for defining, training, and evaluating these deep neural networks. It allows you to focus on the architecture and training process without getting bogged down in low-level implementation details, integrating smoothly with backend engines like TensorFlow, PyTorch, or JAX (especially with Keras 3 onwards). As you progress through this course, you will use Keras to build and train various types of deep learning models, starting with the fundamentals covered in the upcoming sections.
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