Deep learning is a subset of machine learning that mimics the structure and functioning of the human brain. It acquires knowledge from unstructured data and employs intricate algorithms to train artificial neural networks.
In deep learning, the primary focus is on neural networks, a cornerstone of Artificial Intelligence. These networks are trained to identify and process diverse data types, including text, numbers, images, and speech. Unlike traditional machine learning, deep learning deals with considerably more complex, diverse, and unstructured data, such as images, audio, and text files.
Central to deep learning is the neural network, typically resembling the illustrated image below:
As depicted earlier, the neural network comprises an Input Layer, an Output Layer, and multiple Hidden Layers sandwiched in between. In any neural network, a minimum of one hidden layer is present, and a network is considered “deep” when it contains more than one hidden layer.
Now, let’s delve deeper into an in-depth examination of these various layers.
Input Layer: acts as the initial point for processing large amounts of data within the neural network. This data can take various forms, such as text, images, audio, and more.
Hidden Layer: is where complex computations occur to process the data and extract essential features. During the training process, these layers incorporate weights and biases, which continually adjust until training is complete. Each neuron in this layer has multiple weights and a single bias. After computation, the resulting values are passed on to the output layer.
Output Layer: produce predictions by applying appropriate activation functions. These predictions can take the form of numerical or categorical values.
Deep Learning Libraries
Several readily available libraries are essential for conducting machine learning and deep learning tasks. Below are some of the most widely used frameworks:
- Theano
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