What DL is?

Deep learning is a subset of machine learning (ML) and a key technology in artificial intelligence (AI) that involves the use of neural networks with multiple layers to learn from data and make predictions or decisions.

A neural network is a type of machine learning model that is inspired by the way the human brain works, and it is composed of layers of interconnected nodes or artificial neurons. Each layer processes the input it receives and passes it to the next layer, until the final output is produced. The more layers a neural network has, the more complex and abstract features it can learn.

Deep learning refers to neural networks with multiple layers, typically more than two or three layers, which are able to learn and represent more complex and abstract features of the data. The ability of deep learning models to learn and represent complex features is what makes them powerful for a wide range of tasks such as image recognition, natural language processing, speech recognition, and many others.

Deep learning algorithms are trained using large amounts of data and powerful computing resources. The training process involves adjusting the parameters of the neural network to minimize the error between the predicted output and the true output. Once the model is trained, it can be used to make predictions or decisions on new unseen data.

Overall, deep learning is a subset of machine learning that involves the use of neural networks with multiple layers to learn from data and make predictions or decisions. The ability of deep learning models to learn and represent complex features makes them powerful for a wide range of tasks and it has been used to achieve state-of-the-art performance in many areas of AI.

Deep learning, as a representation learning, differs from traditional machine learning in the sense that it is able to learn the features from the data automatically, without requiring the intervention of a human in selecting these features.

In traditional machine learning, the features are often hand-crafted by domain experts, and this process can be time-consuming, labor-intensive and may not always lead to the best results. The features are selected based on prior knowledge and the understanding of the problem.

Deep learning, on the other hand, is able to learn the features automatically by applying multiple layers of non-linear transformations to the input data. Each layer of the neural network is able to learn increasingly complex and abstract features of the data, and this allows the model to automatically discover and extract the most relevant features for the task at hand.

In summary, traditional machine learning requires the selection of features by human experts, while deep learning is able to learn the features automatically by applying multiple layers of non-linear transformations to the input data. This ability to automatically learn the features is one of the key strengths of deep learning and it has led to breakthroughs in many areas of AI.