What is Deep Learning?
Deep Learning Framework
Before exploring what deep learning is, it is a good idea comprehending its overall surrounding structure. We can simplify this as follows: artificial intelligence (AI), machine learning (ML), neural networks (ANNs), and then deep learning (DL).
In a nutshell:
- Artificial intelligence (AI) enables machine mimic human intelligence.
- Machine learning (ML) is a subset of AI, dealing with algorithms and data.
- Neural networks (ANNs) tackle problems by recognising patterns.
- Deep learning (DL) are complex multi-layered neural networks.
The following is a simple representation of the above framework.
What is deep learning?
Now, let’s define the term.
Deep learning is a subset of machine learning, which is itself a branch of artificial intelligence (AI). It’s inspired by the structure and function of the human brain, particularly the interconnected network of neurons. DL algorithms attempt to mimic the way the human brain works. It does so by using layers of interconnected nodes, called artificial neural networks, to learn from large amounts of data.
Deep refers to the multiple layers through which data is processed in these neural networks. Each layer of nodes processes information, extracting progressively higher-level features from the raw input data. Deep learning models can automatically discover patterns, features, and representations of the data without being explicitly programmed to do so. This capability makes them powerful tools for tasks such as image and speech recognition, natural language processing, and even playing strategic games.
DL has gained significant attention and popularity due to its remarkable performance in various domains. Particularly, with the availability of large datasets and advances in computational power, such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). As a result, training of deep neural networks is accelerated.
Components of deep learning networks
Deep learning networks consist of several key components, each playing a crucial role in the network’s functionality. There are three main sections: input layer, hidden layers, and output layer. More on neural networks here.
- Input layer introduces the data to the network. Each node in the input layer represents a feature or attribute of the input data.
- Hidden layers are the layers between the input and output layers. They perform the bulk of the computation in the network.
- Output layer produces the final output of the network. The number of nodes in the output layer depends on the type of problem the network is solving.
Below is a basic representation of a neural network. Generally, deep learning networks have multiple hidden layers in the middle.
Challenges of DL
Deep learning has shown remarkable success in various domains, but it also comes with several challenges. The main DL challenges are data and power. Data refers to the quality and quantity of data. Power refers to the need of high computational resources.
- Data Quality and Quantity: DL models require large amounts of labelled data to learn meaningful patterns effectively. Obtaining high-quality labelled data can be expensive and time-consuming. Especially, in domains where data annotation requires domain expertise.
- Computational Resources: Training DL models, especially large-scale ones, demands substantial computational resources. This includes powerful GPUs or TPUs as well as large memory capacities. This requirement can be a barrier for individuals or organisations with limited access to such resources.
