Deep learning: What is it? Why is this an increasing trend in machine learning?

Deep learning is a subfield of machine learning that is inspired by the structure and function of the brain, known as artificial neural networks. It involves training artificial neural networks on a large dataset, allowing the network to learn and make intelligent decisions on its own. Deep learning algorithms have been used for a variety of tasks, including image and speech recognition, natural language processing, and even playing games.

In deep learning, a neural network is composed of layers of interconnected nodes, each of which performs a simple mathematical operation. The input to the network is processed through these layers, with the output of each layer serving as the input to the next layer, until finally producing a final output. The parameters of the network, such as the weights of the connections between nodes, are optimized during training to minimize a loss function that measures the difference between the network’s predictions and the actual labels in the training data.

The term “deep” in deep learning refers to the many layers in these neural networks, as opposed to shallow machine learning algorithms that have only a few layers. The depth of the network allows it to learn and represent very complex patterns in the data, making it well-suited for tasks that require high-level abstraction and reasoning.

What is deep learning
photo: Pixabay

Why is “Deep learning” a growing trend in machine learning?

Deep learning is a growing trend in machine learning for several reasons:
  • Increased computational power: Advances in hardware, such as GPUs, have made it possible to train large deep learning models on large datasets, which was previously infeasible.
  • Large amounts of data: With the growth of the internet and the increasing use of connected devices, the amount of data being generated has grown exponentially, providing a wealth of information for training deep learning models.
  • Improved algorithms: Researchers have developed new deep learning algorithms and architectures that have significantly improved the performance of deep learning models on a variety of tasks, such as image and speech recognition.
  • Successful real-world applications: The success of deep learning in various real-world applications, such as self-driving cars and virtual personal assistants, has increased its popularity and demonstrated its potential to solve complex problems.
  • Interdisciplinary collaborations: Deep learning has also benefited from interdisciplinary collaborations between researchers in fields such as computer science, neuroscience, and psychology, leading to a better understanding of how the brain works and how to build more advanced deep learning models.

In summary, the combination of increased computational power, access to large amounts of data, improved algorithms, successful real-world applications, and interdisciplinary collaborations has driven the growth of deep learning in recent years.

Why not use SVMs?

Support Vector Machines (SVMs) are a popular class of machine learning algorithms that can be used for classification and regression tasks. However, there are several reasons why deep learning has become a more popular choice than SVMs in recent years:
  1. Better performance on complex tasks: Deep learning algorithms are well-suited for tasks that require high-level abstraction and reasoning, such as image and speech recognition. In comparison, SVMs are not as effective at learning and representing complex patterns in the data, and they may not perform as well on these tasks.
  2. Ability to handle large amounts of data: Deep learning algorithms are designed to handle large amounts of data and can be trained on large datasets, which is particularly important for many real-world applications. In comparison, SVMs can become computationally expensive and may struggle to scale to large datasets.
  3. Better representation learning: Deep learning algorithms can learn to extract and represent features from the data automatically, which can be useful for tasks where the features are not well understood. SVMs, on the other hand, rely on manually-engineered features and may not perform as well if the features are not well designed.
  4. More interpretable models: While some deep learning models can be difficult to interpret, recent developments in interpretable deep learning have made it possible to build models that are more transparent and easier to understand. In comparison, SVMs can be less transparent and may not provide as much insight into how they are making decisions.

That being said, there are still many cases where SVMs can be a good choice, especially for smaller datasets and simple tasks. The choice between deep learning and SVMs will depend on the specific requirements of each task and the resources available for training and deploying the model.

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