What is Deep Learning?

 

Deep learning refers to complicated scientific principles and techniques utilised in the field of artificial intelligence, or AI. It is evolving in response to the constant influx of digital information across cyberspace. In this post, we explain what deeplearning is and its fundamental concepts, as well as the distinctions between machine learning and deep learning and also, we at Saiwa provide several types of services based on deep learning.

What is deep learning?

Machine learning includes deeplearning. It is a branch of computer science that focuses on self-learning and improvement via the study of computer algorithms. Deep learning, as opposed to machine learning, makes use of artificial neural networks designed to replicate how humans think and learn. Until recently, the complexity of neural networks was restricted owing to processing power constraints, but advances in big data analysis have allowed for larger and more complicated neural networks, and computers now have the capacity to monitor, learn, and respond swiftly in complex circumstances. Deep learning has made important contributions to picture classification, language translation, and speech recognition, and it may also be used to address diagnostic issues without the need for human interaction.

Deep learning is powered by multi-layer artificial neural networks. Deep Neural Networks (DNNs) are networks that can execute complex operations such as representation and abstraction to make sense of pictures, sounds, and text. Deep learning, the fastest-growing subject in machine learning, is a really disruptive digital technology that an increasing number of businesses are using to establish new business models. We are in Saiwa As AI experts, we always encounter other researchers with very valuable experimental data rigorously collected in laboratories, but time and resource constraints limit the viability of applying machine learning or computer vision methods on these data.

A brief history of deep learning

 

Machine learning was inspired by the mathematical modelling of neural networks. In 1943, logician Walter Pitts and neurologist Warren McCulloch published an essay in which they attempted to systematically map out mental processes and decision-making in human cognition.

In 1950, Alan Turing created the Turing Test, which became the litmus test for deciding which machines were "intelligent" or "unintelligent." To be regarded as "intelligent," a machine had to be able to persuade a human being that it, too, was a human being. Soon after, a Dartmouth College summer research programme was recognised as the birthplace of AI.

From this point on, "intelligent" machine learning algorithms and computer programmes that could do activities ranging from arranging salespeople's travel routes to playing board games with humans, such as checkers, began to develop.

Intelligent robots have gone on to accomplish everything from educate newborns how to pronounce words to defeating a global chess champion.

 

How does deep learning work?

 

Deep learning models are sometimes referred to as "deep neural networks" since most deep learning approaches use neural network topologies.

 

The term "deep" is commonly used to refer to the number of hidden layers in a neural network. Deep networks can have up to 150 hidden layers, whereas traditional neural networks only have 2-3. Deep learning models are taught utilizing huge volumes of labelled data and neural network topologies that learn features directly from the data, hence eliminating the requirement for human feature extraction.

Convolutional neural networks (CNN or ConvNet) are a type of deep neural network that is widely used. A CNN convolutionally layers learnt features with incoming data, making it ideal for processing 2D data like as photos here in Saiwa two CNNs are provided for training on user specific dataset.

CNNs reduce the requirement for manual feature extraction, which means you won't have to identify image-classification characteristics. CNN pulls characteristics from photographs directly. The critical characteristics are not pre-trained; rather, they are discovered when the network trains on a batch of pictures. Because of automated feature extraction, deep learning models are particularly successful for computer vision applications such as object classification. CNNs learn to distinguish different visual cues through the use of tens or hundreds of hidden layers. Every hidden layer adds to the complexity of the picture characteristics learnt. For example, the first hidden layer may learn to recognize edges, whereas the last layer learns to detect more sophisticated shapes specific to the geometry of the thing we're aiming to recognise.

 

Types of algorithms used in deep learning

 

Machine learning is a hot topic in academic and industrial, with new techniques emerging all the time. Because of the field's pace and intricacy, professionals struggle to keep up with new strategies, while newbies may become overwhelmed.

A machine learning model, also called as a "model," is a mathematical representation that describes data in the context of a problem, typically a commercial challenge.

 

The top 10 most common deep learning algorithms are as follows:

1. Neural Networks using Convolutions (CNNs)
2. Networks of Long-Term Memory (LSTMs)
3. Recurrent Neural Networks (RNNs) (RNNs)
4. Adversarial Generative Networks (GANs)
5. Networks using Radial Basis Functions (RBFNs)
6. Perceptron’s with Multiple Layers (MLPs)
7. Maps that organize themselves (SOMs)
8. Networks of Deep Belief (DBNs)
9. Boltzmann Machines with Restrictions (RBMs)
10. Autoencoders

 

Deep learning method at Saiwa

Deep learning is a subcategory of machine learning methods based on artificial neural networks, which automatically extract high-level features from raw input data. Convolutional neural networks (CNNs) are among the most effective and widely used deep learning architectures. As a result, in Saiwa, two CNNs are supplied for training on user-specific datasets (Detectron2 and Yolov5).

There are different options for providing training data:

1. Directly uploading from a user's computer or cloud space

2. Adding a link to a publicly accessible dataset

The dataset will be downloaded by the Saiwa team in the second way. Saiwa boundary (for Detectron2) or bounding box (for YOLOv5) annotation tools are useful for annotating images.

 

The features of the Saiwa deep learning service

The saiwa deep learning service includes the following features and facilities:

 

• Train automatically on user-specific data
• Verifying the trained model with additional services such as object detection
• Train on public datasets with little user intervention.
• Saiwa border and bounding box annotation services are supported.
• Request for annotation by the Saiwa team
• Tracking training jobs using diagrams and other data from the user panel
• After the model has reached the necessary accuracy for validation, the number of epochs can be modified.
• Download and run the trained model's online test.
• It is compatible with the SAIWA object detection and count objects service for testing the trained model.