Are you intrested in deep learning Today I will tell you what is deep learning and what are basics, applications and algorithm of deep learning so read this article till the end I have another article on machine learning roadmap if you are intrested you can read here – Machine learning roadmap.
Literal Meaning of Deep Learning
The literal translation is deep learning, but deep learning is a subcategory of machine learning and the broader world of artificial intelligence, and it is a broader basis than “simple” learning at different machine levels. In the article below we will try to know what Deep Learning is. Basics of Deep Learning and some important applications of Deep Learning.
Deep learning is a sub field of artificial intelligence if you want to know more about future of artificial intelligence then read this guide – Artificial Intelligence A New Gateway to the Future
What is deep learning?
Deep Learning, literally translated as deep learning, is a subcategory of machine learning (literally translated as machine learning), refers to a branch of artificial intelligence, and refers to algorithms based on the structure and function of the brain, called artificial neural networks. From a scientific point of view, it can be said that deep learning is “machine” learning by using data obtained by algorithms (mainly statistical calculations).
Deep Learning Described
Deep learning (also known as deep structured learning or hierarchical learning) is essentially part of a broader series of machine learning methods that are based on the assimilation of data representations rather than algorithms that perform specific tasks.
Deep learning architecture (through which the public now draws the public’s attention to the concept of artificial neural networks) such as B. In computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics (using computer tools for certain Numerical and statistical descriptions of biological phenomena, such as gene sequence, protein composition, and structure, biochemical processes in cells, etc.).
Compilation of various interpretations by some of the most famous researchers and scientists in the field of deep learning (eg: Andrew Yan Tak Ng, Founder of Google Brain, senior scientist at Baidu, professor and director of Stanford University Artificial Intelligence Laboratory;
Researcher Ian J.Goodfellow named Boston Massachusetts Institute of Technology one of the world’s best innovators under 35; Joshua Bengio, deep learning field One of the most famous scientists; Ilya Sutskever, Research Director of OpenAI;
Jeffrey Everest Hinton, a key figure in deep learning and artificial intelligence, the first researcher to demonstrate the use of generalized backpropagation algorithms to train multilayer neural networks.
We can use deep learning as a system algorithm that uses a class of machine learning:
1) Use different levels of u non-linear blocks to enter Arcadephenomenaorm feature extraction and transformation tasks. It takes the output of the previous layer as input. Algorithms can be unsupervised. Applications include pattern analysis (unsupervised learning) and classification (supervised learning)
2) Unsupervised learning based on several levels of data features (and representation). The upper-level elements are derived from the lower-level elements to create hierarchical representations
3) Are part of a broader category of learning algorithms used for data representation in machine learning?
4) You have studied different levels of abstraction several levels of representation; these levels form a conceptual level
Artificial neural network, the basis of deep learning
As mentioned in the previous paragraph, his work in deep learning is based on the classification and “selection” of the most relevant data to obtain results, just like our biological brain answers a question to derive Make logical assumptions, solve problems, activate biological neurons, and neural connections (connected biological neurons from the neural network of our brain so that everyone can infer, perform parallel calculations, sounds, images, faces, learn and take actions).
The behavior of deep learning is similar, using artificial neural networks; a mathematical calculation model based on the working principle of biological neural networks, that is, a model composed of information relationships.
A neural network looks like an “adaptive” system that can change its structure (nodes and connections) according to the external and internal information connected and transmitted by the neural network during the learning and thinking phase.
Applications of Deep Learning
1) Automatic coloring of black and white images (for neural networks, this means identifying edges, backgrounds, details and understanding the typical colors of butterflies, such as knowing exactly where to place the correct colors)
2) Automatically adding sound to silent movies ( For deep learning systems, this means synthesizing sounds by recognizing images and actions and placing them correctly in a given situation, such as a video of a worker walking on an asphalt road with a Break jackhammer);
3) Simultaneous interpretation (Deep learning refers to understanding natural language, understanding spoken language, and translating meaning into another language)
4) Classification of objects in the photo (in this case, the system can recognize and classify everything it sees in the picture Things, even very complex objects, such as background scenery, such as mountains, people, hiking trails, grazing animals, etc.)
5) Automatic handwriting generation (deep learning system already exists, can use human handwriting, even though research and Imitate human handwriting estimates)
6) Automatic text generation (These systems have learned to write correctly in a certain language, consider spelling, punctuation, grammar, and even learn to use different writing styles according to the results to be achieved, such as news articles or short films Fairy tales)
7) Automatic subtitle generation (in this case, image recognition, context analysis, and recording functions enable the system to automatically record subtitles for images, ideally describing the scene);
8) Automatic games (we Learned to understand the potential of a system, it can play a certain game on its own, thanks to DeepMind, it is now part of Google, it developed the deep learning AlphaGo system, it not only learned to play games. It also successfully defeated World champion humans)
Deep learning framework: Why Facebook is getting attention
One of the most widely used deep learning frameworks by researchers, developers, and data scientists is TensorFlow, which is a well-known open-source machine learning software library (a project supported by Google) that provides testing and performance Create modules optimized for algorithms of different types of software and different types of programming languages, including Python, C/C++, Java, Go, RUST, R… (especially to “execute” tasks” and understand natural languages.
However, another framework began to spread in 2019, and according to some analysts (as the analyst Janakiram MSV reported in a Forbes article)
It is quickly becoming a favorite of developers and data scientists. Open-source from Facebook The project has been widely used by companies. In the first (and several years) version, Facebook developers used a framework called Caffe2, which was also adopted by many universities and researchers, but as early as 2018, Facebook announced that it would develop its own.
Different types of frameworks. The purpose of developing Caffe2 is to create a new framework that can be used by the open-source community.
Facebook combines the advantages of Caffe2 and ONNX in a new framework (PyTorch); ONNX stands for Open Neural Network Exchange, Is an interoperable platform, and Microsoft and AWS also actively participate in it by supporting Microsoft CNTK and Apache MXNet.
PyTorch 1.0 combines the advantages of Caffe2 and ONNX (this is one of the first frameworks to provide native support for ONNX models A).
The developers of Facebook (but not limited to) focus on a framework that is simpler and more accessible than TensorFlow. For example, PyTorch uses a technique called dynamic computing to easily train neural networks. “PyTorch The runtime model mimics the standard programming model known to ordinary Python developers.
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