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What is Deep Learning? Definition, Tools, Applications
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What is Deep Learning? Definition, Tools, Applications

Deep Learning exploits algorithms made up of neural networks, which aim to reproduce the actions of the human brain. Increasingly used in companies, we explain why you should follow a Deep Learning training and present you the ones we deliver at Jedha!

Deep Learning is now indispensable in the Data Science business. Its applications and advantages enable the development of artificial intelligence. Thanks to the learning methods of Deep Learning, it is possible to analyse data and solve problems that are complex to deal with using traditional approaches. A training in Deep Learnig thus allows you to better position yourself on the data market, within a large company looking for highly qualified people for their project.

Definition of Deep Learning

Deep Learning (DL) is one of the main technologies of Machine Learning. We have written an article explaining the differences between machine learning and deep learning.

It uses algorithms to reproduce the actions of the human brain using artificial neural networks.

These networks consist of several layers of neurons to interpret the data. The DL learns from experiments and data recorded with the machines.

Deep learning is a new stage in the development of artificial intelligence.

Thanks to deep learning, artificial intelligence becomes autonomous in learning new rules making it more efficient and reliable.

The exponential development of computing power and related applications allows artificial intelligence to have even more complex and dense layers of neurons.

Why train in Deep Learning?

Today, computer science is constantly evolving with a real expansion of artificial intelligence. One can take a course in deep learning because one is passionate about technology or mathematics and wants to deepen one's knowledge to work on a personal project. A course in data science at university often involves a master's degree in computer science. Other practical courses, whether distance learning or not, are offered by professionals to share their knowledge and skills.

The main objective of studying to become an expert in deep learning and artificial intelligence is to get a job in Big Data. The employability offer in the data professions is indeed considerable. These new data processing systems are very useful in companies.

For example, deep learning can improve productivity by automating previously tedious and time-consuming processes.

This will allow the company to focus on higher value-added tasks. Training in data science and deep learning allows you to quickly find a well-paid job or to consider a career change.

Deep Learning training at Jedha

Our Data Science training courses fall into three categories: Data Essentials, DataFullstack and Data Lead.

The Data Essentials course is at a beginner level. The courses start from basic data analysis to learning algorithms and implementing a project. It includes a module on data visualisation, programming languages, Deep Learning, machine learning and artificial intelligence...

With the Fullstack course, students deepen their knowledge to become autonomous in all aspects of a Data project, especially in Deep Learning. They obtain the "Data Science Developer Certificate" to start a career.

As for the Lead training, it is a program that allows the management of complex Big Data infrastructures, the mastery of all the skills of a Data Engineer and the methods he uses. It is the perfect training to become a Data Engineer.

Our trainings are very practical and built by professionals with excellent backgrounds in Data. Delivered face-to-face or remote, full or part-time, our Jedha deep learning courses are suitable for everyone.

DL algorithms

The systems used in deep learning are of different types. Deep Learning algorithms are neural networks that allow the processing of specific data from a set of data.

Convolutional Neural Networks (CNN)

CNNs are composed of several layers for processing and extracting features from data. Also known as ConvNets, they are generally used for image processing and object detection. They are used for satellite image recognition, time series prediction, medical image processing and anomaly identification.

Recurrent Neural Networks (RNN)

RNNs (Recurrent Neural Networks) are networks that allow the use of LSTM (Long short-term memory) outputs as inputs for the current phase, with the aim of memorising previous inputs. This network is used for image subtitling, machine translation, handwriting recognition, natural language processing, etc.

Radial Basic Function Networks (RBFNs)

RBFNs (Radial-basis function networks) will rely on radial-basis functions for their activation functions. These are feedforward neural networks used in data science for classification, regression and prediction of time series.

Long and short term memory networks (LSTM)

Derived from RNNs, LSTMs can learn and remember dependencies over the long term. By default, they store past data for long periods. In data science, these networks are used to predict time series, to compose musical notes, for speech recognition, etc.

Generative Adversarial Networks (GANs)

In deep learning, GANs (generative adversarial networks) are used to create new instances of data similar to the training data. These are algorithms composed of two components: a generator and a discriminator. The first component learns to generate false data while the second learns to exploit this false data. The network is used for a number of purposes, including the improvement of textures in 2D images by video game designers.

Restricted Boltzmann Machines (RBM)

MBRs are neural networks with two layers, visible and hidden units. They are able to learn from a probability distribution on a set of inputs. This deep learning algorithm is useful for classification projects, collaborative filtering, dimensionality reduction, etc. There are also auto-encoders, a type of algorithm in which the input and output are identical. This allows the data to be reproduced from the input layer to the output. It is used for popularity prediction, pharmaceutical discovery, image processing, etc.

Other algorithms used in deep learning include deep belief networks(DBNs), multilayer perceptrons(MLPs) and self-organising maps(SOMs). DBNs are a set of Boltzmann machines with connections between layers, used for image recognition, motion capture data and video recognition.

MLPs can be used to build software for speech recognition, image recognition and machine translation. They are equipped with several layers of perceptrons with activation functions.

Maps are used in data visualisation for dimension reduction using self-organising artificial neural networks. They facilitate the visualisation of data and the understanding of highly dimensional information.

Applications of Deep Learning

The development of IT, more specifically the management of big data and the use of increasingly powerful systems, has created the need for efficient methods for analysing and processing large amounts of data.

In business, the applications of Deep Learning are very diverse. Most industries benefit from advances in computing and deep learning.

In particular in the field of automation, cybersecurity, robotics, bioinformatics, etc. In the health sector, for example, applications are used to automatically diagnose a patient. In the automotive sector, artificial intelligence and its concepts are used in assisted driving. It was by using deep learning that in 2016 Google's Alpha Go model beat the best Go champions. The search engine is also increasingly turning to learning via deep learning. Today, it is even possible to create paintings from an autonomous system with deep learning.

The field of e-commerce, which produces large volumes of data, also benefits from the advantages of deep learning, particularly for better data analysis. Thanks to predictive analysis, it is possible to send suggestions to customers based on their preferences.

Deep Learning tools

The tools used in deep learning are called frameworks. Here are some examples of the main tools available on the market:

  • PyTorch,
  • TensorFlow,
  • Keras,
  • Sonnet,
  • MXNet.

Developed by Facebook, PyTorch is an open source framework based on the Torch library. Widely used for applications such as natural language processing and computer vision, it is a particularly effective tool for training, building and deploying small projects or prototypes. Owned by Google, TensorFlow is also an open source platform. Based on the JavaScript language system, this tool has a wide range of resources to facilitate training and deployment of ML/DL models.

Very easy to use and extensible, Keras is a framework written with the Python language. Allowing to write readable and accurate code, it is suitable for beginners who can easily learn and master the concepts related to deep neural networks. Sonnet is a library designed by DeepMind. Developed for a high level, this tool is used to build complex neural network structures in TensorFlow. Finally, MXNet is a lightweight, rather flexible and very scalable framework. It uses deep learning systems such as CNNs and LSTMs.

NLP (Natural Language Processing)

NLP(Natural Language Processing) refers to natural language processing. It is a part of artificial intelligence whose objective is the processing of human language, and the generation of spoken and/or written language. This involves the development of a specific computer program.

The classical computer works with a well-structured, precise and marked-up programming language. Whereas the natural language of humans is rather ambiguous. To make sure that a program can understand the meaning of words, algorithms must analyse the meaning and structure of words to make them less confusing, but also to recognise references and generate language based on the same principles.

The NLP algorithms perform several syntactic and semantic analyses for the evaluation of the meaning of a sentence according to the grammatical rules given beforehand. In order to understand the meaning and context, a comparison of the text is made in real time with the entire database. To determine the relevant correlations, these algorithms need a large amount of data. They will also exploit modern deep learning and machine learning techniques and methods such as named entity recognition, topic modelling, text intent targeting and sentiment analysis.

Deep learning is an evolution of machine learning and an evolution of artificial intelligence. Deep learning skills and knowledge are highly sought after in companies. They can be acquired during a classic university course (Master Data Science at the University of Paris-Saclay for example). However, bootcamp courses, whether distance or face-to-face, generally offer a more practical route for those wishing to study to discover or perfect their data science skills. With a bit of organisation, it is possible to become an expert and hopefully compete for a better job.

If you are interested in Deep Learning, please check out our Data Science courses 👉

Antoine Krajnc
Written by
Antoine Krajnc

Programmes, Data themes, admissions,
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