EXPERT COURSES

Deep learning

Bootcamp (10 weeks)
NEXT START DATE
03 May 2022

Deep learning training content

Fundamentals of Deep Learning

Keras, Convolutional Neural Networks

TensorFlow and application

TensorFlow, Application to speech recognition

Deep Learning for time series

Preprocessing and feature engineering, regression and classification of time series

Deep Learning methodology and application

Deep Learning for face detection

TensorFlow and application

TensorFlow, Application to speech recognition

Deep Learning methodology and application

Deep Learning for face detection

The objectives of the training

Acquire

A solid foundation in the Keras and Tensorflow libraries.

Understanding

How a neural network works

Build

A speech recognition model

Discover

Time series classification problematics

Complementary training

Complementary training

Computer Vision

Want to know what’s behind facial recognition
and the autonomous car?
NEXT START DATE
10 january 2022

Complementary training

Natural Language processing

Want to understand how Alexa or Siri works and implement your own sentiment analysis algorithm?
NEXT START DATE
05 April 2022

Training rates

The Fundamentals
of Deep Learning

1500€

Deep Learning Fundamentals of Deep Learning + CV or NLP

4000€

Deep Learning Fundamentals of Deep Learning + NLP + CV

6000€

You have questions ? We have the answers.

Deep learning belongs to the field of artificial intelligence and machine learning. However, it was only with the onset of big data that deep learning research experienced real growth, with the help of companies.

Deep Learning or deep learning  is part of a family of  machine learning methods  based on learning data models.

Deep learning  works by stage  or  step . At each step, the incorrect answers are removed and the system returns to the previous steps to correctly parameterize its model. Gradually, the program reorganizes the information into more complex groups.

When this deep learning model is used on other cases, it is able to recognize if it corresponds to what it has learned previously. For example, if the program has learned to recognize a house, it will be able to say afterwards if a house is present or not on an image.

In addition, to be able to identify a house, the algorithm must be able to distinguish between the different types of dwellings, what is a house and what is not, and to recognize it regardless of the angle in which it is shown to the algorithm.

Indeed, deep learning is perfected with lived experiences and therefore becomes more and more efficient as it studies new systems. To do this, it is important to make him do as many training sessions as possible.

To return to the example of the house, it will be necessary to present him with a maximum of images of houses and to integrate images which are not so that he is able to differentiate the correct from the incorrect. These images are then transformed into data and put on the network. The algorithm will then compare this answer to the correct answers given by humans. If the answers are similar then the system saves this success in memory and can use it later to recognize the houses without error. If not, the system remembers the error and adjusts the algorithm to prevent it from happening again. The process is repeated many, many times until he is able to always recognize the house in a photo. 

One of the main advantages of Deep Learning is  the quality of the results obtained . Thanks to high-quality data, Deep Learning allows its users to do everyday tasks much more easily.

There are other types of learning, but these very often require the intervention of humans to analyze the raw data and to add additional information so that the predictive power of the algorithm is higher.

On the contrary, in deep learning, it is the algorithm itself that is able to identify the data and integrate it into its learning model: it is this quality in particular that gives it its power. Thus, it is not necessary to involve a qualified human to guarantee the development of its functionalities, which constitutes a real economic gain.

In addition, it was previously required to insert large amounts of data oneself to enable machine learning. With deep learning, this phase is much shorter and this is a real advantage: in fact, companies obtain very large amounts of data every day, but this data is very rarely structured. In this sense, deep learning is the only one capable of analyzing different sources of unstructured data depending on the type of task to be performed.

Finally, to say that Deep Learning is too expensive for mass production is incorrect.

Indeed, more and more services give companies the possibility to rely on existing algorithms rather than having to develop them from scratch. It is these strengths that allow deep learning to establish itself in the business world.

Thus, deep learning is used in many areas such  as image recognition  (which allows your phone, for example, to recognize faces and sort your photos according to the people who are on them),  automatic translation ,  recommendations personalized ,  live chats . 

The Deep Learning expert is both a researcher and a computer scientist . He develops computer programs capable of thinking and performing tasks performed by humans.

The Deep Learning expert first analyzes the functioning of the human brain in response to a given problem. He then designs complex and innovative computer programs capable of decoding and analyzing unstructured data based on innovative mathematical models.

The applications of Deep Learning, and artificial intelligence, are multiple and almost infinite: image and video processing, language-related applications, predictive analysis, games, automation, robots, health and bioinformatics…

Its development continues and intensifies thanks to big data and the ever-increasing performance of our computers and algorithms. According to Statista (a market research website), the main investments in AI will be in image recognition applications, natural language processing as well as the use of algorithms to improve financial performance and the processing of medical data by 2025. This means that the Deep Learning expert has a bright future ahead of him.

Computer Vision and Natural Language Processing are at the forefront of what a Deep Learning and AI engineer does. In this sense, this training offers the best way to specialize in an area that lacks the experts it deserves to have.

Today, if you want to develop artificial intelligence, you must understand and master Deep Learning . Thus, training in Deep Learning has become a major asset in the job market.

According to a recent survey by Deloitte, 96% of employees believe that AI will radically change their organization in less than 5 years [ source ]. In addition, investments in AI are close to 17 billion dollars in 2020, and should double in the next 2 years. [ source ]

In view of these figures, companies need highly qualified people on Deep Learning topics in addition to traditional data scientists. That’s why we launched the Deep Learning Expert Course, keeping in mind that this sector is prone to change and progress all the time.

In the profession of Data Scientist , it seems essential today to have a perfect knowledge of Deep Learning, its use and its advantages. Indeed, Data Scientists are often asked to be able to master Deep Learning tools such as  Tensorflow  and  Keras . It is possible to train through specialized distance learning. This is why  DataScientest  has set up a  40-hour module dedicated to Deep Learning  in its  data scientist training . 

The curriculum works with blocks separated into different modules that will help you master the skills needed to work as an expert in Deep Learning.
After several different studies that we have carried out with our different communities, our experts have built a path that exactly meets the objectives of recruiters.

During the training, our two courses (NLP and Computer Vision) have a total of 100 hours separated into two parts. 85% of the work will be done via an online personalized coaching platform (asynchronous). The remaining 15% will be masterclasses with conferences led by our teaching team.
In addition, during the course, you will have a project on which you will work to validate the skills you have acquired which will allow you to be fully operational at the end of the training.

The degree course is available as a continuous course requiring 10 hours of work per week. Make an appointment to find out more. 

Obviously ! And who better to provide support than our teachers, who also designed the program. They are available and attentive to any questions , whether theoretical or practical.
They also follow the progress of learners closely so that everyone is neither neglected nor demotivated.

Each disconnection of a certain duration will be communicated to your cohort manager who will then hear from you so as not to leave you in difficulty!

Finally, our qualified teachers are responsible for correcting the exams and defenses by hand, so that each of our learners progresses effectively.
DataScientest is convinced that quality learning requires personalized follow-up!

To discover the Bootcamp course seen by a learner, discover this article.

During the training, you will use the tools you have learned to put into practice during the realization of a project . The Computer Vision and Natural Language Processing blocks both have their own dedicated projects. 

Your cohort leader will present you with a list of subjects, and you can position yourself on the one that meets your desires and interests.

You will start from scratch without clean databases and pre-trained models. With our teachers, you will progress step by step on this project during the entire course.

This is one of the most important parts of the course, and you will be accompanied by a project manager to ensure that progress is smooth.

This will give you operational experience which is one of the most sought after parts of a Deep Learning expert. 

In data, each profession will have its specificities. One thing is common to all, it is the need to exchange and communicate on the use of data. Your work is part of an orderly process based on a common data culture and efficient information transfer.

This is why we offer workshops allowing you to develop your soft-skills. Among these you will find in particular:

  • Data classes around project management or management tools that are now part of the syllabus 
  • For those who wish, you will have the opportunity to participate in CV workshops and career coaching with career managers. 
French leader in Data Science training, DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. This confidence has forged the recognition of his diplomas.

Jobs in artificial intelligence, and more specifically experts in Deep Learning, are jobs sought after by recruiters.

There is currently a strong need for qualified experts. Large companies are increasingly aware of the importance of these experts to ensure that data is handled correctly.

Today, every industry is vying for the best deep learning talent. Artificial intelligence applications are used in all areas, from education to health, even industry or IT. The uses are varied, image and speech recognition, risk management, fraud prevention, customer knowledge, etc.

As the Deep Learning expert program is recent, we do not have exact figures concerning professional integration. On the other hand, the training should follow the same trend as our other training (if you want to know more, click here ) and therefore have an average return to employment of 85%. 

Deep Learning is not an easy tool to master. To apply for this course, there are two options. Either you have taken our Data Scientist training (are you interested? click here ), or you have acquired programming skills in Python and Machine Learning through other academic and professional experiences.

This means that you must be a Data Scientist and above all that you are proficient in Python. Also, it’s a plus if you know and understand most Machine Learning libraries and have a minimum of basic Deep Learning knowledge.
If you think you need an upgrade or you don’t master all the necessary prerequisites, don’t panic, our common core has been designed for that! Indeed, upstream of the training you will have the opportunity to follow the common core which will offer you all the necessary bases to follow our training in Deep Learning.

You think you have the necessary prerequisites and you want to know more about the training?
Chat with one of our advisors 

The first step would be to make an appointment with our advisors. Attentive and very attentive, they will answer all your requests and questions.

You can schedule a call with one of the team members

The team will discuss with you your training project as well as your motivation and will find a viable financial operation.
You will then take an admission test which will allow us to verify that you have the prerequisites to follow the Deep Learning expert course. The test will be a timed technical test including technical and theoretical knowledge.

Throughout the process, you are in no way financially committed to DataScientest and you can end the process at any time.
Once these steps have been validated by DataScientest, you will be able to join the next cohort. 

DataScientest is the only organization to offer distance learning that is also hybrid, that is to say both with synchronous and asynchronous times. This translates into 85% learning on the coached platform and 15% videoconference session, with the aim of combining flexibility and rigor.
It is a carefully considered choice that motivates our pedagogy to allow learning to be carried out with motivation.

Depending on the modules you choose, you will increase your skills in Natural Language Processing and/or Computer Vision. These skills are essential for R&D in artificial intelligence, and are the most sought after on the market. This means that you can work in the fields of AI R&D, Deep Learning and more generally as an experienced Data Scientist.
Do not hesitate to contact us here for more information and more details on the careers you could undertake after this training. 

Beta tests are available for our alumni in order to gain knowledge in Data Science even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. 

To keep in touch and allow former students to communicate with each other, DataScientest has set up a group of alumni on LinkedIn who share and discuss various themes around Data Science.

The DatAlumni community is a LinkedIn community that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope that will connect alumni, it will include the company and the position of each.

Initially, DataScientest supported the data transition of companies. This has made it possible to create strong links between the major groups which have ensured the growth of our structure.

Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted with 30 of the 40 CAC 40 groups. Even with severe budgetary constraints, only 4% believe that they would reduce their data scientist workforce: by comparison 28% would still seek to increase their number by more than 20%…

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni. All the partner companies undertake to include all our students at the end of their training in their recruitment process: this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

Not only can we help you, but we are also in an ideal position to do so and make your professional integration a success.

Deep learning belongs to the field of artificial intelligence and machine learning. However, it was only with the onset of big data that deep learning research experienced real growth, with the help of companies.

Deep Learning or deep learning  is part of a family of  machine learning methods  based on learning data models.

Deep learning  works by stage  or  step . At each step, the incorrect answers are removed and the system returns to the previous steps to correctly parameterize its model. Gradually, the program reorganizes the information into more complex groups.

When this deep learning model is used on other cases, it is able to recognize if it corresponds to what it has learned previously. For example, if the program has learned to recognize a house, it will be able to say afterwards if a house is present or not on an image.

In addition, to be able to identify a house, the algorithm must be able to distinguish between the different types of dwellings, what is a house and what is not, and to recognize it regardless of the angle in which it is shown to the algorithm.

Indeed, deep learning is perfected with lived experiences and therefore becomes more and more efficient as it studies new systems. To do this, it is important to make him do as many training sessions as possible.

To return to the example of the house, it will be necessary to present him with a maximum of images of houses and to integrate images which are not so that he is able to differentiate the correct from the incorrect. These images are then transformed into data and put on the network. The algorithm will then compare this answer to the correct answers given by humans. If the answers are similar then the system saves this success in memory and can use it later to recognize the houses without error. If not, the system remembers the error and adjusts the algorithm to prevent it from happening again. The process is repeated many, many times until he is able to always recognize the house in a photo. 

One of the main advantages of Deep Learning is  the quality of the results obtained . Thanks to high-quality data, Deep Learning allows its users to do everyday tasks much more easily.

There are other types of learning, but these very often require the intervention of humans to analyze the raw data and to add additional information so that the predictive power of the algorithm is higher.

On the contrary, in deep learning, it is the algorithm itself that is able to identify the data and integrate it into its learning model: it is this quality in particular that gives it its power. Thus, it is not necessary to involve a qualified human to guarantee the development of its functionalities, which constitutes a real economic gain.

In addition, it was previously required to insert large amounts of data oneself to enable machine learning. With deep learning, this phase is much shorter and this is a real advantage: in fact, companies obtain very large amounts of data every day, but this data is very rarely structured. In this sense, deep learning is the only one capable of analyzing different sources of unstructured data depending on the type of task to be performed.

Finally, to say that Deep Learning is too expensive for mass production is incorrect.

Indeed, more and more services give companies the possibility to rely on existing algorithms rather than having to develop them from scratch. It is these strengths that allow deep learning to establish itself in the business world.

Thus, deep learning is used in many areas such  as image recognition  (which allows your phone, for example, to recognize faces and sort your photos according to the people who are on them),  automatic translation ,  recommendations personalized ,  live chats . 

The Deep Learning expert is both a researcher and a computer scientist . He develops computer programs capable of thinking and performing tasks performed by humans.

The Deep Learning expert first analyzes the functioning of the human brain in response to a given problem. He then designs complex and innovative computer programs capable of decoding and analyzing unstructured data based on innovative mathematical models.

The applications of Deep Learning, and artificial intelligence, are multiple and almost infinite: image and video processing, language-related applications, predictive analysis, games, automation, robots, health and bioinformatics…

Its development continues and intensifies thanks to big data and the ever-increasing performance of our computers and algorithms. According to Statista (a market research website), the main investments in AI will be in image recognition applications, natural language processing as well as the use of algorithms to improve financial performance and the processing of medical data by 2025. This means that the Deep Learning expert has a bright future ahead of him.

Computer Vision and Natural Language Processing are at the forefront of what a Deep Learning and AI engineer does. In this sense, this training offers the best way to specialize in an area that lacks the experts it deserves to have.

Today, if you want to develop artificial intelligence, you must understand and master Deep Learning . Thus, training in Deep Learning has become a major asset in the job market.

According to a recent survey by Deloitte, 96% of employees believe that AI will radically change their organization in less than 5 years [ source ]. In addition, investments in AI are close to 17 billion dollars in 2020, and should double in the next 2 years. [ source ]

In view of these figures, companies need highly qualified people on Deep Learning topics in addition to traditional data scientists. That’s why we launched the Deep Learning Expert Course, keeping in mind that this sector is prone to change and progress all the time.

In the profession of Data Scientist , it seems essential today to have a perfect knowledge of Deep Learning, its use and its advantages. Indeed, Data Scientists are often asked to be able to master Deep Learning tools such as  Tensorflow  and  Keras . It is possible to train through specialized distance learning. This is why  DataScientest  has set up a  40-hour module dedicated to Deep Learning  in its  data scientist training . 

The curriculum works with blocks separated into different modules that will help you master the skills needed to work as an expert in Deep Learning.
After several different studies that we have carried out with our different communities, our experts have built a path that exactly meets the objectives of recruiters.

During the training, our two courses (NLP and Computer Vision) have a total of 100 hours separated into two parts. 85% of the work will be done via an online personalized coaching platform (asynchronous). The remaining 15% will be masterclasses with conferences led by our teaching team.
In addition, during the course, you will have a project on which you will work to validate the skills you have acquired which will allow you to be fully operational at the end of the training.

The degree course is available as a continuous course requiring 10 hours of work per week. Make an appointment to find out more. 

Obviously ! And who better to provide support than our teachers, who also designed the program. They are available and attentive to any questions , whether theoretical or practical.
They also follow the progress of learners closely so that everyone is neither neglected nor demotivated.

Each disconnection of a certain duration will be communicated to your cohort manager who will then hear from you so as not to leave you in difficulty!

Finally, our qualified teachers are responsible for correcting the exams and defenses by hand, so that each of our learners progresses effectively.
DataScientest is convinced that quality learning requires personalized follow-up!

To discover the Bootcamp course seen by a learner, discover this article.

During the training, you will use the tools you have learned to put into practice during the realization of a project . The Computer Vision and Natural Language Processing blocks both have their own dedicated projects. 

Your cohort leader will present you with a list of subjects, and you can position yourself on the one that meets your desires and interests.

You will start from scratch without clean databases and pre-trained models. With our teachers, you will progress step by step on this project during the entire course.

This is one of the most important parts of the course, and you will be accompanied by a project manager to ensure that progress is smooth.

This will give you operational experience which is one of the most sought after parts of a Deep Learning expert. 

In data, each profession will have its specificities. One thing is common to all, it is the need to exchange and communicate on the use of data. Your work is part of an orderly process based on a common data culture and efficient information transfer.

This is why we offer workshops allowing you to develop your soft-skills. Among these you will find in particular:

  • Data classes around project management or management tools that are now part of the syllabus 
  • For those who wish, you will have the opportunity to participate in CV workshops and career coaching with career managers. 
French leader in Data Science training, DataScientest enjoys a great reputation among companies who entrust it with the data science training of their teams. This confidence has forged the recognition of his diplomas.

Jobs in artificial intelligence, and more specifically experts in Deep Learning, are jobs sought after by recruiters.

There is currently a strong need for qualified experts. Large companies are increasingly aware of the importance of these experts to ensure that data is handled correctly.

Today, every industry is vying for the best deep learning talent. Artificial intelligence applications are used in all areas, from education to health, even industry or IT. The uses are varied, image and speech recognition, risk management, fraud prevention, customer knowledge, etc.

As the Deep Learning expert program is recent, we do not have exact figures concerning professional integration. On the other hand, the training should follow the same trend as our other training (if you want to know more, click here ) and therefore have an average return to employment of 85%. 

Deep Learning is not an easy tool to master. To apply for this course, there are two options. Either you have taken our Data Scientist training (are you interested? click here ), or you have acquired programming skills in Python and Machine Learning through other academic and professional experiences.

This means that you must be a Data Scientist and above all that you are proficient in Python. Also, it’s a plus if you know and understand most Machine Learning libraries and have a minimum of basic Deep Learning knowledge.
If you think you need an upgrade or you don’t master all the necessary prerequisites, don’t panic, our common core has been designed for that! Indeed, upstream of the training you will have the opportunity to follow the common core which will offer you all the necessary bases to follow our training in Deep Learning.

You think you have the necessary prerequisites and you want to know more about the training?
Chat with one of our advisors 

The first step would be to make an appointment with our advisors. Attentive and very attentive, they will answer all your requests and questions.

You can schedule a call with one of the team members

The team will discuss with you your training project as well as your motivation and will find a viable financial operation.
You will then take an admission test which will allow us to verify that you have the prerequisites to follow the Deep Learning expert course. The test will be a timed technical test including technical and theoretical knowledge.

Throughout the process, you are in no way financially committed to DataScientest and you can end the process at any time.
Once these steps have been validated by DataScientest, you will be able to join the next cohort. 

DataScientest is the only organization to offer distance learning that is also hybrid, that is to say both with synchronous and asynchronous times. This translates into 85% learning on the coached platform and 15% videoconference session, with the aim of combining flexibility and rigor.
It is a carefully considered choice that motivates our pedagogy to allow learning to be carried out with motivation.

Depending on the modules you choose, you will increase your skills in Natural Language Processing and/or Computer Vision. These skills are essential for R&D in artificial intelligence, and are the most sought after on the market. This means that you can work in the fields of AI R&D, Deep Learning and more generally as an experienced Data Scientist.
Do not hesitate to contact us here for more information and more details on the careers you could undertake after this training. 

Beta tests are available for our alumni in order to gain knowledge in Data Science even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. 

To keep in touch and allow former students to communicate with each other, DataScientest has set up a group of alumni on LinkedIn who share and discuss various themes around Data Science.

The DatAlumni community is a LinkedIn community that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope that will connect alumni, it will include the company and the position of each.

Initially, DataScientest supported the data transition of companies. This has made it possible to create strong links between the major groups which have ensured the growth of our structure.

Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted with 30 of the 40 CAC 40 groups. Even with severe budgetary constraints, only 4% believe that they would reduce their data scientist workforce: by comparison 28% would still seek to increase their number by more than 20%…

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni. All the partner companies undertake to include all our students at the end of their training in their recruitment process: this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

Not only can we help you, but we are also in an ideal position to do so and make your professional integration a success.

Are you interested?