Online or onsite, instructor-led live Neural Network training courses demonstrate through interactive discussion and hands-on practice how to construct Neural Networks using a number of mostly open-source toolkits and libraries as well as how to utilize the power of advanced hardware (GPUs) and optimization techniques involving distributed computing and big data. Our Neural Network courses are based on popular programming languages such as Python, Java, R language, and powerful libraries, including TensorFlow, Torch, Caffe, Theano and more. Our Neural Network courses cover both theory and implementation using a number of neural network implementations such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
Neural Network training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Neural Networks training can be carried out locally on customer premises in India or in NobleProg corporate training centers in India.
NobleProg -- Your Local Training Provider
Testimonials
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course: Introduction to the use of neural networks
The interactive part, tailored to our specific needs.
Thomas Stocker
Course: Introduction to the use of neural networks
I generally enjoyed the knowledgeable trainer.
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview. Good background into why Tensorflow operates as it does.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course: Neural Networks Fundamentals using TensorFlow as Example
I liked the new insights in deep machine learning.
Josip Arneric
Course: Neural Network in R
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course: Neural Network in R
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Course: Neural Network in R
Very flexible.
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
I generally enjoyed the flexibility.
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course: Neural Networks Fundamentals using TensorFlow as Example
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course: Applied AI from Scratch in Python
The trainer very easily explained difficult and advanced topics.
Leszek K
Course: Artificial Intelligence Overview
Machine Translated
Communication with lecturers
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
like it all
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
a lot of exercises that I can directly use in my work.
Alior Bank S.A.
Course: Neural Network in R
Machine Translated
Examples on real data.
Alior Bank S.A.
Course: Neural Network in R
Machine Translated
neuralnet, pROC in a loop.
Alior Bank S.A.
Course: Neural Network in R
Machine Translated
A wide range of topics covered and substantial knowledge of the leaders.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Lack
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Examples and issues discussed.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Practical exercises prepared by Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Neural Networks Subcategories in India
Neural Networks Course Outlines in India
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
Overview
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
21 hours
Overview
This instructor-led, live training in India (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.
By the end of this training, participants will be able to:
- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.
By the end of this training, participants will be able to:
- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.
7 hours
Overview
In this instructor-led, live training in India, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
7 hours
Overview
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
This course is an introduction to applying neural networks in real world problems using R-project software.
14 hours
Overview
This instructor-led, live training in India (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems.
By the end of this training, participants will be able to:
- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
By the end of this training, participants will be able to:
- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
21 hours
Overview
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
14 hours
Overview
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
28 hours
Overview
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
7 hours
Overview
The training is aimed at people who want to learn the basics of neural networks and their applications.
21 hours
Overview
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.
In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.
By the end of this training, participants will be able to:
- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.
By the end of this training, participants will be able to:
- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
14 hours
Overview
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.
By the end of this training, participants will be able to:
- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters
Audience
- Developers
- Engineers
- Domain experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters
Audience
- Developers
- Engineers
- Domain experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Overview
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
21 hours
Overview
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
14 hours
Overview
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
21 hours
Overview
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
14 hours
Overview
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
28 hours
Overview
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
28 hours
Overview
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
21 hours
Overview
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
35 hours
Overview
This course is created for people who have no previous experience in probability and statistics.
35 hours
Overview
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
-
understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
-
be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
-
understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
-
be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
Other regions in India
Other countries
Consulting in India
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