Neural Networks Training Courses

Neural Networks Training Courses

Local, 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 "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Sweden or in NobleProg corporate training centers in Sweden. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

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Neural Networks Course Outlines

CodeNameDurationOverview
aiintArtificial Intelligence Overview7 hoursThis 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.
neuralnetIntroduction to the use of neural networks7 hoursThe training is aimed at people who want to learn the basics of neural networks and their applications.
rneuralnetNeural Network in R14 hoursThis course is an introduction to applying neural networks in real world problems using R-project software.
appliedmlApplied Machine Learning14 hoursThis 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.
annmldtArtificial Neural Networks, Machine Learning, Deep Thinking21 hoursArtificial 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.
aiintrozeroFrom Zero to AI35 hoursThis course is created for people who have no previous experience in probability and statistics.
aiautoArtificial Intelligence in Automotive14 hoursThis 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.
NeuralnettfNeural Networks Fundamentals using TensorFlow as Example28 hoursThis 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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
datamodelingPattern Recognition35 hoursThis 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.

Audience
Data analysts
PhD students, researchers and practitioners
OpenNNOpenNN: Implementing Neural Networks14 hoursOpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning.

In this course we go over the principles of neural networks and use OpenNN to implement a sample application.

Audience
Software developers and programmers wishing to create Deep Learning applications.

Format of the course
Lecture and discussion coupled with hands-on exercises.
FairsecFairsec: Setting Up a CNN-Based Machine Translation System7 hoursFairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements.

Audience

- Localization specialists with a technical background
- Global content managers
- Localization engineers
- Software developers in charge of implementing global content solutions

Format of the course

- Part lecture, part discussion, heavy hands-on practice
FairseqFairseq: Setting up a CNN-based machine translation system7 hoursFairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

- Localization specialists with a technical background
- Global content managers
- Localization engineers
- Software developers in charge of implementing global content solutions

Format of the course
Part lecture, part discussion, heavy hands-on practice

Note

- If you wish to use specific source and target language content, please contact us to arrange.
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing Units7 hoursThe Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision.

In this instructor-led, live training, 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

Audience

- Developers
- Researchers
- Engineers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
MicrosoftCognitiveToolkitMicrosoft Cognitive Toolkit 2.x21 hoursMicrosoft 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.
PaddlePaddlePaddlePaddle21 hoursPaddlePaddle (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
snorkelSnorkel: Rapidly Process Training Data7 hoursSnorkel 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
encogadvEncog: Advanced Machine Learning14 hoursEncog 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
encogintroEncog: Introduction to Machine Learning14 hoursEncog 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
matlabdlMatlab for Deep Learning14 hoursIn 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
undnnUnderstanding Deep Neural Networks35 hoursThis 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

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.
drlpythonDeep Reinforcement Learning with Python21 hoursDeep 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

Upcoming Neural Networks Courses

CourseCourse DateCourse Price [Remote / Classroom]
Understanding Deep Neural Networks - UppsalaMon, 2018-09-03 09:3016520EUR / 18220EUR
Neural Network in R - Malmö, StadskärnaTue, 2018-09-04 09:303730EUR / 4330EUR
Applied Machine Learning - Örebro, City CenterThu, 2018-09-06 09:303730EUR / 4330EUR
Artificial Neural Networks, Machine Learning, Deep Thinking - LinköpingTue, 2018-09-11 09:305450EUR / 6250EUR
Artificial Intelligence Overview - VästeråsWed, 2018-09-19 09:301930EUR / 2330EUR
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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Haskell Fundamentals Malmö, Stadskärna Mon, 2018-09-10 09:30 2394EUR / 2994EUR
MariaDB Database Administration Stockholm, Hötorget Tue, 2018-09-11 09:30 2673EUR / 3273EUR
Introduction to Recommendation Systems Stockholm, Hötorget Fri, 2018-10-12 09:30 1314EUR / 1714EUR
Statistical Thinking for Decision Makers Malmö, Stadskärna Mon, 2018-11-26 09:30 1503EUR / 1903EUR
Managing Business Rules with PHP Business Rules Malmö, Stadskärna Wed, 2018-12-12 09:30 2430EUR / 3030EUR

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