Explore Our Courses

Applied AI from Scratch in Python

 28 時間
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. 
続きを読む...

Deep Reinforcement Learning with Python

 21 時間
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. This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to 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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
続きを読む...

Artificial Intelligence (AI) Overview

 7 時間
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.
続きを読む...

Artificial Intelligence (AI) in Automotive

 14 時間
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.
続きを読む...

From Zero to AI

 35 時間
This course is created for people who have no previous experience in probability and statistics.
続きを読む...

Artificial Neural Networks, Machine Learning, Deep Thinking

 21 時間
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.
続きを読む...

Applied AI from Scratch

 28 時間
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. 
続きを読む...

Applied Machine Learning

 14 時間
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.
続きを読む...

Artificial Neural Networks, Machine Learning and Deep Thinking

 21 時間
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.
続きを読む...

Deep Learning Neural Networks with Chainer

 14 時間
Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms. This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start developing neural network models.
  • Define and implement neural network models using a comprehensible source code.
  • Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
続きを読む...

Pattern Recognition

 21 時間
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.
続きを読む...

Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

 21 時間
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.
続きを読む...

Matlab for Deep Learning

 14 時間
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
続きを読む...

Artificial Intelligence (AI) for Mechatronics

 21 時間
Mechatronics (a.k.a. mechatronic engineering) is a combination of mechanical, electronics and computer science. This instructor-led, live training (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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
続きを読む...

Introduction to the Use of Neural Networks

 7 時間
The training is aimed at people who want to learn the basics of neural networks and their applications.
続きを読む...

Neural Networks Fundamentals using TensorFlow as Example

 28 時間
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.
続きを読む...

Neural computing – Data science

 14 時間
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
続きを読む...

PaddlePaddle

 21 時間
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
続きを読む...

Recommender Systems with Python

 14 時間
A recommender system is an information filtering process that predicts the user's preferences. Python can be used to program deep learning, machine learning, and neural network recommender systems to help users discover new products and content. This instructor-led, live training (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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
続きを読む...

Neural Network in R

 14 時間
This course is an introduction to applying neural networks in real world problems using R-project software.
続きを読む...

TPU Programming: Building Neural Network Applications on Tensor Processing Units

 7 時間
The 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.
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
続きを読む...

Understanding Deep Neural Networks

 35 時間
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
続きを読む...

OpenNN: Implementing Neural Networks

 14 時間
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.
続きを読む...

Last Updated:

お客様の声(5)

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
コース - Artificial Neural Networks, Machine Learning, Deep Thinking

It felt like we were going through directly relevant information at a good pace (i.e. no filler material)

Maggie Webb - Department of Jobs, Regions, and Precincts
コース - Introduction to the use of neural networks

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
コース - Applied AI from Scratch in Python

Very flexible.

Frank Ueltzhöffer
コース - Artificial Neural Networks, Machine Learning and Deep Thinking

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane
コース - Neural Networks Fundamentals using TensorFlow as Example

Upcoming Courses

Other regions in 日本

他の国

これらのコースは他の国でもあります>>

コンサルティング

ニューラルネットワーク コンサルティング
週末ニューラルネットワークコース, 夜のニューラルネットワークトレーニング, ニューラルネットワークブートキャンプ, ニューラルネットワーク インストラクターよる, 週末ニューラルネットワークトレーニング, 夜のニューラルネットワークコース, ニューラルネットワーク指導, ニューラルネットワークインストラクター, ニューラルネットワークレーナー, ニューラルネットワークレーナーコース, ニューラルネットワーククラス, ニューラルネットワークオンサイト, ニューラルネットワークプライベートコース, ニューラルネットワーク1対1のトレーニング