Local instructor-led live Deep Learning training courses in България.
Oтзиви от потребители
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
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
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company
Sebastiaan Holman
Course: Machine Learning and Deep Learning
The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Course: Machine Learning and Deep Learning
Coverage and depth of topics
Anirban Basu
Course: Machine Learning and Deep Learning
The deep knowledge of the trainer about the topic.
Sebastian Görg
Course: Introduction to Deep Learning
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee
Course: TensorFlow for Image Recognition
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
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
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course: Python for Advanced Machine Learning
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course: Neural Networks Fundamentals using TensorFlow as Example
way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
Possibility to discuss the proposed issues yourself
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
Communication with lecturers
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
like it all
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Big and up-to-date knowledge of leading and practical application examples.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
A lot of exercises, very good cooperation with the group.
Janusz Chrobot - ING Bank Śląski S.A.
Course: Introduction to Deep Learning
work on colaborators,
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
It was obvious that the enthusiasts of the presented topics were leading. Used interesting examples during exercise.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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
Lack
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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
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
exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Examples and issues discussed.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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
Practical exercises prepared by Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
Human identification and circuit board bad point detection
王 春柱 - 中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Demonstrate
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
A lot of practical tips
Pawel Dawidowski - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
A lot of information related to the implementation of solutions
Michał Smolana - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
A multitude of practical tips and knowledge of the lecturer from a wide range of AI / IT / SQL / IoT issues.
ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course: Natural Language Processing with TensorFlow
the way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The excersise where we should train a network to approximate a function
Nercia Utbildning AB
Course: Deep Learning with TensorFlow 2.0
Томасз наистина познава информацията добре и курсът беше много вървец.
Raju Krishnamurthy - Google
Course: TensorFlow Extended (TFX)
Machine Translated
Deep Learning Subcategories
Deep Learning Course Outlines
Име на Kурса
Продължителност
Общ преглед
Име на Kурса
Продължителност
Общ преглед
14 hours
Общ преглед
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.
14 hours
Общ преглед
TensorFlow.js is a JavaScript framework for machine learning. TensorFlow.js enables users to build and train machine learning models directly in JavaScript.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning models using custom data.
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.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning models using custom data.
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.
14 hours
Общ преглед
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
14 hours
Общ преглед
OpenCV is a library of programming functions for deciphering images with computer algorithms. OpenCV 4 is the latest OpenCV release and it provides optimized modularity, updated algorithms, and more. With OpenCV 4 and Python, users will be able to view, load, and classify images and videos for advanced image recognition.
This instructor-led, live training (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
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.
This instructor-led, live training (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
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.
14 hours
Общ преглед
OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.
In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
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 OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Общ преглед
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
14 hours
Общ преглед
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.
21 hours
Общ преглед
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
21 hours
Общ преглед
In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
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:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Общ преглед
In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
Audience
- Developers
- Analysts
- Data scientists
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:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Общ преглед
Deep learning is a subfield of machine learning. It uses methods based on learning data representations and structures such as neural networks.
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build a self-driving car (autonomous vehicle) using deep learning techniques.
By the end of this training, participants will be able to:
- Use computer vision techniques to identify lanes.
- Use Keras to build and train convolutional neural networks.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
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.
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build a self-driving car (autonomous vehicle) using deep learning techniques.
By the end of this training, participants will be able to:
- Use computer vision techniques to identify lanes.
- Use Keras to build and train convolutional neural networks.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
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.
21 hours
Общ преглед
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.
After completing this course, delegates will:
- understand SINGA’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, embedding terms, building graphs and logging
Audience
This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.
After completing this course, delegates will:
- understand SINGA’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, embedding terms, building graphs and logging
7 hours
Общ преглед
Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.
In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.
By the end of this training, participants will be able to:
- Install tensor2tensor, select a data set, and train and evaluate an AI model
- Customize a development environment using the tools and components included in Tensor2Tensor
- Create and use a single model to concurrently learn a number of tasks from multiple domains
- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
- Obtain satisfactory processing results using a single GPU
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 prepare a deep-learning model to resolve multiple tasks.
By the end of this training, participants will be able to:
- Install tensor2tensor, select a data set, and train and evaluate an AI model
- Customize a development environment using the tools and components included in Tensor2Tensor
- Create and use a single model to concurrently learn a number of tasks from multiple domains
- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
- Obtain satisfactory processing results using a single GPU
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Общ преглед
TensorFlow is a popular and machine learning library developed by Google for deep learning, numeric computation, and large-scale machine learning. TensorFlow 2.0, released in Jan 2019, is the newest version of TensorFlow and includes improvements in eager execution, compatibility and API consistency.
This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.0 to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
- Install and configure TensorFlow 2.0.
- Understand the benefits of TensorFlow 2.0 over previous versions.
- Build deep learning models.
- Implement an advanced image classifier.
- Deploy a deep learning model to the cloud, mobile and IoT devices.
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.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/
This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.0 to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
- Install and configure TensorFlow 2.0.
- Understand the benefits of TensorFlow 2.0 over previous versions.
- Build deep learning models.
- Implement an advanced image classifier.
- Deploy a deep learning model to the cloud, mobile and IoT devices.
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.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/
21 hours
Общ преглед
TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
- Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
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.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
- Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
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.
21 hours
Общ преглед
MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
- Install and configure Apache MXNet and its components.
- Understand MXNet's architecture and data structures.
- Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
- Build a convolutional neural network for image classification.
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.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
- Install and configure Apache MXNet and its components.
- Understand MXNet's architecture and data structures.
- Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
- Build a convolutional neural network for image classification.
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.
21 hours
Общ преглед
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/lite/
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/lite/
21 hours
Общ преглед
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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.
21 hours
Общ преглед
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
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.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
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.
7 hours
Общ преглед
TensorFlow Serving is a system for serving machine learning (ML) models to production.
In this instructor-led, live training (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.
By the end of this training, participants will be able to:
- Train, export and serve various TensorFlow models
- Test and deploy algorithms using a single architecture and set of APIs
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.
By the end of this training, participants will be able to:
- Train, export and serve various TensorFlow models
- Test and deploy algorithms using a single architecture and set of APIs
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 hours
Общ преглед
TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
- 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
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
- 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
28 hours
Общ преглед
This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
21 hours
Общ преглед
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
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.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
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.
21 hours
Общ преглед
Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others.
In this instructor-led, live training, we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.
By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.
Format of the Course
- Overview of Machine and Deep Learning
- In-class coding and integration exercises
- Test questions sprinkled along the way to check understanding
In this instructor-led, live training, we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.
By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.
Format of the Course
- Overview of Machine and Deep Learning
- In-class coding and integration exercises
- Test questions sprinkled along the way to check understanding
7 hours
Общ преглед
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
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
35 hours
Общ преглед
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- 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, embedding terms, building graphs and logging
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- 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, embedding terms, building graphs and logging
35 hours
Общ преглед
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
14 hours
Общ преглед
Video analytics refers to the technology and techniques used to process a video stream. A common application would be capturing and identifying live video events through motion detection, facial recognition, crowd and vehicle counting, etc.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
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.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
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.
28 hours
Общ преглед
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.
21 hours
Общ преглед
This course covers AI (emphasizing Machine Learning and Deep Learning)
Upcoming Deep Learning Courses
2020-12-07 09:30:00
28 hours
2020-12-07 09:30:00
28 hours
2021-02-01 09:30:00
28 hours
2021-02-02 09:30:00
28 hours
2021-03-29 09:30:00
28 hours
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