Deep Learning Training Courses

AlphaFold е система, която извършва прогнозата на протеинови структури. Той е разработен от Alphabet’s/Google’s DeepMind като система за дълбоко учене, която може точно да предскаже 3D модели на протеинови структури. Това обучение, ръководено от инструктори, е насочено към биолози, които искат да разберат как AlphaFold работят и използват AlphaFold модели като ръководители в експерименталните си проучвания. В края на обучението участниците ще могат да:

Разберете основните принципи на AlphaFold. Научете как действа AlphaFold. Научете как да тълкувате AlphaFold прогнози и резултати.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

In this instructor-led, live training in България, 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.

Дълбокото Reinforcement Learning се свързва на способността на артификален агент" да научи чрез процедура и грешка и награда. Изкуството на цел да емулира човешко и#39; способност да получи и създаде познания, пряко от сирови входи като видение. За да се осъзнае подкреплението на учителство, се използва дълбоко учителство и неврални мрежи. Научването на преодоляването е различно от учителството на машини и не се обяви на надзора и не надгледащи подходи за учителство.В този инструктор, живо обучение, участниците ще научат основните на Дълбокото Reinforcement Learning, докато те стъпят през създаването на Deep Learning Агент.До края на този обучение участниците ще могат да:

Разберете ключовите концепции зад Дълбокото Reinforcement Learning и можете да го разделите от Machine Learning Приложите напредни алгоритми Reinforcement Learning за решаване на проблемите на реално-святно изгради Deep Learning Агент

Слушателство

Разработчиците данни научници

Формат на курса

Частична лекция, частни дискусии, упражнения и тежки ръце на практика

Машинното обучение е отрасъл на изкуствения интелект, в който компютрите имат способността да учат, без да бъдат изрично програмирани. Дълбокото обучение е подполе на машинното обучение, което използва методи, базирани на представления и структури на данни за учене, като например невронни мрежи. Python е високо ниво на програмиране език, известен със своята ясна синтеза и четене на кодове. В това обучение, ръководено от инструктори, участниците ще научат как да внедряват модели за дълбоко обучение за телекомуникации, като стъпват през създаването на модел за дълбоко обучение кредитен риск. В края на обучението участниците ще могат да:

Разберете основните понятия за дълбоко учене. Научете приложенията и приложенията на дълбокото обучение в телекомуникациите. Използвайте Python, Keras и TensorFlow, за да създадете модели за дълбоко обучение за телекомуникации. Изградете своя собствен модел за предсказване на дълбокото обучение на клиента, като използвате Python.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to:

• Explore how data is being interpreted by machine learning models
• Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
• Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
• Explore the properties of a specific embedding to understand the behavior of a model
• Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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.

This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

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.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to:

• understand Caffe’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, implementing layers and logging

Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images This course provide working examples.

Този курс обхваща AI (инфазиране Machine Learning и Deep Learning) в Automotive Индустрия. Той помага да се определи каква технология може (потенциално) да се използва в много ситуации в автомобила: от проста автоматизация, разпознаване на изображенията до автономно вземане на решения.

This course covers AI (emphasizing Machine Learning and Deep Learning)

В този инструктор-управлява, на живо обучение, ние преминаваме през принципите на невронните мрежи и използваме OpenNN за прилагане на приложението на проба. Формат на курса

Четене и дискусия, съчетани с практични упражнения.

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

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.

В това обучение, ръководено от инструктори, участниците ще научат как да използват Facebook NMT (Fairseq) за извършване на превод на съдържание на проба. До края на обучението участниците ще имат необходимите знания и практика за прилагане на живо решение за машинно превод на базата на Fairseq. Формат на курса

Частна лекция, частна дискусия, тежка практика

Забележка

Ако искате да използвате специфичен източник и съдържание на целевия език, моля, свържете се с нас, за да организирате.

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.

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 DSSTNE to build a recommendation application. By the end of this training, participants will be able to:

• Train a recommendation model with sparse datasets as input
• Scale training and prediction models over multiple GPUs
• Spread out computation and storage in a model-parallel fashion
• Generate Amazon-like personalized product recommendations
• Deploy a production-ready application that can scale at heavy workloads

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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

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 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.

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

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use R to create deep learning models for finance
• Build their own deep learning stock price prediction model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use Python, Keras, and TensorFlow to create deep learning models for banking
• Build their own deep learning credit risk model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use R to create deep learning models for banking
• Build their own deep learning credit risk model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use Python, Keras, and TensorFlow to create deep learning models for finance
• Build their own deep learning stock price prediction model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Въведение на: Дълбокото обучение се превръща в основен компонент на бъдещия дизайн на продукти, който иска да интегрира изкуствения интелект в сърцето на своите модели. В рамките на следващите 5 до 10 години инструментите за развитие на дълбоко обучение, библиотеките и езиците ще станат стандартни компоненти на всеки комплект инструменти за разработка на софтуер. Досега Google, Sales Force, Facebook, Amazon успешно са използвали задълбочено учене AI, за да стимулират бизнеса си. Приложенията варират от автоматичен машинен превод, анализ на изображенията, видео анализ, анализ на движението, генериране на целенасочена реклама и много други. Този курс е насочен към тези организации, които искат да включат Deep Learning като много важна част от своята стратегия за продукт или услуга. По-долу е извода за дълбокото обучение, което можем да персонализираме за различни нива на служители / участници в една организация. Целенасочена аудитория: (В зависимост от целевата аудитория, курсовите материали ще бъдат персонализирани) Изпълнители Общ преглед на ИИ и как тя се вписва в корпоративната стратегия, с прекъсване сесии по стратегическо планиране, технологични пътни карти и разпределение на ресурси, за да се гарантира максимална стойност. Проектни мениджъри Как да планирате AI проект, включително събиране и оценка на данни, изчистване и проверка на данни, разработване на модел на доказателство за концепция, интегриране в бизнес процеси и доставка в цялата организация. Разработчиците Дълбоко техническо обучение, с фокус върху невронни мрежи и дълбоко учене, анализ на изображения и видео (CNNs), аудио и текстови анализи (NLP), и въвеждане на AI в съществуващите приложения. продавачи Общ преглед на изкуствения интелект и как той може да задоволи нуждите на клиентите, предложения за стойност за различни продукти и услуги и как да се отървем от страховете и да се насърчават ползите от изкуствения интелект.

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