Deep Learning Počítačové Kurzy

AlphaFold je Artificial Intelligence (AI) systém, který provádí předpověď proteinových struktur. Je vyvinut Alphabet’s/Google’s DeepMind jako systém hlubokého učení, který může přesně předpovědět 3D modely proteinových struktur. Tento výcvik vedený instruktorem (online nebo on-site) je zaměřen na biology, kteří chtějí pochopit, jak AlphaFold pracují a používají AlphaFold modely jako průvodce ve svých experimentálních studiích. Po ukončení tohoto tréninku budou účastníci schopni:

Pochopte základní principy AlphaFold. Zjistěte, jak AlphaFold funguje. Naučte se interpretovat AlphaFold předpovědi a výsledky.

Formát kurzu

Interaktivní přednáška a diskuse. Mnoho cvičení a praxe. Hands-on implementace v živém laboratoři prostředí.

Možnosti personalizace kurzu

Chcete-li požádat o přizpůsobené školení pro tento kurz, kontaktujte nás, abyste uspořádali.

In this instructor-led, live training in České republice, 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.

Hlubka Reinforcement Learning se odkazuje na schopnost & quot; artificial agent", aby se naučil zkušením a chybam a výhrady. Umělný agent cílí emulaci lidského ' schopnost získat a vytvořit svůj vlastní znalost, přímo z surovéch vstupů, například vize. Aby se zjistili povzbuzení učiní, jsou používány hlubokou učenství a neurální sítě. Učení povzbuzení je jiné od vyučených strojů a nezáleží na nadzorných a nevzájemných přístupů učit.V tomto instruktorům budou účastníci naučit základní základy Hluby Reinforcement Learning, když přes vytvoření agentu Deep Learning.Až do konce tohoto školy budou účastníci umožni:

Porozumět klíčové koncepce za Hlubkou Reinforcement Learning a bude možné je rozdělit od Machine Learning Použijte pokročené algoritmy Reinforcement Learning k řešení problémů reálního světa Stvořit Deep Learning Agent

Slušenství

Vývojci Data vědeců

Formatu práce

Částní předmět, částní diskusie, vztahů a těžké rukové praxi

Machine learning je odvětví umělé inteligence, ve kterém mají počítače schopnost učit se bez výslovného programování. Hluboké učení je podzemí strojového učení, které využívá metody založené na vzdělávacích údajích a strukturách, jako jsou neurální sítě. Python je programovací jazyk vysoké úrovně známý pro jeho jasný syntax a čitelnost kódu. V tomto instruktorově vedeném, živém tréninku se účastníci naučí, jak implementovat modely hlubokého učení pro telekomunikace pomocí Python jak postupují prostřednictvím vytvoření modelu hlubokého učení úvěrového rizika. Po ukončení tohoto tréninku budou účastníci schopni:

Pochopte základní pojmy hlubokého učení. Naučte se aplikace a využití hlubokého učení v telekomunikacích. Použijte Python, Keras a TensorFlow k vytvoření hlubokých modelů učení pro telekom. Vytvořte si vlastní model předpovědi hlubokého učení zákazníků pomocí Python.

Formát kurzu

Interaktivní přednáška a diskuse. Mnoho cvičení a praxe. Hands-on implementace v živém laboratoři prostředí.

Možnosti personalizace kurzu

Chcete-li požádat o přizpůsobené školení pro tento kurz, kontaktujte nás, abyste uspořádali.

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.

Tento kurz pokrývá AI (emphasizing Machine Learning a Deep Learning) v Automotive Průmyslu. Pomáhá určit, jakou technologii lze (potenciálně) použít v několika situacích v autě: od jednoduché automatizace, rozpoznávání obrazu až po autonomní rozhodování.

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

V tomto instruktor vedeném, živé školení, my jdeme přes principy nervových sítí a používat OpenNN k provádění vzorkové aplikace. Formát kurzu

Čtení a diskuse v kombinaci s praktickými cvičeními.

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.

V tomto živém tréninku vedeném instruktorem se účastníci dozví, jak použít Facebook NMT (Fairseq) k provedení překladu vzorkového obsahu. Do konce tohoto tréninku budou účastníci mít znalosti a praxi potřebné k implementaci živého řešení strojového překladu založeného na Fairseq. Formát kurzu

Částečná přednáška, částečná diskuse, těžká praxe

Poznámka

Pokud chcete použít konkrétní zdrojový a cílový jazykový obsah, kontaktujte nás k uspořádání.

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

Představení : Hluboké učení se stává hlavním prvkem budoucího designu produktů, který chce začlenit umělou inteligenci do srdce svých modelů. Během následujících 5 až 10 let se nástroje pro vývoj hlubokého učení, knihovny a jazyky stanou standardními složkami každého souboru nástrojů pro vývoj softwaru. Zatím Google, Sales Force, Facebook, Amazon úspěšně využívá hluboké učení AI k rozšiřování svého podnikání. Aplikace se liší od automatického strojového překladu, analýzy obrazu, analýzy videa, analýzy pohybu, generování cílené reklamy a mnoho dalších. Tento kurz je zaměřen na ty organizace, které chtějí začlenit Deep Learning jako velmi důležitou součást své strategie produktu nebo služby. Níže je přehled hlubokého kurzu, který můžeme přizpůsobit různým úrovním zaměstnanců / subjektů v organizaci. Cílové publikum : (V závislosti na cílové publikum budou kurzy materiály přizpůsobeny) Výkonní Obecný přehled umělé inteligence a toho, jak se hodí do firemní strategie, s přerušenými sezeními o strategickém plánování, technologických cestních mapách a přidělování zdrojů, aby byla zajištěna maximální hodnota. Projektový manažer Jak plánovat projekt umělé inteligence, včetně shromažďování a hodnocení dat, čištění a ověřování dat, vývoj modelu prokázání koncepce, integrace do obchodních procesů a dodání v celé organizaci. Vývojáři Dlouhodobé technické školení, se zaměřením na neurální sítě a hluboké učení, analýzu obrazu a videa (CNNs), analýzu zvuku a textu (NLP) a zavádění umělé inteligence do stávajících aplikací. prodejci Obecný přehled umělé inteligence a toho, jak může uspokojit potřeby zákazníků, hodnotové návrhy pro různé produkty a služby a jak odstraňovat obavy a podporovat výhody umělé inteligence.

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