This instructor-led, live training in České republice (online or onsite) is aimed at software engineers or anyone who wish to learn how to use Vertex AI to perform and complete machine learning activities.
By the end of this training, participants will be able to:
Understand how Vertex AI works and use it as a machine learning platform.
Learn about machine learning and NLP concepts.
Know how to train and deploy machine learning models using Vertex AI.
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.
Waikato Environment for Knowledge Analysis (Weka) je open-source data mining visualization software. Poskytuje sbírku algoritmů strojového učení pro přípravu údajů, klasifikaci, klastrování a další data těžby činnosti.
Tento výcvik vedený instruktorem (online nebo on-site) je zaměřen na analytiky a vědce údajů, kteří chtějí použít Weka k provádění úkolů v oblasti datového těžby.
Po ukončení tohoto tréninku budou účastníci schopni:
Instalace a nastavení Weka
Pochopte Weka prostředí a pracovní zázemí.
Výkon datových úkolů pomocí Weka.
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.
Cílem tohoto kurzu je poskytnout základní dovednosti při uplatňování Machine Learning metod v praxi. Prostřednictvím používání Python programovacího jazyka a jeho různých knihoven a na základě mnoha praktických příkladů se tento kurz učí, jak používat nejdůležitější stavební bloky Machine Learning, jak činit data modelování rozhodnutí, interpretovat výstupy algoritmů a validovat výsledky.
Naším cílem je poskytnout vám dovednosti k pochopení a důvěryhodnému používání nejzákladnějších nástrojů z nástrojové krabice Machine Learning a vyhnout se běžným úderům aplikací Data Science.
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.
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Jedná se o 4denní kurz, který zavádí AI a jeho aplikaci pomocí Python programovacího jazyka. Existuje možnost mít další den k zahájení projektu AI po dokončení tohoto kurzu.
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
Tento kurz byl vytvořen pro manažery, architekty řešení, inovační úředníky, CTO, architekty softwaru a všechny, kteří se zajímají o přehled aplikované umělé inteligence a nejbližší předpověď pro jeho vývoj.
This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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 introduces machine learning methods in robotics applications.
It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.
After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
R je open-source programovací jazyk pro statistické výpočetní techniky, analýzu dat a grafiku. Výzkum využívá rostoucí počet manažerů a analytiků v korporacích a akademii. R má širokou škálu balíčků pro data mining.
PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.
Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same.
Target Audience
Investors and AI entrepreneurs
Managers and Engineers whose company is venturing into AI space
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
Programmatically create training sets to enable the labeling of massive training sets
Train high-quality end models by first modeling noisy training sets
Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
Implement different neural networks optimization techniques to resolve underfitting and overfitting
Understand and choose from a number of neural network architectures
Implement supervised feed forward and feedback networks
Audience
Developers
Analysts
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
Prepare data for neural networks using the normalization process
Implement feed forward networks and propagation training methodologies
Implement classification and regression tasks
Model and train neural networks using Encog's GUI based workbench
Integrate neural network support into real-world applications
Audience
Developers
Analysts
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.
By the end of this training, participants will be able to:
Solve text-based data science problems with high-quality, reusable code
Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
Build effective machine learning models using text-based data
Create a dataset and extract features from unstructured text
Visualize data with Matplotlib
Build and evaluate models to gain insight
Troubleshoot text encoding errors
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 the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app.
By the end of this training, participants will be able to:
Create a mobile app capable of image processing, text analysis and speech recognition
Access pre-trained ML models for integration into iOS apps
Create a custom ML model
Add Siri Voice support to iOS apps
Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
Developers
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 apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
Developers
Data scientists
Banking professionals with a technical background
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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