Neural Networks Training Courses

Neural Networks Training

Neural Networks courses

Neural Networks Course Outlines

ID Name Duration Overview
911909 Using Computer Network ToolKit (CNTK) 28 hours Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects. Getting started Setup CNTK on your machine Enabling 1bit SGD Developing and Testing CNTK Production Test Configurations How to contribute to CNTK Tutorial Tutorial II CNTK usage overview Examples Presentations Multiple GPUs and machines Configuring CNTK Config file overview Simple Network Builder BrainScript Network Builder SGD block Reader block Train, Test, Eval Top-level configurations Describing Networks Basic concepts Expressions Defining functions Full Function Reference Data readers Text Format Reader CNTK Text Format Reader UCI Fast Reader (deprecated) HTKMLF Reader LM sequence reader LU sequence reader Image reader Evaluating CNTK Models Overview C++ Evaluation Interface C# Evaluation Interface Evaluating Hidden Layers C# Image Transforms for Evaluation Advanced topics Command line parsing rules Top-level commands Plot command ConvertDBN command
417023 Introduction to Machine Learning 7 hours 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. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Ridge regression Clustering
416995 Machine Learning Fundamentals with R 14 hours 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. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means
463976 Introduction to Deep Learning 21 hours 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. Backprop, modular models Logsum module RBF Net MAP/MLE loss Parameter Space Transforms Convolutional Module Gradient-Based Learning  Energy for inference, Objective for learning PCA; NLL:  Latent Variable Models Probabilistic LVM Loss Function Handwriting recognition
13020 Artificial Intelligence Overview 7 hours This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and everyone who is interested overview of applied artificial intelligence and the nearest forecast for its development. Artificial Intelligence History Intelligent Agents Problem Solving Solving Problems by Searching Beyond Classical Search Adversarial Search Constraint Satisfaction Problems Knowledge and Reasoning Logical Agents First-Order Logic Inference in First-Order Logic Classical Planning Planning and Acting in the Real World Knowledge Representation Uncertain Knowledge and Reasoning Quantifying Uncertainty Probabilistic Reasoning Probabilistic Reasoning over Time Making Simple Decisions Making Complex Decisions Learning Learning from Examples Knowledge in Learning Learning Probabilistic Models Reinforcement Learning Communicating, Perceiving, and Acting; Natural Language Processing Natural Language for Communication Perception Robotics Conclusions Philosophical Foundations AI: The Present and Future
164943 Introduction to the use of neural networks 7 hours The training is aimed at people who want to learn the basics of neural networks and their applications. The Basics Whether computers can think of? Imperative and declarative approach to solving problems Purpose Bedan on artificial intelligence The definition of artificial intelligence. Turing test. Other determinants The development of the concept of intelligent systems Most important achievements and directions of development Neural Networks The Basics Concept of neurons and neural networks A simplified model of the brain Opportunities neuron XOR problem and the nature of the distribution of values The polymorphic nature of the sigmoidal Other functions activated Construction of neural networks Concept of neurons connect Neural network as nodes Building a network Neurons Layers Scales Input and output data Range 0 to 1 Normalization Learning Neural Networks Backward Propagation Steps propagation Network training algorithms range of application Estimation Problems with the possibility of approximation by Examples XOR problem Lotto? Equities OCR and image pattern recognition Other applications Implementing a neural network modeling job predicting stock prices of listed Problems for today Combinatorial explosion and gaming issues Turing test again Over-confidence in the capabilities of computers
226127 Training Neural Network in R 14 hours This course is an introduction to applying neural networks in real world problems using R-project software. Introduction to Neural Networks What are Neural Networks What is current status in applying neural networks Neural Networks vs regression models Supervised and Unsupervised learning Overview of packages available nnet, neuralnet and others differences between packages and itls limitations Visualizing neural networks Applying Neural Networks Concept of neurons and neural networks A simplified model of the brain Opportunities neuron XOR problem and the nature of the distribution of values The polymorphic nature of the sigmoidal Other functions activated Construction of neural networks Concept of neurons connect Neural network as nodes Building a network Neurons Layers Scales Input and output data Range 0 to 1 Normalization Learning Neural Networks Backward Propagation Steps propagation Network training algorithms range of application Estimation Problems with the possibility of approximation by Examples OCR and image pattern recognition Other applications Implementing a neural network modeling job predicting stock prices of listed
417029 From Data to Decision with Big Data and Predictive Analytics 21 hours Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Quick Overview Data Sources Minding Data Recommender systems Target Marketing Datatypes Structured vs unstructured Static vs streamed Attitudinal, behavioural and demographic data Data-driven vs user-driven analytics data validity Volume, velocity and variety of data Models Building models Statistical Models Machine learning Data Classification Clustering kGroups, k-means, nearest neighbours Ant colonies, birds flocking Predictive Models Decision trees Support vector machine Naive Bayes classification Neural networks Markov Model Regression Ensemble methods ROI Benefit/Cost ratio Cost of software Cost of development Potential benefits Building Models Data Preparation (MapReduce) Data cleansing Choosing methods Developing model Testing Model Model evaluation Model deployment and integration Overview of Open Source and commercial software Selection of R-project package Python libraries Hadoop and Mahout Selected Apache projects related to Big Data and Analytics Selected commercial solution Integration with existing software and data sources
417022 Applied Machine Learning 14 hours 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. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Bayesian Graphical Models Factor Analysis (FA) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Support Vector Machines (SVM) for regression and classification Boosting Ensemble models Neural networks Hidden Markov Models (HMM) Space State Models Clustering
463700 Artificial Neural Networks, Machine Learning, Deep Thinking 21 hours DAY 1 - ARTIFICIAL NEURAL NETWORKS Introduction and ANN Structure. Biological neurons and artificial neurons. Model of an ANN. Activation functions used in ANNs. Typical classes of network architectures . Mathematical Foundations and Learning mechanisms. Re-visiting vector and matrix algebra. State-space concepts. Concepts of optimization. Error-correction learning. Memory-based learning. Hebbian learning. Competitive learning. Single layer perceptrons. Structure and learning of perceptrons. Pattern classifier - introduction and Bayes' classifiers. Perceptron as a pattern classifier. Perceptron convergence. Limitations of a perceptrons. Feedforward ANN. Structures of Multi-layer feedforward networks. Back propagation algorithm. Back propagation - training and convergence. Functional approximation with back propagation. Practical and design issues of back propagation learning. Radial Basis Function Networks. Pattern separability and interpolation. Regularization Theory. Regularization and RBF networks. RBF network design and training. Approximation properties of RBF. Competitive Learning and Self organizing ANN. General clustering procedures. Learning Vector Quantization (LVQ). Competitive learning algorithms and architectures. Self organizing feature maps. Properties of feature maps. Fuzzy Neural Networks. Neuro-fuzzy systems. Background of fuzzy sets and logic. Design of fuzzy stems. Design of fuzzy ANNs. Applications A few examples of Neural Network applications, their advantages and problems will be discussed. DAY -2 MACHINE LEARNING The PAC Learning Framework Guarantees for finite hypothesis set – consistent case Guarantees for finite hypothesis set – inconsistent case Generalities Deterministic cv. Stochastic scenarios Bayes error noise Estimation and approximation errors Model selection Radmeacher Complexity and VC – Dimension Bias - Variance tradeoff Regularisation Over-fitting Validation Support Vector Machines Kriging (Gaussian Process regression) PCA and Kernel PCA Self Organisation Maps (SOM) Kernel induced vector space Mercer Kernels and Kernel - induced similarity metrics Reinforcement Learning DAY 3 - DEEP LEARNING This will be taught in relation to the topics covered on Day 1 and Day 2 Logistic and Softmax Regression Sparse Autoencoders Vectorization, PCA and Whitening Self-Taught Learning Deep Networks Linear Decoders Convolution and Pooling Sparse Coding Independent Component Analysis Canonical Correlation Analysis Demos and Applications
464027 Inteligencja obliczeniowa w praktyce 7 hours
464025 Systemy wieloagentowe 7 hours
464028 Metody Inteligencji Obliczeniowej 7 hours
464026 Przetwarzanie języka naturalnego 7 hours
915929 From Zero to AI 35 hours This course is created for people who have no previous expierence in probability and statistics.   Probability (3.5h) Definition of probability Binomial distribution Everyday usage exercises Statistics (10.5h) Descriptive Statistics Inferential Statistics Regression Logistic Regression Exercises Intro to programming (3.5h) Procedural Programming Funcional Programming OOP Programming Exercises (writing logic for a game of choice, e.g. cross and noughts) Machine Learning (10.5h) Classification Clustering Neural Networks Exercises (write AI for a computer game of choice) Rules Engines and Expert Systems (7 hours) Intro to Rule Engines Write AI for the same game and combing solutions into hybrid approach

Course Discounts

Course Venue Course Date Course Price [Remote/Classroom]
Java Spring Kraków, ul. Rzemieślnicza 1 Mon, 2016-08-29 09:00 7039PLN / 5245PLN
Java Spring Szczecin, ul. Małopolska 23 Mon, 2016-09-05 09:00 7039PLN / 5044PLN
Programming in WPF 4.5 Warszawa, ul. Złota 3/11 Mon, 2016-09-05 09:00 2809PLN / 1805PLN
Web Application Development in PHP Szczecin, ul. Małopolska 23 Tue, 2016-09-06 09:00 2688PLN / 2081PLN
Building Web Apps using the MEAN stack Szczecin, ul. Małopolska 23 Mon, 2016-09-12 09:00 4388PLN / 3003PLN
Java Spring Gdańsk, ul. Powstańców Warszawskich 45 Mon, 2016-09-12 09:00 7039PLN / 5153PLN
Java Spring Poznan, Garbary 100/63 Mon, 2016-09-12 09:00 7039PLN / 4961PLN
Access Intermediate Bydgoszcz, ul. Dworcowa 94 Tue, 2016-09-13 09:00 1218PLN / 910PLN
Java Spring Warszawa, ul. Złota 3/11 Mon, 2016-09-19 09:00 7039PLN / 4961PLN
Java Performance Gdynia, ul. Ejsmonda 2 Mon, 2016-09-19 09:00 4150PLN / 2866PLN
Java Spring Wroclaw, ul.Ludwika Rydygiera 2a/22 Mon, 2016-09-19 09:00 7039PLN / 4961PLN
Oracle 11g - Programming with PL / SQL II Wroclaw, ul.Ludwika Rydygiera 2a/22 Mon, 2016-09-26 09:00 2363PLN / 1785PLN
BPMN 2.0 for Business Analysts Wroclaw, ul.Ludwika Rydygiera 2a/22 Tue, 2016-09-27 09:00 3110PLN / 2337PLN
ITIL® Foundation Certificate in IT Service Management Warszawa, ul. Złota 3/11 Mon, 2016-10-10 09:00 2639PLN / 2076PLN
Visual Basic for Applications (VBA) in Excel - Advanced Wroclaw, ul.Ludwika Rydygiera 2a/22 Mon, 2016-10-10 09:00 1689PLN / 1296PLN
Market Forecasting Poznan, Garbary 100/63 Thu, 2016-10-13 09:00 2936PLN / 2112PLN
Effective working with spreadsheet in Excel Rzeszów, Plac Wolności 13 Tue, 2016-10-18 09:00 918PLN / 843PLN
A Practical Guide to Successful Pricing Strategies Poznan, Garbary 100/63 Wed, 2016-10-26 09:00 1427PLN / 1093PLN
Agile Project Management with Scrum Kraków, ul. Rzemieślnicza 1 Wed, 2016-11-02 09:00 1746PLN / 1449PLN
Visual Basic for Applications (VBA) in Excel - Advanced Białystok, ul. Malmeda 1 Mon, 2016-11-14 09:00 1689PLN / 1413PLN

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