Szkolenia Machine Learning

Szkolenia Machine Learning

Machine Learning courses

Opinie uczestników

Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease - Knowledgepool Group Ltd

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Data Mining & Machine Learning with R

The trainer was so knowledgeable and included areas I was interested in

Mohamed Salama - Edmonton Police Service

Advanced Deep Learning

The global overview of deep learning

Bruno Charbonnier - OSONES

Advanced Deep Learning

The exercises are sufficiently practical and do not need a high knowledge in Python to be done.

Alexandre GIRARD - OSONES

Advanced Deep Learning

Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.

Paul Kassis - OSONES

Podkategorie

Plany Szkoleń Machine Learning

Kod Nazwa Czas trwania Charakterystyka kursu
mlfsas Machine Learning Fundamentals with Scala and Apache Spark 14 godz. 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. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools 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
predio Machine Learning with PredictionIO 21 godz. 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. Getting Started Quick Intro Installation Guide Downloading Template Deploying an Engine Customizing an Engine App Integration Overview Developing PredictionIO System Architecture Event Server Overview Collecting Data Learning DASE Implementing DASE Evaluation Overview Intellij IDEA Guide Scala API Machine Learning Education and Usage​ Examples Comics Recommendation Text Classification Community Contributed Demo Dimensionality Reducation and usage PredictionIO SDKs (Select One) Java PHP Python Ruby Community Contributed  
systemml Apache SystemML for Machine Learning 14 godz. Apache SystemML is a distributed and declarative machine learning platform. SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark. Audience This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning. Running SystemML Standalone Spark MLContext Spark Batch Hadoop Batch JMLC Tools Debugger IDE Troubleshooting Languages and ML Algorithms DML PyDML Algorithms
aiintrozero From Zero to AI 35 godz. This course is created for people who have no previous experience 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 Functional Programming OOP Programming Exercises (writing logic for a game of choice, e.g. noughts and crosses) 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
aiauto Artificial Intelligence in Automotive 14 godz. 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. Current state of the technology What is used What may be potentially used Rules based AI  Simplifying decision Machine Learning  Classification Clustering Neural Networks Types of Neural Networks Presentation of working examples and discussion Deep Learning Basic vocabulary  When to use Deep Learning, when not to Estimating computational resources and cost Very short theoretical background to Deep Neural Networks Deep Learning in practice (mainly using TensorFlow) Preparing Data Choosing loss function Choosing appropriate type on neural network Accuracy vs speed and resources Training neural network Measuring efficiency and error Sample usage Anomaly detection Image recognition ADAS        
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 godz. 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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. TensorFlow Basics Creation, Initializing, Saving, and Restoring TensorFlow variables Feeding, Reading and Preloading TensorFlow Data How to use TensorFlow infrastructure to train models at scale Visualizing and Evaluating models with TensorBoard TensorFlow Mechanics Inputs and Placeholders Build the GraphS Inference Loss Training Train the Model The Graph The Session Train Loop Evaluate the Model Build the Eval Graph Eval Output The Perceptron Activation functions The perceptron learning algorithm Binary classification with the perceptron Document classification with the perceptron Limitations of the perceptron From the Perceptron to Support Vector Machines Kernels and the kernel trick Maximum margin classification and support vectors Artificial Neural Networks Nonlinear decision boundaries Feedforward and feedback artificial neural networks Multilayer perceptrons Minimizing the cost function Forward propagation Back propagation Improving the way neural networks learn Convolutional Neural Networks Goals Model Architecture Principles Code Organization Launching and Training the Model Evaluating a Model
datamodeling Pattern Recognition 35 godz. This course provides an introduction into the field of pattern recognition and machine learning. It also touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, continuous feedback, and testing of knowledge and skills acquired. Audience     Data analysts     PhD students, researchers and practitioners   Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models  
MLFWR1 Machine Learning Fundamentals with R 14 godz. 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
patternmatching Pattern Matching 14 godz. Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Audience     Engineers and developers seeking to develop machine vision applications     Manufacturing engineers, technicians and managers Format of the course     This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision. Introduction     Computer Vision     Machine Vision     Pattern Matching vs Pattern Recognition Alignment     Features of the target object     Points of reference on the object     Determining position     Determining orientation Gauging     Setting tolerance levels     Measuring lengths, diameters, angles, and other dimensions     Rejecting a component Inspection     Detecting flaws     Adjusting the system Closing remarks  
mlfunpython Machine Learning Fundamentals with Python 14 godz. The aim of this course is to provide a basic 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. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools 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
Torch Torch: Getting started with Machine and Deep Learning 21 godz. 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 course 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. Audience     Software developers and programmers wishing to enable Machine and Deep Learning within their applications 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 Introduction to Torch     Like NumPy but with CPU and GPU implementation     Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking Installing Torch     Linux, Windows, Mac     Bitmapi and Docker Installing Torch packages     Using the LuaRocks package manager Choosing an IDE for Torch     ZeroBrane Studio     Eclipse plugin for Lua Working with the Lua scripting language and LuaJIT     Lua's integration with C/C++     Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.     Object orientation and serialization in Torch     Coding exercise Loading a dataset in Torch     MNIST     CIFAR-10, CIFAR-100     Imagenet Machine Learning in Torch     Deep Learning         Manual feature extraction vs convolutional networks     Supervised and Unsupervised Learning         Building a neural network with Torch         N-dimensional arrays Image analysis with Torch     Image package     The Tensor library Working with the REPL interpreter Working with databases Networking and Torch GPU support in Torch Integrating Torch     C, Python, and others Embedding Torch     iOS and Android Other frameworks and libraries     Facebook's optimized deep-learning modules and containers Creating your own package Testing and debugging Releasing your application The future of AI and Torch
appliedml Applied Machine Learning 14 godz. 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
OpenNN OpenNN: Implementing neural networks 14 godz. OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience     Software developers and programmers wishing to create Deep Learning applications. Format of the course     Lecture and discussion coupled with hands-on exercises. Introduction to OpenNN, Machine Learning and Deep Learning Downloading OpenNN Working with Neural Designer     Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics OpenNN architecture     CPU parallelization OpenNN classes     Data set, neural network, loss index, training strategy, model selection, testing analysis     Vector and matrix templates Building a neural network application     Choosing a suitable neural network     Formulating the variational problem (loss index)     Solving the reduced function optimization problem (training strategy) Working with datasets      The data matrix (columns as variables and rows as instances) Learning tasks     Function regression     Pattern recognition Compiling with QT Creator Integrating, testing and debugging your application The future of neural networks and OpenNN
mlintro Introduction to Machine Learning 7 godz. 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
mldt Machine Learning and Deep Learning 21 godz. This course covers AI (emphasizing Machine Learning and Deep Learning)Machine learning Introduction to Machine Learning Applications of machine learning Supervised Versus Unsupervised Learning Machine Learning Algorithms Regression Classification Clustering Recommender System Anomaly Detection Reinforcement Learning Regression Simple & Multiple Regression Least Square Method Estimating the Coefficients Assessing the Accuracy of the Coefficient Estimates Assessing the Accuracy of the Model Post Estimation Analysis Other Considerations in the Regression Models Qualitative Predictors Extensions of the Linear Models Potential Problems Bias-variance trade off [under-fitting/over-fitting] for regression models Resampling Methods Cross-Validation The Validation Set Approach Leave-One-Out Cross-Validation k-Fold Cross-Validation Bias-Variance Trade-Off for k-Fold The Bootstrap Model Selection and Regularization Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model] Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net] Selecting the Tuning Parameter Dimension Reduction Methods Principal Components Regression Partial Least Squares Classification Logistic Regression The Logistic Model cost function Estimating the Coefficients Making Predictions Odds Ratio Performance Evaluation Matrices [Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.] Multiple Logistic Regression Logistic Regression for >2 Response Classes Regularized Logistic Regression Linear Discriminant Analysis Using Bayes’ Theorem for Classification Linear Discriminant Analysis for p=1 Linear Discriminant Analysis for p >1 Quadratic Discriminant Analysis K-Nearest Neighbors Classification with Non-linear Decision Boundaries Support Vector Machines Optimization Objective The Maximal Margin Classifier Kernels One-Versus-One Classification One-Versus-All Classification Comparison of Classification Methods Introduction to Deep Learning ANN Structure Biological neurons and artificial neurons Non-linear Hypothesis Model Representation Examples & Intuitions Transfer Function/ Activation Functions Typical classes of network architectures Feed forward ANN. Structures of Multi-layer feed forward networks Back propagation algorithm Back propagation - training and convergence Functional approximation with back propagation Practical and design issues of back propagation learning Deep Learning Artificial Intelligence & Deep Learning Softmax Regression Self-Taught Learning Deep Networks Demos and Applications Lab: Getting Started with R Introduction to R Basic Commands & Libraries Data Manipulation Importing & Exporting data Graphical and Numerical Summaries Writing functions Regression Simple & Multiple Linear Regression Interaction Terms Non-linear Transformations Dummy variable regression Cross-Validation and the Bootstrap Subset selection methods Penalization [Ridge, Lasso, Elastic Net] Classification Logistic Regression, LDA, QDA, and KNN, Resampling & Regularization Support Vector Machine Resampling & Regularization Artificial Neural Network Deep Learning   Note: For ML algorithms, case studies will be used to discuss their application, advantages & potential issues. Analysis of different data sets will be performed using R
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21 godz. 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
dmmlr Data Mining & Machine Learning with R 14 godz. Introduction to Data mining and 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 Dicriminant analysis Logistic regression K-Nearest neighbors Support Vector Machines Neural networks Decision trees Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means Advanced topics Ensemble models Mixed models Boosting Examples Multidimensional reduction Factor Analysis Principal Component Analysis Examples
wdneo4j Wprowadzenie do Neo4j - grafowej bazy danych 7 godz. Wprowadzenie do Neo4j Instalacja i konfiguracja Struktura aplikacji Neo4j Relacyjne i grafowe sposoby reprezentacji danych Model grafowy danych Czy zagadnienie można i powinno reprezentować się jako graf? Wybrane przypadki użycia i modelowanie wybranego zagadnienia Najważniejsze pojęcia modelu grafowego Neo4j: Węzeł Relacja Właściwość Etykieta Język zapytań Cypher i operacje na grafach Tworzenie i zarządzanie schematem za pomocą języka Cypher Operacje CRUD na danych Zapytania Cypher oraz ich odpowiedniki w SQL Algorytmy grafowe wykorzystywane w Neo4j Interfejs REST Podstawowe zagadnienia administracyjne Tworzenie i odtwarzanie kopii zapasowych Zarządzanie bazą z poziomu przeglądarki Import i eksport danych w uniwersalnych formatach
BigData_ A practical introduction to Data Analysis and Big Data 28 godz. Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course     Part lecture, part discussion, heavy hands-on practice and implementation, occasional quizing to measure progress. Introduction to Data Analysis and Big Data What makes Big Data "big"? Velocity, Volume, Variety, Veracity (VVVV) Limits to traditional Data Processing Distributed Processing Statistical Analysis Types of Machine Learning Analysis Data Visualization Distributed Processing MapReduce Languages used for Data Analysis R language (crash course) Python (crash course) Approaches to Data Analysis Statistical Analysis Time Series analysis Forecasting with Correlation and Regression models Inferential Statistics (estimating) Descriptive Statistics in Big Data sets (e.g. calculating mean) Machine Learning Supervised vs unsupervised learning Classification and clustering Estimating cost of specific methods Filter Natural Language Processing Processing text Understaing meaning of the text Automatic text generation Sentiment/Topic Analysis Computer Vision Big Data infrastructure Data Storage SQL (relational database) MySQL Postgres Oracle NoSQL Cassandra MongoDB Neo4js Understanding the nuances: hierarchical, object-oriented, document-oriented, graph-oriented, etc. Distributed File Systems HDFS Search Engines ElasticSearch Distributed Processing Spark Machine Learning libraries: MLlib Spark SQL Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing right solution for the problem  
mlrobot1 Machine Learning for Robotics 21 godz. This course introduce 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 short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software. Regression Probabilistic Graphical Models Boosting Kernel Methods Gaussian Processes Evaluation and Model Selection Sampling Methods Clustering CRFs Random Forests IVMs
octnp Octave nie tylko dla programistów 21 godz. Szkolenie dedykowane osobom, które chciałyby zapoznać się z obsługą programu alternatywnego do komercyjnego pakietu MATLAB. Kurs trzydniowy dostarcza kompleksowo informacje dotyczące poruszania się po środowisku i wykonywaniu pakietu OCTAVE w zastosowaniu do analizy danych i obliczeń inżynierskich. Adresatami szkolenia są osoby początkujące ale także ci, którzy znają program i chcieliby usystematyzować swoją wiedzę i podnieść umiejętności. Nie jest wymagana znajomość innych języków programowania ale w znacznym stopniu ułatwi to uczestnikom przyswajanie wiedzy. Na kursie pokazane zostaną możliwości wykorzystania program na wielu przykładach praktycznych. Wstęp Podstawowe obliczenia Rozpoczęcie pracy z Octave, Octave jako kalkulator, funkcje wbudowane Środowisko Octave Zmienne, liczny I formatowanie, reprezentacje liczb I ich dokładność, zapisywanie i wczytywanie danych  Tablice i wektory Wyodrębnianie liczb z wektorów, arytmetyka wektorowa Wykresy Prezentacja danych na wykresie, przygotowanie wielu wykresów oraz wielu okien z wykresami, zapisywanie i drukowanie wykresów Programowanie w Octave cz. I: Skrypty Tworzenie i edycja skryptu, uruchamianie i debugowanie skryptów, Instrukcje sterujące wykonywanie programu If else, switch, for, while Programowanie w Octave cz. II: Funkcje Macierze i wektory macierze, transpozycja, funkcje tworzące macierze, budowanie złożonych macierzy, macierz jako tablica, wyodrębnianie bitów z macierzy, podstawowe funkcje macierzowe Równanie liniowe i nieliniowe Więcej wykresów Budowanie wielu wykresów na jednym rysunku, wykresy 3D, zmiana perspektywy, wykresy powierzchniowe, rysunki i filmy,  Matematyka macierzowa Wartości własne, dekompozycja  Liczby zespolone Wykresy liczb zespolonych Statystyka i przetwarzanie danych Budowanie graficznego interfejsu użytkownika
matlabml1 Introduction to Machine Learning with MATLAB 21 godz. MATLAB Basics MATLAB More Advanced Features BP Neural Network RBF, GRNN and PNN Neural Networks SOM Neural Networks Support Vector Machine, SVM Extreme Learning Machine, ELM Decision Trees and Random Forests Genetic Algorithm, GA Particle Swarm Optimization, PSO Ant Colony Algorithm, ACA Simulated Annealing, SA Dimenationality Reduction and Feature Selection
dladv Advanced Deep Learning 28 godz. Machine Learning Limitations Machine Learning, Non-linear mappings Neural Networks Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent Back Propagation Deep Sparse Coding Sparse Autoencoders (SAE) Convolutional Neural Networks (CNNs) Successes: Descriptor Matching Stereo-based Obstacle Avoidance for Robotics Pooling and invariance Visualization/Deconvolutional Networks Recurrent Neural Networks (RNNs) and their optimizaiton Applications to NLP RNNs continued, Hessian-Free Optimization Language analysis: word/sentence vectors, parsing, sentiment analysis, etc. Probabilistic Graphical Models Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines Hopfield Networks, (Restricted) Bolzmann Machines Deep Belief Nets, Stacked RBMs Applications to NLP , Pose and Activity Recognition in Videos Recent Advances Large-Scale Learning Neural Turing Machines  

Najbliższe szkolenia

SzkolenieData KursuCena szkolenia [Zdalne / Stacjonarne]
Machine Learning with PredictionIO - Kielce, ul. Warszawska 19pon., 2017-06-12 09:0024430PLN / 8003PLN
Machine Learning Fundamentals with R - Rzeszów, Plac Wolności 13wt., 2017-06-13 09:007000PLN / 3900PLN
Artificial Neural Networks, Machine Learning, Deep Thinking - Szczecin, ul. Małopolska 23śr., 2017-06-14 09:0012370PLN / 4498PLN

Other regions

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Kursy w promocyjnej cenie

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne / Stacjonarne]
Programowanie w języku Python Szczecin, ul. Sienna 9 pon., 2017-05-29 09:00 10000PLN / 4448PLN
Facebook w marketingu i reklamie Lublin, ul. Spadochroniarzy 9 pt., 2017-06-02 09:00 1881PLN / 1002PLN
MongoDB for Administrators Kraków, ul. Rzemieślnicza 1 wt., 2017-06-06 09:00 3861PLN / 2087PLN
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Adobe Photoshop Elements Gdynia, ul. Ejsmonda 2 śr., 2017-06-07 09:00 1881PLN / 1127PLN
Microsoft Office Excel - moduł Business Intelligence Gdynia, ul. Ejsmonda 2 śr., 2017-06-07 09:00 2673PLN / 1391PLN
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Techniki DTP (InDesign, Photoshop, Illustrator, Acrobat) Opole, Władysława Reymonta 29 pon., 2017-06-12 09:00 5940PLN / 4230PLN
Spring i Hibernate w tworzeniu aplikacji w języku Java Poznań, Garbary 100/63 wt., 2017-06-13 09:00 7722PLN / 3358PLN
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Build applications with Oracle Application Express (APEX) Katowice ul. Opolska 22 pon., 2017-06-19 09:00 9801PLN / 4720PLN
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