Szkolenia Sieci Neuronowe w Opole

Sieci Neuronowe Training in Opole

Neural Networks courses

Opole, Władysława Reymonta 29

sale NobleProg Opole
Władysława Reymonta 29
Opole 46-020
Poland
PL
Opole, Władysława Reymonta 29

Testi...Client Testimonials

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

Introduction to the use of neural networks

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq - Tricentis GmbH

Introduction to the use of neural networks

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq - Tricentis GmbH

Introduction to the use of neural networks

the interactive part, tailored to our specific needs

Thomas Stocker - Tricentis GmbH

From Data to Decision with Big Data and Predictive Analytics

zakres materialu

Maciej Jonczyk - Orange Polska

From Data to Decision with Big Data and Predictive Analytics

usystematyzowanie wiedzy z dziedziny ML

- Orange Polska

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Introduction to Deep Learning

The topic is very interesting

Wojciech Baranowski - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training

Grzegorz Mianowski - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Topic. Very interesting!

Piotr - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

- Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Interesting subject

Wojciech Wilk - Dolby Poland Sp. z o.o.

Neural Networks Fundamentals using TensorFlow as Example

Knowledgeable trainer

Sridhar Voorakkara - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview.Good background into why Tensorflow operates as it does.

Kieran Conboy - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane - INTEL R&D IRELAND LIMITED

Machine Learning and Deep Learning

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company

Sebastiaan Holman - Travix International

Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

Jean-Paul van Tillo - Travix International

Machine Learning and Deep Learning

Coverage and depth of topics

Anirban Basu - Travix International

Artificial Intelligence Overview

Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.

Leszek K - PwC Polska Sp. z o.o.

Neural Network in R

new insights in deep machine learning

Josip Arneric - Faculty of Economics and Business Zagreb

Neural Network in R

We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.

Tea Poklepovic - Faculty of Economics and Business Zagreb

Neural Network in R

Graphs in R :)))

- Faculty of Economics and Business Zagreb

Introduction to Deep Learning

The deep knowledge of the trainer about the topic.

Sebastian Görg - FANUC Europe Corporation

Sieci Neuronowe Course Events - Opole

Kod Nazwa Miejscowość Czas trwania Data Kursu PHP Cena szkolenia [Zdalne / Stacjonarne]
iop Inteligencja obliczeniowa w praktyce Opole, Władysława Reymonta 29 7 hours czw., 2017-11-02 09:00 6000PLN / 2268PLN
cntk Using Computer Network ToolKit (CNTK) Opole, Władysława Reymonta 29 28 hours pon., 2017-11-06 09:00 25020PLN / 9382PLN
mlintro Introduction to Machine Learning Opole, Władysława Reymonta 29 7 hours pon., 2017-11-06 09:00 5060PLN / 1983PLN
deeplearning1 Introduction to Deep Learning Opole, Władysława Reymonta 29 21 hours pon., 2017-11-06 09:00 18260PLN / 6883PLN
neuralnet Introduction to the use of neural networks Opole, Władysława Reymonta 29 7 hours czw., 2017-11-09 09:00 4580PLN / 1797PLN
pjn Przetwarzanie języka naturalnego Opole, Władysława Reymonta 29 7 hours pt., 2017-11-10 09:00 6000PLN / 2268PLN
aiintrozero From Zero to AI Opole, Władysława Reymonta 29 35 hours pon., 2017-11-13 09:00 24900PLN / 9795PLN
d2dbdpa From Data to Decision with Big Data and Predictive Analytics Opole, Władysława Reymonta 29 21 hours wt., 2017-11-14 09:00 29220PLN / 10205PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Opole, Władysława Reymonta 29 28 hours wt., 2017-11-14 09:00 30980PLN / 11188PLN
mtdintob Metody Inteligencji Obliczeniowej Opole, Władysława Reymonta 29 7 hours śr., 2017-11-15 09:00 6000PLN / 2268PLN
aiauto Artificial Intelligence in Automotive Opole, Władysława Reymonta 29 14 hours czw., 2017-11-16 09:00 13800PLN / 5082PLN
sysagent Systemy wieloagentowe Opole, Władysława Reymonta 29 7 hours pt., 2017-11-17 09:00 6000PLN / 2268PLN
MLFWR1 Machine Learning Fundamentals with R Opole, Władysława Reymonta 29 14 hours czw., 2017-11-23 09:00 7000PLN / 4400PLN
appliedml Applied Machine Learning Opole, Władysława Reymonta 29 14 hours pon., 2017-11-27 09:00 10110PLN / 3964PLN
rneuralnet Sieci Neuronowe w R Opole, Władysława Reymonta 29 14 hours wt., 2017-12-05 09:00 7000PLN / 4400PLN
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Opole, Władysława Reymonta 29 21 hours pon., 2017-12-11 09:00 12370PLN / 5098PLN
mldt Machine Learning and Deep Learning Opole, Władysława Reymonta 29 21 hours śr., 2017-12-13 09:00 25100PLN / 8956PLN
aiint Artificial Intelligence Overview Opole, Władysława Reymonta 29 7 hours pon., 2017-12-18 09:00 3900PLN / 1950PLN
iop Inteligencja obliczeniowa w praktyce Opole, Władysława Reymonta 29 7 hours czw., 2017-12-21 09:00 6000PLN / 2268PLN
mlintro Introduction to Machine Learning Opole, Władysława Reymonta 29 7 hours śr., 2018-01-03 09:00 5060PLN / 1983PLN
aiauto Artificial Intelligence in Automotive Opole, Władysława Reymonta 29 14 hours czw., 2018-01-11 09:00 13800PLN / 5082PLN
MLFWR1 Machine Learning Fundamentals with R Opole, Władysława Reymonta 29 14 hours pon., 2018-01-15 09:00 7000PLN / 4400PLN
sysagent Systemy wieloagentowe Opole, Władysława Reymonta 29 7 hours wt., 2018-01-16 09:00 6000PLN / 2268PLN
appliedml Applied Machine Learning Opole, Władysława Reymonta 29 14 hours wt., 2018-01-16 09:00 10110PLN / 3964PLN
aiintrozero From Zero to AI Opole, Władysława Reymonta 29 35 hours pon., 2018-01-22 09:00 24900PLN / 9795PLN
deeplearning1 Introduction to Deep Learning Opole, Władysława Reymonta 29 21 hours wt., 2018-01-23 09:00 18260PLN / 6883PLN
d2dbdpa From Data to Decision with Big Data and Predictive Analytics Opole, Władysława Reymonta 29 21 hours wt., 2018-01-23 09:00 29220PLN / 10205PLN
neuralnet Introduction to the use of neural networks Opole, Władysława Reymonta 29 7 hours wt., 2018-01-23 09:00 4580PLN / 1797PLN
mtdintob Metody Inteligencji Obliczeniowej Opole, Władysława Reymonta 29 7 hours wt., 2018-01-23 09:00 6000PLN / 2268PLN
pjn Przetwarzanie języka naturalnego Opole, Władysława Reymonta 29 7 hours czw., 2018-02-01 09:00 6000PLN / 2268PLN
mldt Machine Learning and Deep Learning Opole, Władysława Reymonta 29 21 hours wt., 2018-02-06 09:00 25100PLN / 8956PLN
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Opole, Władysława Reymonta 29 21 hours wt., 2018-02-06 09:00 12370PLN / 5098PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Opole, Władysława Reymonta 29 28 hours wt., 2018-02-13 09:00 30980PLN / 11188PLN
cntk Using Computer Network ToolKit (CNTK) Opole, Władysława Reymonta 29 28 hours wt., 2018-02-20 09:00 25020PLN / 9382PLN
mlintro Introduction to Machine Learning Opole, Władysława Reymonta 29 7 hours pt., 2018-02-23 09:00 5060PLN / 1983PLN
rneuralnet Sieci Neuronowe w R Opole, Władysława Reymonta 29 14 hours pon., 2018-02-26 09:00 7000PLN / 4400PLN
appliedml Applied Machine Learning Opole, Władysława Reymonta 29 14 hours czw., 2018-03-08 09:00 10110PLN / 3964PLN
iop Inteligencja obliczeniowa w praktyce Opole, Władysława Reymonta 29 7 hours pt., 2018-03-16 09:00 6000PLN / 2268PLN
aiint Artificial Intelligence Overview Opole, Władysława Reymonta 29 7 hours pt., 2018-03-23 09:00 3900PLN / 1950PLN
mtdintob Metody Inteligencji Obliczeniowej Opole, Władysława Reymonta 29 7 hours wt., 2018-03-27 09:00 6000PLN / 2268PLN
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Opole, Władysława Reymonta 29 21 hours wt., 2018-04-03 09:00 12370PLN / 5098PLN
deeplearning1 Introduction to Deep Learning Opole, Władysława Reymonta 29 21 hours pon., 2018-04-09 09:00 18260PLN / 6883PLN
mldt Machine Learning and Deep Learning Opole, Władysława Reymonta 29 21 hours śr., 2018-04-11 09:00 25100PLN / 8956PLN
sysagent Systemy wieloagentowe Opole, Władysława Reymonta 29 7 hours pt., 2018-04-13 09:00 6000PLN / 2268PLN
neuralnet Introduction to the use of neural networks Opole, Władysława Reymonta 29 7 hours wt., 2018-04-17 09:00 4580PLN / 1797PLN
rneuralnet Sieci Neuronowe w R Opole, Władysława Reymonta 29 14 hours wt., 2018-04-17 09:00 7000PLN / 4400PLN
d2dbdpa From Data to Decision with Big Data and Predictive Analytics Opole, Władysława Reymonta 29 21 hours śr., 2018-04-18 09:00 29220PLN / 10205PLN
aiauto Artificial Intelligence in Automotive Opole, Władysława Reymonta 29 14 hours śr., 2018-04-18 09:00 13800PLN / 5082PLN
MLFWR1 Machine Learning Fundamentals with R Opole, Władysława Reymonta 29 14 hours śr., 2018-04-18 09:00 7000PLN / 4400PLN
aiintrozero From Zero to AI Opole, Władysława Reymonta 29 35 hours pon., 2018-04-23 09:00 24900PLN / 9795PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Opole, Władysława Reymonta 29 28 hours pon., 2018-04-23 09:00 30980PLN / 11188PLN
cntk Using Computer Network ToolKit (CNTK) Opole, Władysława Reymonta 29 28 hours wt., 2018-04-24 09:00 25020PLN / 9382PLN
pjn Przetwarzanie języka naturalnego Opole, Władysława Reymonta 29 7 hours śr., 2018-04-25 09:00 6000PLN / 2268PLN
mlintro Introduction to Machine Learning Opole, Władysława Reymonta 29 7 hours czw., 2018-04-26 09:00 5060PLN / 1983PLN
appliedml Applied Machine Learning Opole, Władysława Reymonta 29 14 hours wt., 2018-05-01 09:00 10110PLN / 3964PLN
iop Inteligencja obliczeniowa w praktyce Opole, Władysława Reymonta 29 7 hours pt., 2018-05-04 09:00 6000PLN / 2268PLN
aiint Artificial Intelligence Overview Opole, Władysława Reymonta 29 7 hours pt., 2018-05-11 09:00 3900PLN / 1950PLN
mtdintob Metody Inteligencji Obliczeniowej Opole, Władysława Reymonta 29 7 hours śr., 2018-05-16 09:00 6000PLN / 2268PLN

Plany Kursów

Kod Nazwa Czas trwania Spis treści
d2dbdpa 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
Fairsec Fairsec: Setting up a CNN-based machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

Introduction
    Why Neural Machine Translation?

Overview of the Torch project

Overview of a Convolutional Neural Machine Translation model
    Convolutional Sequence to Sequence Learning
    Convolutional Encoder Model for Neural Machine Translation
    Standard LSTM-based model

Overview of training approaches
    About GPUs and CPUs
    Fast beam search generation

Installation and setup

Evaluating pre-trained models

Preprocessing your data

Training the model

Translating

Converting a trained model to use CPU-only operations

Joining to the community

Closing remarks

annmldt 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
Fairseq Fairseq: Setting up a CNN-based machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

Introduction
    Why Neural Machine Translation?

Overview of the Torch project

Overview of a Convolutional Neural Machine Translation model
    Convolutional Sequence to Sequence Learning
    Convolutional Encoder Model for Neural Machine Translation
    Standard LSTM-based model

Overview of training approaches
    About GPUs and CPUs
    Fast beam search generation

Installation and setup

Evaluating pre-trained models

Preprocessing your data

Training the model

Translating

Converting a trained model to use CPU-only operations

Joining to the community

Closing remarks

deeplearning1 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
facebooknmt Facebook NMT: Setting up a neural machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course

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

Note

  • If you wish to use specific source and target language content, please contact us to arrange.

Introduction
    Why Neural Machine Translation?
    Borrowing from image recognition techniques

Overview of the Torch and Caffe2 projects

Overview of a Convolutional Neural Machine Translation model
    Convolutional Sequence to Sequence Learning
    Convolutional Encoder Model for Neural Machine Translation
    Standard LSTM-based model

Overview of training approaches
    About GPUs and CPUs
    Fast beam search generation

Installation and setup

Evaluating pre-trained models

Preprocessing your data

Training the model

Translating

Converting a trained model to use CPU-only operations

Joining to the community

Closing remarks

sysagent Systemy wieloagentowe 7 hours

1. Wstęp systemy wieloagentowe

a. czym jest agent programowy

b. rodzaje agentów

c. platformawieloagentowa i społeczność agentów

d. analogia do systemów żywych

2. Teoria

a. Architektury systemów wieloagentowych

  • architektury logiczne
  • architektury reaktywne
  • architektury BDI (belief, desires, intentions)
  • architektury AGR (Agent/Group/Role)
  • inne architektury

b. Inteligencja agenta i interakcja z otoczeniem

  • pozyskiwanie i gromadzenie wiedzy
  • interakcja ze środowiskiem w którym funkcjonuje agent
  • komunikacja i interakcja z innymi agentami (wymiana wiedzy)
  • rozwiązywanie konfliktów (negocjacje)
  • planowanie i podejmowanie decyzji

c. Wybrane algorytmy społecznościowe

  • kolonia mrówek
  • stado (ławica, rój cząstek)
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units 7 hours

The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision.

In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

  • Train various types of neural networks on large amounts of data
  • Use TPUs to speed up the inference process by up to two orders of magnitude
  • Utilize TPUs to process intensive applications such as image search, cloud vision and photos

Audience

  • Developers
  • Researchers
  • Engineers
  • Data scientists

Format of the course

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

To request a customized course outline for this training, please contact us.

pjn Przetwarzanie języka naturalnego 7 hours

1. Wprowadzenie

2. Zastosowania NLP

  • Podstawowe pojęcia
  • Narzędzia NLP

3. Podstawy języka Perl

  • Struktury danych
  • Wyrażenia regularne
  • Parsowanie i tokenizacja

4 . Podstawy narzędzi RDBMS

  • Pająki internetowe
  • Korpus tekstowy
  • Własności statystyczne
  • Listy stopsłów
  • Indeksowanie dokumentów

5. Wyszukiwarka dokumentów

  • analiza leksykalna
  • wyszukiwanie wzorca
  • słowniki i automaty słownikowe
  • analiza morfologiczna
  • techniki ngramów
  • podobieństwo dokumentów
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x 21 hours

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 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.

To request a customized course outline for this training, please contact us.

iop Inteligencja obliczeniowa w praktyce 7 hours

1. Obszary zastosowań

  • klasyfikacja (metody jakościowe, np. WTA)
  • regresja (metody ilościowe, np. próg decyzyjny)

2. Surowe dane

3. Przetwarzanie wstępne danych, sygnałów (np. normalizacja, PCA, FFT itp.)

4. Dobór elementów do zbioru uczącego i testowego (np. walidacja krzyżowa)

5.  Wybór metody inteligencji obliczeniowej

6.  Optymalizacja parametrów treningu (np. algorytmy genetyczne)

7. Ocena uzyskanych wyników (np. krzywa ROC)

8. Przykładowe zastosowania MIO:

  • rozpoznawanie osób na podstawie gestów z ekranu
  • rozpoznawanie gestów, ruchów dłonią
  • identyfikacja rodzaju atramentu i papieru
  • diagnozowanie dysfunkcji mięśnia sercowego
  • analiza lotnych związków organicznych przy użyciu elektronicznego nosa (klasyfikacja gatunków herbaty i aproksymacja stężenia fenolu)
  • szacowanie wypracowania pompy wyporowej
snorkel Snorkel: Rapidly process training data 7 hours

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

To request a customized course outline for this training, please contact us.

 

mtdintob Metody Inteligencji Obliczeniowej 7 hours

1. Wstęp Sztuczna inteligencja

a. słaba i silna sztuczna inteligencja

b. sztuczna inteligencja a inteligencja obliczeniowa

c. klasyfikacja metod inteligencji obliczeniowej

d. analogie do systemów żywych

2. Metody inteligencji obliczeniowej

a. sztuczne sieci neuronowe

  • klasyfikacja i typy sieci neuronowych
  • model sztucznego neuronu
  • topologia
  • metody i algorytmy uczenia
  • sieci neuronowe: SOM, MLP, PNN, LVQ, RNN, RBF, GRNN

b. systemy rozmyte

  • logika rozmyta
  • zbiory rozmyte i funkcje przynależności
  • wnioskowanie przybliżone
  • zasada działania
  • model Mamdani i Sugeno

c. maszyna wektorów nośnych

  • zasada działania
  • typy funkcji jądra
  • typy wielokrotnej klasyfikacji
  • mocne i słabe strony

d. obliczenia ewolucyjne

  • algorytmy genetyczne
  • metody selekcji
  • skalowanie funkcji przystosowania
  • operatory genetyczne
  • porównanie algorytmów ewolucyjnych

e. inteligencja roju

f. inteligentne agenty

g. algorytm knajbliższych sąsiadów

h. systemy hybrydowe

  • ewolucyjnoneuronowe
  • neuronoworozmyte
  • ewolucyjnorozmyte
encogintro Encog: Introduction to Machine Learning 14 hours

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

To request a customized course outline for this training, please contact us.

cntk 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 GPUs¹ 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

¹ The topic related to the use of CNTK with a GPU is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

encogadv Encog: Advanced Machine Learning 14 hours

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

To request a customized course outline for this training, please contact us.

aiintrozero From Zero to AI 35 hours

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 combine solutions into hybrid approach
aiauto Artificial Intelligence in Automotive 14 hours

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

 

 

 

 

aiint Artificial Intelligence Overview 7 hours

Kurs ten został stworzony dla menadżerów, architektów, analityków biznesowych i systemowych, menedżerów oprogramowania oraz wszystkich zainteresowanych przeglądem stosowania sztucznej inteligencji i prognozą dla jej rozwoju.

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
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours

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
neuralnet Introduction to the use of neural networks 7 hours

Szkolenie skierowane jest do osób, które chcą zapoznać się z podstawami sieci neuronowych oraz ich zastosowań.

Podstawy

  • Czy komputery mogą myśleć?
  • Podejście deklaratywne i imperatywne do rozwiązywania problemów
  • Cel bedań nad sztuczna inteligencją
  • Definicja sztucznej inteligencji. Test Turinga. Inne wyznaczniki
  • Rozwój koncepcji inteligentnych systemów
  • Najważniejsze osiągniącia i kierunki rozwoju

Sieci neuronowe

  • Podstawy
  • Koncepcja neuronu i sieci neuronowych
  • Uproszczony model mózgu
  • Możliwości neuronu
  • Problem XOR i charakter podziału wartości
  • Polimorficzny charakter funkcji sigmoidalnej
  • Pozostałe funkcje aktywacji
  • Budowa sieci neuronowych
  • Koncepcja łączenie neuronów
  • Sieć neuronowa jako węzły
  • Budowa sieci
  • Neurony
  • Warstwy
  • Wagi
  • Dane wejściowe i wyjściowe
  • Zakresy 0..1
  • Normalizacja
  • Uczenie sieci neuronowych
  • Propagacja wsteczna
  • Kroki propagacji
  • Algorytmy uczenia sieci
  • Zakres zastosowań
  • Estymacja
  • Problemy z możliwością przybliżenia wyniku
  • Przykłady
  • Problem XOR
  • Totolotek?
  • Kursy akcji
  • OCR i rozpoznawanie wzorów obrazów
  • Inne zastosowania
  • Modelowanie sieci neuronowej realizującej zadanie przewidywania kursów akcji giełdowych

Problemy na dziś

  • Eksplocja kombinatoryczna i problemy gier
  • Test Turinga raz jeszcze
  • Zbytnia ufność w możliwości komputerów
datamodeling Pattern Recognition 35 hours

This course provides an introduction into the field of pattern recognition and machine learning. It 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, instructor 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

 

rneuralnet Sieci Neuronowe w R 14 hours Szkolenie jest wprowadzeniem do wdrożenia sieci neuronowych w życiu codziennym wykorzystując oprogramowanie R-project.

Introduction to Neural Networks

  1. What are Neural Networks
  2. What is current status in applying neural networks
  3. Neural Networks vs regression models
  4. Supervised and Unsupervised learning

Overview of packages available

  1. nnet, neuralnet and others
  2. differences between packages and itls limitations
  3. 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
Torch Torch: Getting started with Machine and Deep Learning 21 hours

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

MLFWR1 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
OpenNN OpenNN: Implementing neural networks 14 hours

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

appliedml 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
mldt Machine Learning and Deep Learning 21 hours

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

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

mlintro 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
opennmt OpenNMT: Setting up a Neural Machine Translation system 7 hours

OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit.

In this 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.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course

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

Introduction
    Why Neural Machine Translation?

Overview of the Torch project

Installation and setup

Preprocessing your data

Training the model

Translating

Using pre-trained models

Working with Lua scripts

Using extensions

Troubleshooting

Joining the community

Closing remarks

Other regions

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