Neural Networks Training Courses

Neural Networks Training

Neural Networks courses

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

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

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

Neural Networks Fundamentals using TensorFlow as Example

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for

Commerzbank AG

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Neural Networks Course Outlines

Code Name Duration Overview
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.
undnn Understanding Deep Neural Networks 35 hours This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy. Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: have a good understanding on deep neural networks(DNN), CNN and RNN understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging   Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject. The Duration of the complete course will be around 70 hours and not 35 hours. Part 1 – Deep Learning and DNN Concepts Introduction AI, Machine Learning & Deep Learning History, basic concepts and usual applications of artificial intelligence far Of the fantasies carried by this domain Collective Intelligence: aggregating knowledge shared by many virtual agents Genetic algorithms: to evolve a population of virtual agents by selection Usual Learning Machine: definition. Types of tasks: supervised learning, unsupervised learning, reinforcement learning Types of actions: classification, regression, clustering, density estimation, reduction of dimensionality Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree Machine learning VS Deep Learning: problems on which Machine Learning remains Today the state of the art (Random Forests & XGBoosts)   Basic Concepts of a Neural Network (Application: multi-layer perceptron) Reminder of mathematical bases. Definition of a network of neurons: classical architecture, activation and Weighting of previous activations, depth of a network Definition of the learning of a network of neurons: functions of cost, back-propagation, Stochastic gradient descent, maximum likelihood. Modeling of a neural network: modeling input and output data according to The type of problem (regression, classification ...). Curse of dimensionality. Distinction between Multi-feature data and signal. Choice of a cost function according to the data. Approximation of a function by a network of neurons: presentation and examples Approximation of a distribution by a network of neurons: presentation and examples Data Augmentation: how to balance a dataset Generalization of the results of a network of neurons. Initialization and regularization of a neural network: L1 / L2 regularization, Batch Normalization ... Optimization and convergence algorithms   Standard ML / DL Tools A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned. Data management tools: Apache Spark, Apache Hadoop Tools Machine Learning: Numpy, Scipy, Sci-kit DL high level frameworks: PyTorch, Keras, Lasagne Low level DL frameworks: Theano, Torch, Caffe, Tensorflow   Convolutional Neural Networks (CNN). Presentation of the CNNs: fundamental principles and applications Basic operation of a CNN: convolutional layer, use of a kernel, Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D and 3D. Presentation of the different CNN architectures that brought the state of the art in classification Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of Innovations brought about by each architecture and their more global applications (Convolution 1x1 or residual connections) Use of an attention model. Application to a common classification case (text or image) CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of Main strategies for increasing feature maps for image generation.   Recurrent Neural Networks (RNN). Presentation of RNNs: fundamental principles and applications. Basic operation of the RNN: hidden activation, back propagation through time, Unfolded version. Evolutions towards the Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory). Presentation of the different states and the evolutions brought by these architectures Convergence and vanising gradient problems Classical architectures: Prediction of a temporal series, classification ... RNN Encoder Decoder type architecture. Use of an attention model. NLP applications: word / character encoding, translation. Video Applications: prediction of the next generated image of a video sequence. Generational models: Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN). Presentation of the generational models, link with the CNNs Auto-encoder: reduction of dimensionality and limited generation Variational Auto-encoder: generational model and approximation of the distribution of a given. Definition and use of latent space. Reparameterization trick. Applications and Limits observed Generative Adversarial Networks: Fundamentals. Dual Network Architecture (Generator and discriminator) with alternate learning, cost functions available. Convergence of a GAN and difficulties encountered. Improved convergence: Wasserstein GAN, Began. Earth Moving Distance. Applications for the generation of images or photographs, text generation, super- resolution. Deep Reinforcement Learning. Presentation of reinforcement learning: control of an agent in a defined environment By a state and possible actions Use of a neural network to approximate the state function Deep Q Learning: experience replay, and application to the control of a video game. Optimization of learning policy. On-policy && off-policy. Actor critic architecture. A3C. Applications: control of a single video game or a digital system.   Part 2 – Theano for Deep Learning Theano Basics Introduction Installation and Configuration Theano Functions inputs, outputs, updates, givens Training and Optimization of a neural network using Theano Neural Network Modeling Logistic Regression Hidden Layers Training a network Computing and Classification Optimization Log Loss Testing the model Part 3 – DNN using 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 Prepare the Data Download 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   Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability): Tensorflow - Advanced Usage Threading and Queues Distributed TensorFlow Writing Documentation and Sharing your Model Customizing Data Readers Manipulating TensorFlow Model Files TensorFlow Serving Introduction Basic Serving Tutorial Advanced Serving Tutorial Serving Inception Model Tutorial
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.
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
aiint 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
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.
neuralnet 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
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.  
rneuralnet 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
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        
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
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.
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
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.
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
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
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
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  
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
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
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
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
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
matlabdl Matlab for Deep Learning 14 hours In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to: Build a deep learning model Automate data labeling Work with models from Caffe and TensorFlow-Keras Train data using multiple GPUs, the cloud, or clusters Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice To request a customized course outline for this training, please contact us.
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
mlbankingr Machine Learning for Banking (with R) 28 hours In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete live team projects. Introduction Difference between statistical learning (statistical analysis) and machine learning Adoption of machine learning technology by finance and banking companies Different Types of Machine Learning Supervised learning vs unsupervised learning Iteration and evaluation Bias-variance trade-off Combining supervised and unsupervised learning (semi-supervised learning) Machine Learning Languages and Toolsets Open source vs proprietary systems and software R vs Python vs Matlab Libraries and frameworks Machine Learning Case Studies Consumer data and big data Assessing risk in consumer and business lending Improving customer service through sentiment analysis Detecting identity fraud, billing fraud and money laundering Introduction to R Installing the RStudio IDE Loading R packages Data structures Vectors Factors Lists Data Frames Matrixes and Arrays How to Load Machine Learning Data Databases, data warehouses and streaming data Distributed storage and processing with Hadoop and Spark Importing data from a database Importing data from Excel and CSV Modeling Business Decisions with Supervised Learning Classifying your data (classification) Using regression analysis to predict outcome Choosing from available machine learning algorithms Understanding decision tree algorithms Understanding random forest algorithms Model evaluation Exercise Regression Analysis Linear regression Generalizations and Nonlinearity Exercise Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercise Hands-on: Building an Estimation Model Assessing lending risk based on customer type and history Evaluating the performance of Machine Learning Algorithms Cross-validation and resampling Bootstrap aggregation (bagging) Exercise Modeling Business Decisions with Unsupervised Learning K-means clustering Challenges of unsupervised learning Beyond K-means Exercise Hands-on: Building a Recommendation System Analyzing past customer behavior to improve new service offerings Extending your company's capabilities Developing models in the cloud Accelerating machine learning with additional GPUs Beyond machine learning: Artificial Intelligence (AI) Applying Deep Learning neural networks for computer vision, voice recognition and text analysis Closing Remarks
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
mlbankingpython_ Machine Learning for Banking (with Python) 21 hours In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Python will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete live team projects. Introduction Difference between statistical learning (statistical analysis) and machine learning Adoption of machine learning technology and talent by finance and banking companies Different Types of Machine Learning Supervised learning vs unsupervised learning Iteration and evaluation Bias-variance trade-off Combining supervised and unsupervised learning (semi-supervised learning) Machine Learning Languages and Toolsets Open source vs proprietary systems and software Python vs R vs Matlab Libraries and frameworks Machine Learning Case Studies Consumer data and big data Assessing risk in consumer and business lending Improving customer service through sentiment analysis Detecting identity fraud, billing fraud and money laundering Hands-on: Python for Machine Learning Preparing the Development Environment Obtaining Python machine learning libraries and packages Working with scikit-learn and PyBrain How to Load Machine Learning Data Databases, data warehouses and streaming data Distributed storage and processing with Hadoop and Spark Exported data and Excel Modeling Business Decisions with Supervised Learning Classifying your data (classification) Using regression analysis to predict outcome Choosing from available machine learning algorithms Understandind decision tree algorithms Understanding random forest algorithms Model evaluation Exercise Regression Analysis Linear regression Generalizations and Nonlinearity Exercise Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercise Hands-on: Building an Estimation Model Assessing lending risk based on customer type and history Evaluating the performance of Machine Learning Algorithms Cross-validation and resampling Bootstrap aggregation (bagging) Exercise Modeling Business Decisions with Unsupervised Learning K-means clustering Challenges of unsupervised learning Beyond K-means Exercise Hands-on: Building a Recommendation System Analyzing past customer behavior to improve new service offerings Extending your company's capabilities Developing models in the cloud Accelerating machine learning with GPU Beyond machine learning: Artificial Intelligence (AI) Applying Deep Learning neural networks for computer vision, voice recognition and text analysis Closing Remarks
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
mlbankingpython Machine Learning for Banking (with Python) - Bespoke 28 hours In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Deep learning techniques are covered in the latter part of the course. Python will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete live team projects. Introduction Difference between statistical learning (statistical analysis) and machine learning Adoption of machine learning technology and talent by finance and banking companies Different Types of Machine Learning Supervised learning vs unsupervised learning Iteration and evaluation Bias-variance trade-off Combining supervised and unsupervised learning (semi-supervised learning) Machine Learning Languages and Toolsets Open source vs proprietary systems and software Python vs R vs Matlab Libraries and frameworks Machine Learning Case Studies Consumer data and big data Assessing risk in consumer and business lending Improving customer service through sentiment analysis Detecting identity fraud, billing fraud and money laundering Hands-on: Python for Machine Learning Preparing the Development Environment Obtaining Python machine learning libraries and packages Working with scikit-learn and PyBrain How to Load Machine Learning Data Databases, data warehouses and streaming data Distributed storage and processing with Hadoop and Spark Exported data and Excel Modeling Business Decisions with Supervised Learning Classifying your data (classification) Using regression analysis to predict outcome Choosing from available machine learning algorithms Understanding decision tree algorithms Understanding random forest algorithms Model evaluation Exercise Regression Analysis Linear regression Generalizations and Nonlinearity Exercise Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercise Hands-on: Building an Estimation Model Assessing lending risk based on customer type and history Evaluating the performance of Machine Learning Algorithms Cross-validation and resampling Bootstrap aggregation (bagging) Exercise Modeling Business Decisions with Unsupervised Learning K-means clustering Challenges of unsupervised learning Beyond K-means Exercise Hands-on: Building a Recommendation System Analyzing past customer behavior to improve new service offerings Introduction to Neural Networks and Deep Learning Layers and nodes Convolutional neural networks Recurrent neural networks Multilayer perceptrons Frameworks: Theano, TensorFlow, Keras Exercise Hands-on: Building an AI system Monitoring big data to detect money laundering and billing fraud Extending your company's capabilities Developing models in the cloud Accelerating machine learning with GPU Beyond machine learning: Artificial Intelligence (AI) Applying neural networks for computer vision, voice recognition and text analysis Closing Remarks

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Applied Machine Learning - DubaiTue, 2018-02-13 09:304000USD / 5500USD

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