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Neuralnettf |
Neural Networks Fundamentals using TensorFlow as Example |
28 godz. |
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics
Inputs and Placeholders
Build the GraphS
Inference
Loss
Training
Train the Model
The Graph
The Session
Train Loop
Evaluate the Model
Build the Eval Graph
Eval Output
The Perceptron
Activation functions
The perceptron learning algorithm
Binary classification with the perceptron
Document classification with the perceptron
Limitations of the perceptron
From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Multilayer perceptrons
Minimizing the cost function
Forward propagation
Back propagation
Improving the way neural networks learn
Convolutional Neural Networks
Goals
Model Architecture
Principles
Code Organization
Launching and Training the Model
Evaluating a Model
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datamodeling |
Pattern Recognition |
35 godz. |
This course provides an introduction into the field of pattern recognition and machine learning. It also touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, continuous feedback, and testing of knowledge and skills acquired.
Audience
Data analysts
PhD students, researchers and practitioners
Introduction
Probability theory, model selection, decision and information theory
Probability distributions
Linear models for regression and classification
Neural networks
Kernel methods
Sparse kernel machines
Graphical models
Mixture models and EM
Approximate inference
Sampling methods
Continuous latent variables
Sequential data
Combining models
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MLFWR1 |
Machine Learning Fundamentals with R |
14 godz. |
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means
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patternmatching |
Pattern Matching |
14 godz. |
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Audience
Engineers and developers seeking to develop machine vision applications
Manufacturing engineers, technicians and managers
Format of the course
This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Introduction
Computer Vision
Machine Vision
Pattern Matching vs Pattern Recognition
Alignment
Features of the target object
Points of reference on the object
Determining position
Determining orientation
Gauging
Setting tolerance levels
Measuring lengths, diameters, angles, and other dimensions
Rejecting a component
Inspection
Detecting flaws
Adjusting the system
Closing remarks
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mlfunpython |
Machine Learning Fundamentals with Python |
14 godz. |
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Machine Learning with Python
Choice of libraries
Add-on tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means
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Torch |
Torch: Getting started with Machine and Deep Learning |
21 godz. |
Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others.
In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.
By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.
Audience
Software developers and programmers wishing to enable Machine and Deep Learning within their applications
Format of the course
Overview of Machine and Deep Learning
In-class coding and integration exercises
Test questions sprinkled along the way to check understanding
Introduction to Torch
Like NumPy but with CPU and GPU implementation
Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking
Installing Torch
Linux, Windows, Mac
Bitmapi and Docker
Installing Torch packages
Using the LuaRocks package manager
Choosing an IDE for Torch
ZeroBrane Studio
Eclipse plugin for Lua
Working with the Lua scripting language and LuaJIT
Lua's integration with C/C++
Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.
Object orientation and serialization in Torch
Coding exercise
Loading a dataset in Torch
MNIST
CIFAR-10, CIFAR-100
Imagenet
Machine Learning in Torch
Deep Learning
Manual feature extraction vs convolutional networks
Supervised and Unsupervised Learning
Building a neural network with Torch
N-dimensional arrays
Image analysis with Torch
Image package
The Tensor library
Working with the REPL interpreter
Working with databases
Networking and Torch
GPU support in Torch
Integrating Torch
C, Python, and others
Embedding Torch
iOS and Android
Other frameworks and libraries
Facebook's optimized deep-learning modules and containers
Creating your own package
Testing and debugging
Releasing your application
The future of AI and Torch |
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appliedml |
Applied Machine Learning |
14 godz. |
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Bayesian Graphical Models
Factor Analysis (FA)
Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
Support Vector Machines (SVM) for regression and classification
Boosting
Ensemble models
Neural networks
Hidden Markov Models (HMM)
Space State Models
Clustering
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OpenNN |
OpenNN: Implementing neural networks |
14 godz. |
OpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning.
In this course we go over the principles of neural networks and use OpenNN to implement a sample application.
Audience
Software developers and programmers wishing to create Deep Learning applications.
Format of the course
Lecture and discussion coupled with hands-on exercises.
Introduction to OpenNN, Machine Learning and Deep Learning
Downloading OpenNN
Working with Neural Designer
Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics
OpenNN architecture
CPU parallelization
OpenNN classes
Data set, neural network, loss index, training strategy, model selection, testing analysis
Vector and matrix templates
Building a neural network application
Choosing a suitable neural network
Formulating the variational problem (loss index)
Solving the reduced function optimization problem (training strategy)
Working with datasets
The data matrix (columns as variables and rows as instances)
Learning tasks
Function regression
Pattern recognition
Compiling with QT Creator
Integrating, testing and debugging your application
The future of neural networks and OpenNN |
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mlintro |
Introduction to Machine Learning |
7 godz. |
This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Ridge regression
Clustering
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mldt |
Machine Learning and Deep Learning |
21 godz. |
This course covers AI (emphasizing Machine Learning and Deep Learning)Machine learning
Introduction to Machine Learning
Applications of machine learning
Supervised Versus Unsupervised Learning
Machine Learning Algorithms
Regression
Classification
Clustering
Recommender System
Anomaly Detection
Reinforcement Learning
Regression
Simple & Multiple Regression
Least Square Method
Estimating the Coefficients
Assessing the Accuracy of the Coefficient Estimates
Assessing the Accuracy of the Model
Post Estimation Analysis
Other Considerations in the Regression Models
Qualitative Predictors
Extensions of the Linear Models
Potential Problems
Bias-variance trade off [under-fitting/over-fitting] for regression models
Resampling Methods
Cross-Validation
The Validation Set Approach
Leave-One-Out Cross-Validation
k-Fold Cross-Validation
Bias-Variance Trade-Off for k-Fold
The Bootstrap
Model Selection and Regularization
Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model]
Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net]
Selecting the Tuning Parameter
Dimension Reduction Methods
Principal Components Regression
Partial Least Squares
Classification
Logistic Regression
The Logistic Model cost function
Estimating the Coefficients
Making Predictions
Odds Ratio
Performance Evaluation Matrices
[Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.]
Multiple Logistic Regression
Logistic Regression for >2 Response Classes
Regularized Logistic Regression
Linear Discriminant Analysis
Using Bayes’ Theorem for Classification
Linear Discriminant Analysis for p=1
Linear Discriminant Analysis for p >1
Quadratic Discriminant Analysis
K-Nearest Neighbors
Classification with Non-linear Decision Boundaries
Support Vector Machines
Optimization Objective
The Maximal Margin Classifier
Kernels
One-Versus-One Classification
One-Versus-All Classification
Comparison of Classification Methods
Introduction to Deep Learning
ANN Structure
Biological neurons and artificial neurons
Non-linear Hypothesis
Model Representation
Examples & Intuitions
Transfer Function/ Activation Functions
Typical classes of network architectures
Feed forward ANN.
Structures of Multi-layer feed forward networks
Back propagation algorithm
Back propagation - training and convergence
Functional approximation with back propagation
Practical and design issues of back propagation learning
Deep Learning
Artificial Intelligence & Deep Learning
Softmax Regression
Self-Taught Learning
Deep Networks
Demos and Applications
Lab:
Getting Started with R
Introduction to R
Basic Commands & Libraries
Data Manipulation
Importing & Exporting data
Graphical and Numerical Summaries
Writing functions
Regression
Simple & Multiple Linear Regression
Interaction Terms
Non-linear Transformations
Dummy variable regression
Cross-Validation and the Bootstrap
Subset selection methods
Penalization [Ridge, Lasso, Elastic Net]
Classification
Logistic Regression, LDA, QDA, and KNN,
Resampling & Regularization
Support Vector Machine
Resampling & Regularization
Artificial Neural Network
Deep Learning
Note:
For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.
Analysis of different data sets will be performed using R
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annmldt |
Artificial Neural Networks, Machine Learning, Deep Thinking |
21 godz. |
DAY 1 - ARTIFICIAL NEURAL NETWORKS
Introduction and ANN Structure.
Biological neurons and artificial neurons.
Model of an ANN.
Activation functions used in ANNs.
Typical classes of network architectures .
Mathematical Foundations and Learning mechanisms.
Re-visiting vector and matrix algebra.
State-space concepts.
Concepts of optimization.
Error-correction learning.
Memory-based learning.
Hebbian learning.
Competitive learning.
Single layer perceptrons.
Structure and learning of perceptrons.
Pattern classifier - introduction and Bayes' classifiers.
Perceptron as a pattern classifier.
Perceptron convergence.
Limitations of a perceptrons.
Feedforward ANN.
Structures of Multi-layer feedforward networks.
Back propagation algorithm.
Back propagation - training and convergence.
Functional approximation with back propagation.
Practical and design issues of back propagation learning.
Radial Basis Function Networks.
Pattern separability and interpolation.
Regularization Theory.
Regularization and RBF networks.
RBF network design and training.
Approximation properties of RBF.
Competitive Learning and Self organizing ANN.
General clustering procedures.
Learning Vector Quantization (LVQ).
Competitive learning algorithms and architectures.
Self organizing feature maps.
Properties of feature maps.
Fuzzy Neural Networks.
Neuro-fuzzy systems.
Background of fuzzy sets and logic.
Design of fuzzy stems.
Design of fuzzy ANNs.
Applications
A few examples of Neural Network applications, their advantages and problems will be discussed.
DAY -2 MACHINE LEARNING
The PAC Learning Framework
Guarantees for finite hypothesis set – consistent case
Guarantees for finite hypothesis set – inconsistent case
Generalities
Deterministic cv. Stochastic scenarios
Bayes error noise
Estimation and approximation errors
Model selection
Radmeacher Complexity and VC – Dimension
Bias - Variance tradeoff
Regularisation
Over-fitting
Validation
Support Vector Machines
Kriging (Gaussian Process regression)
PCA and Kernel PCA
Self Organisation Maps (SOM)
Kernel induced vector space
Mercer Kernels and Kernel - induced similarity metrics
Reinforcement Learning
DAY 3 - DEEP LEARNING
This will be taught in relation to the topics covered on Day 1 and Day 2
Logistic and Softmax Regression
Sparse Autoencoders
Vectorization, PCA and Whitening
Self-Taught Learning
Deep Networks
Linear Decoders
Convolution and Pooling
Sparse Coding
Independent Component Analysis
Canonical Correlation Analysis
Demos and Applications
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dmmlr |
Data Mining & Machine Learning with R |
14 godz. |
Introduction to Data mining and Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Dicriminant analysis
Logistic regression
K-Nearest neighbors
Support Vector Machines
Neural networks
Decision trees
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means
Advanced topics
Ensemble models
Mixed models
Boosting
Examples
Multidimensional reduction
Factor Analysis
Principal Component Analysis
Examples
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wdneo4j |
Wprowadzenie do Neo4j - grafowej bazy danych |
7 godz. |
Wprowadzenie do Neo4j
Instalacja i konfiguracja
Struktura aplikacji Neo4j
Relacyjne i grafowe sposoby reprezentacji danych
Model grafowy danych
Czy zagadnienie można i powinno reprezentować się jako graf?
Wybrane przypadki użycia i modelowanie wybranego zagadnienia
Najważniejsze pojęcia modelu grafowego Neo4j:
Węzeł
Relacja
Właściwość
Etykieta
Język zapytań Cypher i operacje na grafach
Tworzenie i zarządzanie schematem za pomocą języka Cypher
Operacje CRUD na danych
Zapytania Cypher oraz ich odpowiedniki w SQL
Algorytmy grafowe wykorzystywane w Neo4j
Interfejs REST
Podstawowe zagadnienia administracyjne
Tworzenie i odtwarzanie kopii zapasowych
Zarządzanie bazą z poziomu przeglądarki
Import i eksport danych w uniwersalnych formatach
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BigData_ |
A practical introduction to Data Analysis and Big Data |
28 godz. |
Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools that enable Big Data storage, Distributed Processing, and Scalability.
Audience
Developers / programmers
IT consultants
Format of the course
Part lecture, part discussion, heavy hands-on practice and implementation, occasional quizing to measure progress.
Introduction to Data Analysis and Big Data
What makes Big Data "big"?
Velocity, Volume, Variety, Veracity (VVVV)
Limits to traditional Data Processing
Distributed Processing
Statistical Analysis
Types of Machine Learning Analysis
Data Visualization
Distributed Processing
MapReduce
Languages used for Data Analysis
R language (crash course)
Python (crash course)
Approaches to Data Analysis
Statistical Analysis
Time Series analysis
Forecasting with Correlation and Regression models
Inferential Statistics (estimating)
Descriptive Statistics in Big Data sets (e.g. calculating mean)
Machine Learning
Supervised vs unsupervised learning
Classification and clustering
Estimating cost of specific methods
Filter
Natural Language Processing
Processing text
Understaing meaning of the text
Automatic text generation
Sentiment/Topic Analysis
Computer Vision
Big Data infrastructure
Data Storage
SQL (relational database)
MySQL
Postgres
Oracle
NoSQL
Cassandra
MongoDB
Neo4js
Understanding the nuances: hierarchical, object-oriented, document-oriented, graph-oriented, etc.
Distributed File Systems
HDFS
Search Engines
ElasticSearch
Distributed Processing
Spark
Machine Learning libraries: MLlib
Spark SQL
Scalability
Public cloud
AWS, Google, Aliyun, etc.
Private cloud
OpenStack, Cloud Foundry, etc.
Auto-scalability
Choosing right solution for the problem
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mlrobot1 |
Machine Learning for Robotics |
21 godz. |
This course introduce machine learning methods in robotics applications.
It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.
After short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
Regression
Probabilistic Graphical Models
Boosting
Kernel Methods
Gaussian Processes
Evaluation and Model Selection
Sampling Methods
Clustering
CRFs
Random Forests
IVMs
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octnp |
Octave nie tylko dla programistów |
21 godz. |
Szkolenie dedykowane osobom, które chciałyby zapoznać się z obsługą programu alternatywnego do komercyjnego pakietu MATLAB. Kurs trzydniowy dostarcza kompleksowo informacje dotyczące poruszania się po środowisku i wykonywaniu pakietu OCTAVE w zastosowaniu do analizy danych i obliczeń inżynierskich. Adresatami szkolenia są osoby początkujące ale także ci, którzy znają program i chcieliby usystematyzować swoją wiedzę i podnieść umiejętności. Nie jest wymagana znajomość innych języków programowania ale w znacznym stopniu ułatwi to uczestnikom przyswajanie wiedzy. Na kursie pokazane zostaną możliwości wykorzystania program na wielu przykładach praktycznych.
Wstęp
Podstawowe obliczenia
Rozpoczęcie pracy z Octave, Octave jako kalkulator, funkcje wbudowane
Środowisko Octave
Zmienne, liczny I formatowanie, reprezentacje liczb I ich dokładność, zapisywanie i wczytywanie danych
Tablice i wektory
Wyodrębnianie liczb z wektorów, arytmetyka wektorowa
Wykresy
Prezentacja danych na wykresie, przygotowanie wielu wykresów oraz wielu okien z wykresami, zapisywanie i drukowanie wykresów
Programowanie w Octave cz. I: Skrypty
Tworzenie i edycja skryptu, uruchamianie i debugowanie skryptów,
Instrukcje sterujące wykonywanie programu
If else, switch, for, while
Programowanie w Octave cz. II: Funkcje
Macierze i wektory
macierze, transpozycja, funkcje tworzące macierze, budowanie złożonych macierzy, macierz jako tablica, wyodrębnianie bitów z macierzy, podstawowe funkcje macierzowe
Równanie liniowe i nieliniowe
Więcej wykresów
Budowanie wielu wykresów na jednym rysunku, wykresy 3D, zmiana perspektywy, wykresy powierzchniowe, rysunki i filmy,
Matematyka macierzowa
Wartości własne, dekompozycja
Liczby zespolone
Wykresy liczb zespolonych
Statystyka i przetwarzanie danych
Budowanie graficznego interfejsu użytkownika |
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matlabml1 |
Introduction to Machine Learning with MATLAB |
21 godz. |
MATLAB Basics
MATLAB More Advanced Features
BP Neural Network
RBF, GRNN and PNN Neural Networks
SOM Neural Networks
Support Vector Machine, SVM
Extreme Learning Machine, ELM
Decision Trees and Random Forests
Genetic Algorithm, GA
Particle Swarm Optimization, PSO
Ant Colony Algorithm, ACA
Simulated Annealing, SA
Dimenationality Reduction and Feature Selection
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dladv |
Advanced Deep Learning |
28 godz. |
Machine Learning Limitations
Machine Learning, Non-linear mappings
Neural Networks
Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent
Back Propagation
Deep Sparse Coding
Sparse Autoencoders (SAE)
Convolutional Neural Networks (CNNs)
Successes: Descriptor Matching
Stereo-based Obstacle
Avoidance for Robotics
Pooling and invariance
Visualization/Deconvolutional Networks
Recurrent Neural Networks (RNNs) and their optimizaiton
Applications to NLP
RNNs continued,
Hessian-Free Optimization
Language analysis: word/sentence vectors, parsing, sentiment analysis, etc.
Probabilistic Graphical Models
Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines
Hopfield Networks, (Restricted) Bolzmann Machines
Deep Belief Nets, Stacked RBMs
Applications to NLP , Pose and Activity Recognition in Videos
Recent Advances
Large-Scale Learning
Neural Turing Machines
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mlfsas |
Machine Learning Fundamentals with Scala and Apache Spark |
14 godz. |
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Machine Learning with Python
Choice of libraries
Add-on tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means
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predio |
Machine Learning with PredictionIO |
21 godz. |
PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
Getting Started
Quick Intro
Installation Guide
Downloading Template
Deploying an Engine
Customizing an Engine
App Integration Overview
Developing PredictionIO
System Architecture
Event Server Overview
Collecting Data
Learning DASE
Implementing DASE
Evaluation Overview
Intellij IDEA Guide
Scala API
Machine Learning Education and Usage Examples
Comics Recommendation
Text Classification
Community Contributed Demo
Dimensionality Reducation and usage
PredictionIO SDKs (Select One)
Java
PHP
Python
Ruby
Community Contributed
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systemml |
Apache SystemML for Machine Learning |
14 godz. |
Apache SystemML is a distributed and declarative machine learning platform.
SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.
Audience
This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.
Running SystemML
Standalone
Spark MLContext
Spark Batch
Hadoop Batch
JMLC
Tools
Debugger
IDE
Troubleshooting
Languages and ML Algorithms
DML
PyDML
Algorithms
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aiintrozero |
From Zero to AI |
35 godz. |
This course is created for people who have no previous experience in probability and statistics.
Probability (3.5h)
Definition of probability
Binomial distribution
Everyday usage exercises
Statistics (10.5h)
Descriptive Statistics
Inferential Statistics
Regression
Logistic Regression
Exercises
Intro to programming (3.5h)
Procedural Programming
Functional Programming
OOP Programming
Exercises (writing logic for a game of choice, e.g. noughts and crosses)
Machine Learning (10.5h)
Classification
Clustering
Neural Networks
Exercises (write AI for a computer game of choice)
Rules Engines and Expert Systems (7 hours)
Intro to Rule Engines
Write AI for the same game and combing solutions into hybrid approach
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aiauto |
Artificial Intelligence in Automotive |
14 godz. |
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
Current state of the technology
What is used
What may be potentially used
Rules based AI
Simplifying decision
Machine Learning
Classification
Clustering
Neural Networks
Types of Neural Networks
Presentation of working examples and discussion
Deep Learning
Basic vocabulary
When to use Deep Learning, when not to
Estimating computational resources and cost
Very short theoretical background to Deep Neural Networks
Deep Learning in practice (mainly using TensorFlow)
Preparing Data
Choosing loss function
Choosing appropriate type on neural network
Accuracy vs speed and resources
Training neural network
Measuring efficiency and error
Sample usage
Anomaly detection
Image recognition
ADAS
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