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

OpenNN 
OpenNN: Implementing neural networks 
14 hours 
OpenNN is an opensource 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 handson exercises.
Introduction to OpenNN, Machine Learning and Deep Learning
Downloading OpenNN
Working with Neural Designer
Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics
OpenNN architecture
CPU parallelization
OpenNN classes
Data set, neural network, loss index, training strategy, model selection, testing analysis
Vector and matrix templates
Building a neural network application
Choosing a suitable neural network
Formulating the variational problem (loss index)
Solving the reduced function optimization problem (training strategy)
Working with datasets
The data matrix (columns as variables and rows as instances)
Learning tasks
Function regression
Pattern recognition
Compiling with QT Creator
Integrating, testing and debugging your application
The future of neural networks and OpenNN 
mlintro 
Introduction to Machine Learning 
7 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

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
Biasvariance trade off [underfitting/overfitting] for regression models
Resampling Methods
CrossValidation
The Validation Set Approach
LeaveOneOut CrossValidation
kFold CrossValidation
BiasVariance TradeOff for kFold
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
KNearest Neighbors
Classification with Nonlinear Decision Boundaries
Support Vector Machines
Optimization Objective
The Maximal Margin Classifier
Kernels
OneVersusOne Classification
OneVersusAll Classification
Comparison of Classification Methods
Introduction to Deep Learning
ANN Structure
Biological neurons and artificial neurons
Nonlinear Hypothesis
Model Representation
Examples & Intuitions
Transfer Function/ Activation Functions
Typical classes of network architectures
Feed forward ANN.
Structures of Multilayer 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
SelfTaught 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
Nonlinear Transformations
Dummy variable regression
CrossValidation and the Bootstrap
Subset selection methods
Penalization [Ridge, Lasso, Elastic Net]
Classification
Logistic Regression, LDA, QDA, and KNN,
Resampling & Regularization
Support Vector Machine
Resampling & Regularization
Artificial Neural Network
Deep Learning
Note:
For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.
Analysis of different data sets will be performed using R

annmldt 
Artificial Neural Networks, Machine Learning, Deep Thinking 
21 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.
Revisiting vector and matrix algebra.
Statespace concepts.
Concepts of optimization.
Errorcorrection learning.
Memorybased 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 Multilayer 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.
Neurofuzzy 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
Overfitting
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
SelfTaught Learning
Deep Networks
Linear Decoders
Convolution and Pooling
Sparse Coding
Independent Component Analysis
Canonical Correlation Analysis
Demos and Applications

dmmlr 
Data Mining & Machine Learning with R 
14 hours 
Introduction to Data mining and Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Dicriminant analysis
Logistic regression
KNearest neighbors
Support Vector Machines
Neural networks
Decision trees
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans
Advanced topics
Ensemble models
Mixed models
Boosting
Examples
Multidimensional reduction
Factor Analysis
Principal Component Analysis
Examples

wdneo4j 
Wprowadzenie do Neo4j  grafowej bazy danych 
7 hours 

BigData_ 
A practical introduction to Data Analysis and Big Data 
28 hours 
Participants who complete this training will gain a practical, realworld understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through handson 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 handson 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, objectoriented, documentoriented, graphoriented, 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.
Autoscalability
Choosing right solution for the problem

mlrobot1 
Machine Learning for Robotics 
21 hours 
This course introduce machine learning methods in robotics applications.
It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.
After short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
Regression
Probabilistic Graphical Models
Boosting
Kernel Methods
Gaussian Processes
Evaluation and Model Selection
Sampling Methods
Clustering
CRFs
Random Forests
IVMs

octnp 
Octave not only for programmers 
21 hours 
Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The threeday training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
Introduction
Simple calculations
Starting Octave, Octave as a calculator, builtin functions
The Octave environment
Named variables, numbers and formatting, number representation and accuracy, loading and saving data
Arrays and vectors
Extracting elements from a vector, vector maths
Plotting graphs
Improving the presentation, multiple graphs and figures, saving and printing figures
Octave programming I: Script files
Creating and editing a script, running and debugging scripts,
Control statements
If else, switch, for, while
Octave programming II: Functions
Matrices and vectors
Matrix, the transpose operator, matrix creation functions, building composite matrices, matrices as tables, extracting bits of matrices, basic matrix functions
Linear and Nonlinear Equations
More graphs
Putting several graphs in one window, 3D plots, changing the viewpoint, plotting surfaces, images and movies,
Eigenvectors and the Singular Value Decomposition
Complex numbers
Plotting complex numbers,
Statistics and data processing
GUI Developmen 
matlabml1 
Introduction to Machine Learning with MATLAB 
21 hours 
MATLAB Basics
MATLAB More Advanced Features
BP Neural Network
RBF, GRNN and PNN Neural Networks
SOM Neural Networks
Support Vector Machine, SVM
Extreme Learning Machine, ELM
Decision Trees and Random Forests
Genetic Algorithm, GA
Particle Swarm Optimization, PSO
Ant Colony Algorithm, ACA
Simulated Annealing, SA
Dimenationality Reduction and Feature Selection

dladv 
Advanced Deep Learning 
28 hours 
Machine Learning Limitations
Machine Learning, Nonlinear mappings
Neural Networks
NonLinear Optimization, Stochastic/MiniBatch Gradient Decent
Back Propagation
Deep Sparse Coding
Sparse Autoencoders (SAE)
Convolutional Neural Networks (CNNs)
Successes: Descriptor Matching
Stereobased Obstacle
Avoidance for Robotics
Pooling and invariance
Visualization/Deconvolutional Networks
Recurrent Neural Networks (RNNs) and their optimizaiton
Applications to NLP
RNNs continued,
HessianFree 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
LargeScale Learning
Neural Turing Machines

mlfsas 
Machine Learning Fundamentals with Scala and Apache Spark 
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 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
BiasVariance tradeoff
Machine Learning with Python
Choice of libraries
Addon tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

predio 
Machine Learning with PredictionIO 
21 hours 
PredictionIO is an open source Machine Learning Server built on top of stateoftheart open source stack.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
Getting Started
Quick Intro
Installation Guide
Downloading Template
Deploying an Engine
Customizing an Engine
App Integration Overview
Developing PredictionIO
System Architecture
Event Server Overview
Collecting Data
Learning DASE
Implementing DASE
Evaluation Overview
Intellij IDEA Guide
Scala API
Machine Learning Education and Usage Examples
Comics Recommendation
Text Classification
Community Contributed Demo
Dimensionality Reducation and usage
PredictionIO SDKs (Select One)
Java
PHP
Python
Ruby
Community Contributed

systemml 
Apache SystemML for Machine Learning 
14 hours 
Apache SystemML is a distributed and declarative machine learning platform.
SystemML provides declarative largescale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, inmemory computations, to distributed computations on Apache Hadoop and Apache Spark.
Audience
This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.
Running SystemML
Standalone
Spark MLContext
Spark Batch
Hadoop Batch
JMLC
Tools
Debugger
IDE
Troubleshooting
Languages and ML Algorithms
DML
PyDML
Algorithms

aiintrozero 
From Zero to AI 
35 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 combing 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

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

datamodeling 
Pattern Recognition 
35 hours 
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 handson exercises, continuous feedback, and testing of knowledge and skills acquired.
Audience
Data analysts
PhD students, researchers and practitioners
Introduction
Probability theory, model selection, decision and information theory
Probability distributions
Linear models for regression and classification
Neural networks
Kernel methods
Sparse kernel machines
Graphical models
Mixture models and EM
Approximate inference
Sampling methods
Continuous latent variables
Sequential data
Combining models

MLFWR1 
Machine Learning Fundamentals with R 
14 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
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

patternmatching 
Pattern Matching 
14 hours 
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Audience
Engineers and developers seeking to develop machine vision applications
Manufacturing engineers, technicians and managers
Format of the course
This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Introduction
Computer Vision
Machine Vision
Pattern Matching vs Pattern Recognition
Alignment
Features of the target object
Points of reference on the object
Determining position
Determining orientation
Gauging
Setting tolerance levels
Measuring lengths, diameters, angles, and other dimensions
Rejecting a component
Inspection
Detecting flaws
Adjusting the system
Closing remarks

mlfunpython 
Machine Learning Fundamentals with Python 
14 hours 
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
BiasVariance tradeoff
Machine Learning with Python
Choice of libraries
Addon tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

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 realworld applications. We step through numerous handson 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
Inclass 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
CIFAR10, CIFAR100
Imagenet
Machine Learning in Torch
Deep Learning
Manual feature extraction vs convolutional networks
Supervised and Unsupervised Learning
Building a neural network with Torch
Ndimensional 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 deeplearning modules and containers
Creating your own package
Testing and debugging
Releasing your application
The future of AI and Torch 