Videos

How to Use the AutoUpdater in InstallBuilder

How to Use the AutoUpdater in InstallBuilder

9 years ago
In version 6.0 of InstallBuilder, we started shipping a new tool called AutoUpdater . This tool allows you to enable users to check for, download and apply updates for your software on their machines, getting the necessary files through HTTP or FTP.
How to Create Components in InstallBuilder

How to Create Components in InstallBuilder

9 years ago
This video shows how to create components for your cross platform installer using BitRock InstallBuilder. This video was created in conjunction with this blog post: blog.bitrock.com/2009/07/how-to-add-components-and-make-them-optional.html
How to Integrate BitRock InstallBuilder with Eclipse

How to Integrate BitRock InstallBuilder with Eclipse

9 years ago
This video shows how to integrate cross platform installer development and generation into Eclipse. It was created in conjunction with this blog post: bitrock.blogspot.com/2009/06/how-to-integrate-bitrock-installbuilder-with-eclipse.html
BitRock  xTuple installer

BitRock xTuple installer

9 years ago
Shows how easy it is to install xTuple, a complete open source ERP system, thanks to a BitRock Custom Stack.
GitHub:Firewall Install, Powered by BitRock

GitHub:Firewall Install, Powered by BitRock

9 years ago
Logical Awesome, developer of the widely used GitHub site for social coding,launched GitHub:FI. Powered by BitRock, GitHub:FI can be installed and running in minutes. GitHub:FI has been packaged by BitRock to provide a fast, easy way to get the Ruby on Rails application and all of its dependencies up and running. BitRock maintains a website […]
Lecture 14 | Machine Learning (Stanford)

Lecture 14 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]
Lecture 18 | Machine Learning (Stanford)

Lecture 18 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics […]
Lecture 4 | Machine Learning (Stanford)

Lecture 4 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton’s method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
Lecture 5 | Machine Learning (Stanford)

Lecture 5 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement […]
Lecture 13 | Machine Learning (Stanford)

Lecture 13 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include […]
Lecture 2 | Machine Learning (Stanford)

Lecture 2 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
Lecture 20 | Machine Learning (Stanford)

Lecture 20 | Machine Learning (Stanford)

10 years ago
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive […]