*Dimensionality Reduction* - Day 09 lecture of the Foundations of Machine Learning in Python course.

*High-Performance Computing and Analytics Lab, University of Bonn*

Difficulty level: Advanced

Duration: 51:02

Speaker: : Elena Trunz

*Introduction to Neural Networks *- Day 10 lecture of the Foundations of Machine Learning in Python course.

*High-Performance Computing and Analytics Lab, University of Bonn*

Difficulty level: Advanced

Duration: 54:12

Speaker: : Moritz Wolter

Introduction to Convolutional Neural Networks* *- Day 11 lecture of the Foundations of Machine Learning in Python course.

*High-Performance Computing and Analytics Lab, University of Bonn*

Difficulty level: Advanced

Duration: 42:07

Speaker: : Moritz Wolter

*Initialization, Optimization, and Regularization** *- Day 12 lecture of the Foundations of Machine Learning in Python course.

*High-Performance Computing and Analytics Lab, University of Bonn*

Difficulty level: Advanced

Duration: 42:07

Speaker: : Moritz Wolter

U-Nets for medical Image-Segmentation* *- Day 13 lecture of the Foundations of Machine Learning in Python course.

*High-Performance Computing and Analytics Lab, University of Bonn*

Difficulty level: Advanced

Duration: 16:45

Speaker: : Moritz Wolter

Sequence Processing - Day 15 lecture of the Foundations of Machine Learning in Python course.

*High-Performance Computing and Analytics Lab, University of Bonn*

Difficulty level: Advanced

Duration: 47:45

Speaker: : Moritz Wolter

Course:

This lecture gives an introduction to the INCF Short Course: Introduction to Neuroinformatics.

Difficulty level: Beginner

Duration: 34:27

Speaker: : Marja-Leena Linne

Course:

Presented by the OHBM OpenScienceSIG, this lesson covers how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers.

Difficulty level: Beginner

Duration: 01:21:59

Speaker: : Tom Shaw & Steffen Bollmann

Course:

This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.

Difficulty level: Beginner

Duration: 2:53

Speaker: : Barton Poulson

Course:

This lesson serves a primer on elementary algebra.

Difficulty level: Beginner

Duration: 3:03

Speaker: : Barton Poulson

Course:

This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science.

Difficulty level: Beginner

Duration: 5:38

Speaker: : Barton Poulson

Course:

In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.

Difficulty level: Beginner

Duration: 5:24

Speaker: : Barton Poulson

Course:

This talk gives a primer on calculus, emphasizing its role in data science.

Difficulty level: Beginner

Duration: 4:17

Speaker: : Barton Poulson

Course:

This lesson clarifies how calculus relates to optimization in a data science context.

Difficulty level: Beginner

Duration: 8:43

Speaker: : Barton Poulson

Course:

This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.

Difficulty level: Beginner

Duration: 5:19

Speaker: : Barton Poulson

Course:

This lesson serves as a primer on the fundamental concepts underlying probability.

Difficulty level: Beginner

Duration: 7:33

Speaker: : Barton Poulson

Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

Difficulty level: Beginner

Duration: 1:00:07

Speaker: : Shawn Grooms

This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

Difficulty level: Beginner

Duration: 1:21:30

Speaker: :

Course:

The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.

Difficulty level: Beginner

Duration: 1:29:28

Speaker: : Brett Jones, Victor Tang

Course:

This lesson delves into the opportunities and challenges of telepsychiatry. While novel digital approaches to clinical research and care have the potential to improve and accelerate patient outcomes, researchers and care providers must consider new population factors, such as digital disparity.

Difficulty level: Beginner

Duration: 1:20:28

Speaker: : Abhi Pratap

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