puzzle branch paper paper message user-group

Warning: Invalid argument supplied for foreach() in /users/radixlibrary/www/views/layout/header1.php on line 404
Login
×
  • Radix
  • Learning Paths
  • Resources
  • Login
  • ×
























    Reviews
    Curator
    Path tree
    Overview

    Reviews

    Curator

    Path tree

    Machine Learning 2

    From module to module the paths lets you dive deeper into the technical details. At end you should understand and be able to explain the functionings of machine learning.

    Curated by Camillo

    Beginner Lvl 1
    8h
    5.3k
    4.6(21,556)

    Calculus Have a basic knowledge of

    1%
    Filter Paths
    Continue
    Top 3
    1. Module
    40

    Machine Learning

    Stanford University

    Mathematical discussion of Linear Regression, Logistic Regression, Neural Networks, Support Vector Machines

    This course provides you with a profound insight on the technicalities of important machine learning algorithm!

    MARK AS DONE
    Author: Andrew Ng   
    Time required: 8h
    Cost: 0€
    40

    Gradient Descent

    Iterative algorithm to find a min / max of a function, optimization strategy, iteratively moving in the direction of steepest descent

    How does a machine learn? This algorithm is at the hearth of learning!

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    2. Module
    40

    Feature extraction

    Facilitates subsequent learning and generalization steps, and in some cases leading to better human interpretations

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    Overfitting

    Fitting data too closely or exactly to a particular dataset, and may therefore fail to fit unseen data

    This is one of most common problems in applied machine learning!

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    Bias-variance tradeoff

    The conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    Mathematics for Machine Learning

    Imperial College London

    Linear algebra, multivariate calculus, principal component analysis

    If you are interested at the mathematical details, take this course!

    MARK AS DONE
    Author: David Dye   
    Time required: 8h
    Cost: 0€
    40

    Curse of dimensionality

    Phenomena that arise when analyzing and organizing data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    3. Module
    40

    Loss Function

    A function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    Goodhart's law

    "When a measure becomes a target, it ceases to be a good measure."

    MARK AS DONE
    Author: Charles Goodhart   
    Time required: 8h
    Cost: 0€
    4. Module
    40

    colah's blog

    Research insights

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    40

    An Introduction to Statistical Learning

    Modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering

    This best selling book dives deeper into relevant concepts of machine learning!

    MARK AS DONE
    Author: Gareth James Daniela Witten   Trevor Hastie
    Time required: 8h
    Cost: 0€
    40

    Machine Learning Explained

    Dissecting papers, explanation of the intuition and theory, aggregating resources, computer vision, deep learning, natural language processing

    Articles on advanced ML concepts

    MARK AS DONE
    Author: Keita Kurita   
    Time required: 8h
    Cost: 0€
    40

    Papers With Code

    Curated implementations of machine learning models in pytorch, tensor flow, caffe2, computer vision, natural language processing

    This platform entails papers that present solutions to various machine learning tasks from a theoretical and practical perspective.

    MARK AS DONE
    Author:    
    Time required: 8h
    Cost: 0€
    5. Extras