Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources...
Introduction to Machine Learning: Supervised Learning University of Colorado Boulder via Coursera In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performa...
In most cases the explanations are based on this great machine learning course by Andrew Ng. The purpose of this repository is not to implement machine learning algorithms by using 3rd party library one-liners but rather to practice implementing these algorithms from scratch and get better ...
In this type of hybrid system, each algorithm is used in an off-the-shelf fashion, which means that each algorithm is used as-is, without any modification. In fact, only their output is used to provide a combined prediction. This construction allows an easy combination of the algorithms and...
Large language models: The foundations of generative AI Feb 17, 202520 mins reviews First look: Solver can code that for you Feb 3, 202515 mins feature Surveying the LLM application framework landscape Dec 9, 202410 mins feature GitHub Copilot: Everything you need to know ...
In most cases the explanations are based on this great machine learning course by Andrew Ng. The purpose of this repository is not to implement machine learning algorithms by using 3rd party library one-liners but rather to practice implementing these algorithms from scratch and get better ...
it’s now possible to come up with very strategic and meaningful clusters for effective targeting. And identifying the target segments requires a robust segmentation exercise. In this blog, we will be discussing the most popular algorithms for unsupervised clustering algorithms and...
Scikit-learn is the library that provides an immense range of algorithms for Supervised Learning and Unsupervised Learning through the interface for the Python programming language. This library is distributed under the “Simplified BSD License” and has distributions for many different Linux versions, ...
Algorithms include: Q-learning; SARSA; Monte-Carlo Regression; Actor-Critic (AC/A2C); Soft Actor-Critic (SAC); Deep Deterministic Policy Gradient (DDPG); Twin Delayed DDPG (TD3); Proximal Policy Optimization (PPO); QT-Opt (including Cross-entropy (CE) Method); PointNet; Transporter; Recurren...
The use of algorithms and model training in machine learning was introduced in the 1950s. Applications at the time were minor. However, fundamental concepts that established the logic behind ML were proposed by a number of pioneering mathematicians and scientists, e.g., Alan Turing; Allen Newell...