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...
Utilizing advanced machine learning algorithms, the project analyzes historical transaction data and builds a predictive model. It examines numerous transaction features like amount, location, time, and cardholder information to determine patterns indicative of fraudulent activity. In real-time, incoming tran...
1. Machine learning Although machine learning may sound new, the term was actually coined by AI pioneer Arthur Samuel in 1959. Samueldefined itas a computer's ability to learn without being explicitly programmed. To do that, mathematical models, or algorithms, are fed large data sets and traine...
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet.. - quantumiracle/Popular-RL-Algorithms
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, ...
3517 views, 2:10:19,Learning with Gaussian Processes, Carl Edward Rasmussen,7 comments 222 views, 29:03,Generating Possible Interpretations for Statistics from Linked Open Data,Heiko Paulheim 4089 views, 2:32:26,Graph Matching Algorithms, Terry Caelli,6 comments ...
Regression analysishelps in identifying the relationship between the independent and dependent variables. We also have various regression algorithms to predict the numerical outcome. For example, house price prediction. In this, we need to have the house features, such as ...
A deep learning application is more than just the network. You also need to take the pre- and postprocessing logic of the application into consideration. Some of the tools and techniques we discussed have been used for quantizing such algorithms for a couple of decades ...