One of the trickiest obstacles in machine learning is overfitting. A Machine Learning model must be able to generalize, that is, it must be able to take new observations and predict with some accuracy the associated target variable.
Voice assistants Alexa, Google Home and Siri are all based on automatic language processing technologies. Objective: to have the ability to understand, process and generate voice messages.
AI is invading all industries. It offers interesting solutions and creates enormous business opportunities. This is what makes the turnover generated by AI today several tens of billions of dollars!
The random forest is a machine learning algorithm designed to obtain a reliable prediction thanks to a system of random subspaces.
Inference is a logical operation based on induction. Inference in machine learning and deep learning aims to make efficient predictions from a trained learning model.
Abbreviation of machine learning operations, the MLOps aims to design learning models adapted to their deployment in production, then to maintain them throughout their life cycle.
TensorFlow is an open source deep learning platform created by Google. Equipped with a Python API, it offers a multitude of tools to train and optimize artificial neural networks.
Open source is H2O.ai’s main differentiator. Available under the Apache 2.0 license, its offer revolves around a machine learning platform equipped with an auto ML engine.
C3.ai Suite is an end-to-end machine learning platform. It comes with the C3.ai Ex Machina no-code development environment.
Written in Python, Keras is an artificial neural network rapid prototyping interface. It is based on the deep learning libraries Tensorflow, Microsoft Cognitive Toolkit, PlaidML and Theano.
