With the success of voice assistants such as Alexa/Siri/Google, deep learning has brought sound and speech processing into the mainstream. Topics include speech recognition, denoising, classification, audio tagging and audio separation (speech & music)…
Image processing is one of the fields that has benefited most dramatically from advances in Deep Learning. Topics range from classification and segmentation to image transformation, including the generation of text-oriented analyses.
Deep Learning has recently revolutionized natural language processing: translation, feature identification, dialogue systems, interpretation, etc.
Reinforcement learning aims to teach an agent how to optimize its actions so as to maximize its gains. This classic field has recently been revolutionized by Deep Learning (Q function, policy, etc.) and now makes it possible to solve tasks previously considered beyond the machine’s reach: process optimization, go/video games, robotics, etc.
After revolutionizing many scientific fields, artificial intelligence is now taking root in industry. In practice, it is supported by Python frameworks such as PyTorch, now a leader in deep learning.
Data mining has become an integral part of R&D activities. However, understanding the issues and the state of the art in the field requires a solid mathematical background.
Artificial intelligence has revolutionized many fields of science, and is now beginning to revolutionize a large number of economic sectors (industry, medicine, communications, etc.). Nevertheless, its presentation in the mainstream media is often nothing more than fantasy. The aim of this training course is to present these approaches and their contribution to solving problems considered to be “intelligent”.
Data mining has become an integral part of the business of R&D. However, understanding the issues and the state of the art in the field requires a solid mathematical background.