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« Meetup Data Science » Mercredi 4 mars 2020 Comment l’IA démultiplie les fonctions cognitives ? A variant of machine learning engineer is called Deep Learning engineer. This role requires deep learning knowledge in addition to the skills profile Jean-Marie PRIGENT (Modeling, Deployment, Data Engineering). It focuses on applications, usually powered by deep ML Engineer learning, such as speech recognition, natural language processing, and computer vision. Hence, it Altran Brest requires skills specific to deep learning projects such as understanding and using various neural network architectures such as fully connected networks, CNNs, and RNNs. P as Passionately curious... Big Data and DL but not only… Drones, FPV, CV, Maker, 3D Printer, Electronics, DonkeyCar, Edge, ... linkedin.com/in/jmprigent Machine Learning Ecosystem Machine Learning Languages: Data Processing: - Python / R / (C++) - BIG Data framework (Cloudera/HDP/Oozie/Pig/Spark/ General Machine Learning Frameworks Scala ) - Numpy - Apache Airflow, NIFI - Scikit-Learn - NLTK, Spacy Hardware Training: - CPU, GPU, TPU, Cloud Data Analysis and Visualisation tools - Distrubuted (Spark, Kubeflow) - Pandas - Federated Learning (WO - Matplotlib centralized server) - Jupyter Notebook Inference ML frameworks for neural networks - Desktop, server … modelling - Mobile - Tensorflow / Tensorboard - Edge device (VPU: Intel NCS2, - Keras GPU: Jetson Nano & TPU: Coral - Pytorch dev board, Coral stick) - (Caffe2, mxnet) What is Deep Learning ? Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance Deep Learning : “a technique for implementing Machine Learning” Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Le Deep Learning dans l’Univers de l’IA... Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Le Deep Learning dans l’Univers de l’IA... Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Some dates in the field of IA 1980 – Kunihiko Fukushima built the ‘neocognitron’, the precursor of modern Convolutional Neural Networks. 2001 – Two researchers at MIT introduced the first face detection framework (Viola-Jones) that works in real-time. 2009 – Google started testing robot cars on roads. 2010 – Google released Goggles, an image recognition app for searches based on pictures taken by mobile devices. 2010 – To help tag photos, Facebook began using facial recognition. 2011 – Facial recognition was used to help confirm the identity of Osama bin Laden after he is killed in a US raid. 2012 – Google Brain’s neural network recognized pictures of cats using a deep learning algorithm. 2015 – Google launched open-source Machine learning-system TensorFlow. 2016 – Google DeepMind’s AlphaGo algorithm beat the world Go champion. 2017 – Apple released the iPhone X in 2017, advertising face recognition as one of its primary new features. 2018 – Alibaba’s AI model scored better than humans in a Stanford University reading and comprehension test. 2018 – Amazon sold its real time face recognition system Rekognition to police departments. 2019 – The Indian government announced a facial recognition plan allowing police officers to search images through mobile app. 2019 – The US added four of China’s leading AI start-ups to a trade blacklist. 2019 – The UK High Court ruled that the use of automatic facial recognition technology to search for people in crowds is lawful. 2025 – By this time, regulation in FR will significantly diverge between China and US/Europe. 2030 – At least 60% of countries globally will be using AI surveillance technology (it is currently 43% according to CEIP). This is an edited extract from the Computer Vision – Thematic Research report produced by GlobalData Thematic Research. https://www.verdict.co.uk/computer-vision-timeline/ Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Apprentissage supervisé “L’objectif de l’apprentissage supervisé est d’apprendre une fonction qui, à partir d’un échantillon de données et des résultats souhaités, se rapproche le mieux de la relation entre entrée et sortie observable dans les données.” -> Y = f (X) L’apprentissage supervisé est généralement effectué dans le contexte de la classification et de la régression. Classification: Un problème de classification survient lorsque la variable de sortie est une catégorie, telle que «rouge», «bleu» ou «maladie» et «pas de maladie». Régression: Un problème de régression se pose lorsque la variable de sortie est une valeur réelle, telle que «dollars» ou «poids». source: https://le-datascientist.fr/apprentissage-supervise-vs-non-supervise Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Apprentissage non supervisé “L’apprentissage non supervisé (Unsupervised Learning) consiste à ne disposer que de données d’entrée (X) et pas de variables de sortie correspondantes. L’objectif est de modéliser la structure ou la distribution sous-jacente dans les données afin d’en apprendre davantage sur les données. On l’appelle apprentissage non supervisé car, contrairement à l’apprentissage supervisé, il n’y a pas de réponse correcte ni d’enseignant. Les algorithmes sont laissés à leurs propres mécanismes pour découvrir et présenter la structure intéressante des données.” Regroupement ou clustering: l’objectif est de séparer les groupes ayant des traits similaires et de les assigner en grappes. Association: consiste à découvrir des relations intéressantes entre des variables dans de grandes bases de données. Par exemple, les personnes qui achètent une nouvelle maison ont aussi tendance à acheter de nouveaux meubles source: https://le-datascientist.fr/apprentissage-supervise-vs-non-supervise Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Apprentissage semi supervisé “Les problèmes pour lesquels vous avez une grande quantité de données d’entrée (X) et que seules certaines données sont étiquetées (Y) sont appelés problèmes d’apprentissage semi-supervisés. Par conséquent, ces problèmes se situent entre l’apprentissage supervisé et l’apprentissage non supervisé” Le Deep Learning rentre dans la catégorie “supervisé” pour la majorité des cas et plus récemment semi supervisé avec les GAN. Le (Deep) Reinforcement Learning rentre dans la categorie non supervisé source: https://le-datascientist.fr/apprentissage-supervise-vs-non-supervise Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent A Visual and Interactive Guide to the Basics of Neural Networks...in a nutshell source: jay Alammar Blog Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent A Visual and Interactive Guide to the Basics of Neural Networks...in a nutshell Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent A Visual and Interactive Guide to the Basics of Neural Networks...in a nutshell Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent A Visual and Interactive Guide to the Basics of Neural Networks...in a nutshell Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent What convolution neural network see... Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Some vocab in Computer Vision tasks 4 differents tasks in CV: - image classification - object detection - semantic segmentation - instance segmentation Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Imaging Applications Deep Learning has applications in all sectors of activity. It is a major issue for the industrial and scientific sectors and the safety of goods and people. Among these uses are in particular : - Image recognition (classification, localization and segmentation), - Description of scenes, - Facial recognition (security), - Optical Character Recognition (OCR), - Content-Based Images Retrieval (CBIR) - Medical Imaging (biology, histology, radiology), - Synthetic image generation (GAN), - Emotion detection, - Detection of age and gender, … Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent NLP-NLU Applications NLP iis becoming more democratic, previously reserved for researchers. The advent of personal assistants (Siri, Alexa, Google Home) and its high level of adoption proves the maturity of this technology. Studies prove that using a text transcriber is 3 times faster than writing the text. Among the cases of use are the following: - Chatbots - Spam detection / Spam filter avoidance - Sentiment analysis (consumer opinions, customer opinions) - E-reputation - Automated translation - Subtitling of video sequences (Speech-to-Text) - Voice User Interface (VUI) (Siri, Alexa, Ok Google) … Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Get datas … and Training Neural Network tensorboard UI Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent Training sample on Fashion-MNIST under Colab Fashion Mnist Meetup Data Sciences - Brest is IA - 2020-03-04 - Jean-Marie Prigent 3/ Inference
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