Generative Ai

Where to start with Data Science There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful. Best resources to study Data Science /Machine Learning 1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning). 2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks). 3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models). 4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html). 5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html) 6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html). 7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html). 8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/). 9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/). #books #wheretostart #mooc

How to Make an Asteroids Game Bot (LIVE) https://www.youtube.com/watch?v=h2qVYpK6TPE&feature=em-lss

Face-Resources Following is a growing list of some of the materials I found on the web for research on face recognition algorithm. https://github.com/betars/Face-Resources

Welcome to fast.ai's 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing... it's totally free! http://course.fast.ai/

Запись трансляции, от Siraj Raval Крестики-нолики на нейронных сетках https://www.youtube.com/watch?v=0a-52ntK3T8

Сооснователь Maps.me Юрий Мельничек выпустил приложение для обработки фона фотографий с помощью нейросетей Сегментацию делает CNN на GPU (на старых телефонах на CPU) Работает по похожему принципу http://xiaoyongshen.me/webpage_portrait/papers/portrait_eg16.pdf Обсуждение в telegram https://telegram.me/joinchat/ABI4pz6rz2iVzWUzaVqpmA

Histogram of Oriented Gradients | Learn OpenCV www.learnopencv.com/histogram-of-oriented-gradients/

Implementing a CNN for Human Activity Recognition in Tensorflow

Видео доклада про практики ЖЦ систем машинного обучения http://ailev.livejournal.com/1316740.html?utm_source=vksharing&utm_medium=social

Презентация Deep learning for audio-based music recommendation We are delighted to republish slides on Deep learning for audio-based music recommendation by Sander Dieleman. Sander is a Research Scientist at DeepMind, and was previously involved in scaling up content-based music recommendation at Spotify. Sander is a PhD student at Ghent University. The presentation covers deep content-based music recommendation approach that is an alternative solution to widely adopted collaborative filtering. Initially slides were presented at Workshop on Deep Learning for Recommender Systems in Bosoton in September 2016. http://www.russia.ai/single-post/2016/12/01/Deep-learning-for-audio-based-music-recommendation