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description=fast.ai s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, pytorch, time series, and much more ;

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deep learning for coders 36 hours of lessons for free menu home about getting started lessons 1 image recognition 2 cnns 3 overfitting 4 embeddings 5 nlp 6 rnns 7 cnn architectures part 2 machine learning contact blog note from jeremy do you want to study deep learning with me in person i ll be teaching the brand new version of this course at the data institute at usf in san francisco from late october 2018 for details or to apply see the course web site welcome to the 2018 edition of 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 one other thing it s totally free and there s a whole community of thousands of other learners ready to help you with your journey just head over to forums fast ai if you need any help or just want to chat to other deep learning learners fast ai can actually get smart motivated students to the point of being able to create industrial grade ml deployments harvard business review the business of artificial intelligence state of the art we show you how to get world class results step by step designed for coders if you can code you can do deep learning this 7 week course is designed for anyone with at least a year of coding experience and some memory of high school math you will start with step one learning how to get a gpu server online suitable for deep learning and go all the way through to creating state of the art highly practical models for computer vision natural language processing and recommendation systems there are around 20 hours of lessons and you should plan to spend around 10 hours a week for 7 weeks to complete the material the course is based on lessons recorded during the first certificate course at the data institute at usf the lessons view details lesson 1 recognizing cats and dogs lesson 2 improving your image classifier lesson 3 understanding convolutions lesson 4 structured time series language models lesson 5 collaborative filtering inside the training loop lesson 6 interpreting embeddings rnns from scratch lesson 7 resnets from scratch resources forums ask and answer questions on the forums most discussion happens here wiki be sure to check the wiki first if you have a question and help contribute too fast ai blog fast ai announcements and articles will be posted to the blog part 2 when you re done here head over there what you need to succeed pragmatic coding 95 tenacity 85 an open mind 80 high school math 60 what our graduates say view all i ve tried and if i m honest failed to scale the steep deep learning curve many times i realise with hindsight it was the equations that were preventing me from becoming a deep learning practitioner jeremy brought me up to speed with the state of the art and within two weeks i was in the top half of the leaderboard for three kaggle competitions christopher kelly ceo nourish balance thrive if you are looking to venture into the deep learning field look no further and take this course it is very hands on and adopts a top down approach which means everyone irrespective of varying knowledge can get started with implementing deep learning models immediately another major factor why this course is very appealing is its emphasis on social relevance that is how can we use this awesome technology to serve the world better sravya tirukkovalur vice president apache sentry this course filled a gap i couldn t find anywhere else there really is no other source where i could learn from a code first perspective this means you can prod poke and cajole these networks in different ways and see how they respond you can quickly feel an intuitive perspective growing as you explore matt o brien data scientist ucsf neurology i teach machine learning in a master s degree program after this course i cannot ignore the new developments in deep learning i will devote one third of my machine learning course to the subject also i now have the tools to apply deep learning models to real world problems yannet interian assistant professor of analytics university of san francisco it was very empowering to be able to start training a model within minutes downloading the jupyter notebooks jeremy and rachel were excellent instructors and the content was high quality and enlightening it was very cool to be able to read blogposts about the latest deep learning research and actually be able to understand it i was surprised to be able to match academic results from just 2 years ago with pretty simple architectures robin kraft robinkraft product manager at planet labs satellites i m a ceo not a coder so the idea that i d be able to create a gpu deep learning server in the cloud meant learning a lot of new things but with all the help on the wiki and from the instructors and community on the forum i did it jeremy is an incredible instructor and is able to make what might seem like a difficult subject completely accessible nichol bradford executive director of transformative tech lab at sofia university sometimes i feared whether i would be able to solve any deep learning problems as all the research papers i read were very mathy beyond reach of simple intuitive terms but jeremy and rachel course professors believe in the theory of simple is powerful by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the magic deep learning janardhan shetty senior big data engineer at salesforce running a company is extremely time intensive so i was a weary of taking on the commitment of the course it was definitely worth it though it smashed my preconceptions about the technological obstructions to doing deep learning and showed again and again examples where just a small subset of the training data and just a few epochs of training on standard gpu hardware could get most of the way towards a really good model dario fanucchi co founder and cto at isazi consulting it can take years to develop the necessary skills and knowledge for deep learning especially without the support of mentors and peers not only did jeremy teach us the most valuable methods and practices he provided us with an invaluable community and environment the course exceeded my expectations and showed me first hand how both deep learning and ourselves could change the world for better taro shigenori chiba organizer of the sf deep learning study group participants from 2016 onwards fast ai all rights reserved pages home lessons follow our blog contact us contact us email email protected