71–77 of 77 posts
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I'm working my way through Elements now. Do you by any chance know of any lectures specifically based on it? And solutions to the exercises there? I have found it hard to find (good) solutions.
The Machine Learning Course offered by Prof. Ravindran at Indian Institute of Technology (IIT- Madras) uses ESLR. The course is free to enroll and you will have weekly assignments. https://onlinecourses.nptel.ac.in/noc18_cs26/preview
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hey neel8986, I know linear algebra is very important for large scale calculations. But how much calculus and statistics do you need for ML? Also, if you can touch what applications of calculus and statistics are used in ML that would be awesome :]. THANKS!
Regarding calculus, I think basic multivariable calculus can be enough for starting. If you need a refresher you can look for ( https://ocw.mit.edu/courses/mathematics/18-02sc-multivariabl... ) Also the basic idea of chain rule is important for deep learning. Regarding statistics, I already mentioned the probability course which describes most of the important statistics concept you need. Also, some idea of Hypothesi…
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This reads as an amazing journey. Kudos for your pursuit of a better process. It seems to me that so many (online) courses jump to applying tf/pytorch to a predefined dataset, whereas most of the work is in preparing the data. I have a personal project I'd like to try out classifying images, and haven't had much luck finding resources on building my own training dataset. Can you recommend any resources on assembling…
It should be noted that I deal primarily with geo-spatial image analysis, so there is a not insignificant amount of bias with regards to what data I'm interested in. I like using the USDA NAIP API for imagery, since I can call in imagery using GDAL directly into python or R. I rely heavily on freely available public utility data sets (Parcel level utility data). Beyond that and other than as a starting point, you're…
Learning from Data by Yaser Abu-Mostafa, and its companion book. Very theoretically heavy but worth the trouble (this is Caltech course)
Do all assignments in the course with Python/Numpy/Scikit-learn
2. Choose your niche
You need to NARROW down your interest. You can start broad just to know enough basics, but gradually pinpoint to making/hacking stuff that is most fun for you. Test waters on computer vision, speech, NLP, and games/robotics (reinforcement learning), or other less popular fields.
E.g. Start with computer vision -> Basic convnet image classification -> Encoder-Decoder architectures -> End-to-end ConvNet monocular RGB to depth image generator
Read papers that have published code on github, this let you reproduce and understand how things work, so that after awhile you can hack your own models and stuff
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It should be noted that I deal primarily with geo-spatial image analysis, so there is a not insignificant amount of bias with regards to what data I'm interested in. I like using the USDA NAIP API for imagery, since I can call in imagery using GDAL directly into python or R. I rely heavily on freely available public utility data sets (Parcel level utility data). Beyond that and other than as a starting point, you're…
PM OTW...
In October, I quit my job to live on savings and work on AI/ML. I am not interested in developing novel AI approaches; my objective is to learn the application of machine learning to solve narrow problems. In light of that, I believe the technology to have progressed to where one can learn how to use existing libraries to solve specific problems. Lots of businesses have specific problems, and will pay me to solve the…
I've been doing Coursera and got done with Udacity but both are just too philosophical. (I'll probably cruse the deep learning class too.) Does FAST.AI get into the grit of actually doing or is it more philosophical?
1. ConvNet (for images and structured data)
2. RNN (for text and sequence data).
They don't teach you far advanced stuff, like how to creatively misuse the hammers to knock different nails (example: using ConvNet, tweak as causal convolution to handle sequence data)
Pattern Recognition and Machine Learning by Christopher Bishop. Book: http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%... Notes: Its very very math heavy but if you really want to grasp the concepts and the idea around each topic, this is one of the way to go. Online Lectures: https://www.youtube.com/watch?v=mbyG85GZ0PI&list=PLD63A284B7... I like how he explains stuff and adds some context behind the math…
Bishop's book is horrible. I've got a PhD in machine learning and yet it makes me feel stupid. I'd go with David Barber's book every time.