List of learning Materials for Deeplearning (free)!!!
Hello Everyone ! As we know that deeplearning is currently a hot topic in the field of technology . Deeplearning comes under a section of machine learning which further is a branch of AI (artificial intelligence). Learning algorithms in deeplearning can be of three types : supervised / semisupervised/unsupervised learning .You may be thinking about what makes other artificial neural networks differs from it ,right ? well , you can think deep neural networks as an artificial neural networks with deep (multiples ) layers. So ,for today we aren’t going to discuss more about deeplearning and things inside it rather materials for learning it (as mentioned in the topic 😃)
MOOCs / Video
Machine learning course By Andrew Ng :
Andrew Ng , the co-founder of Coursera is one of the best teacher in the field of AI . His machine learning course is one of the most enrolled course and it’s free to enroll . I also highly recommend this course for begineers to start with as you don’t need much prior knowledge of maths and programming to enroll it as he teaches it in matlab language which is easier to learn . (Links given below 👇👇)
Udacity Pytorch course for begineers:
This course is the part of a nanodegree course (deeplearning)made by Facebook . It also teaches you pytorch framework along with deeplearning . Pytorch is one of the most popular python deeplearning framework and my most favourite framework as it is easy to learn and is more pythonic . To enroll this course , you only need to have the prior knowledge of basics maths and basics of python language. (Links given below 👇👇)
Fast.ai deeplearning course :
Fast.ai courses focuses more on practical real life examples . Fast.ai is a deep learning framework which is build on top of pytorch framework . If you have the habit of learning things by doing practicals more than theory then this course is for you !
Mathematics for Machine learning :
As you know , machine learning models works purely on the principle of mathematics . This course is highly recommended by me for learning maths which for related to machine learning so that you can understand the logics behind the working of machine learning algorithms . This course consist of three sub courses .(Note : you can enroll paid courses on coursera(a site for courses) if you enroll it’s sub courses and click on audit only and continue)
- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA
Recommended Books :
- Deeplearning book by Ian goodfellow
- Mathematics for machine learning book
- Pattern Recognition and Machine Learning
You might be thinking about your specification of your computer , right ? but don’t worry about it as you can use kaggle notebook or google collabs to run your code in a cloud for free 😊 and you don’t to worry about your computer performance 🥱.
Additional links :
- https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
- https://www.youtube.com/user/MIT
- https://www.youtube.com/user/Zan560
- https://deep-learning-drizzle.github.io/
- https://ai.google/
- http://pytorch.org/
- https://www.kdnuggets.com
Conclusion :
Thanks for reading 🤗🤗, apart from these learning materials you guys should also make the habit of reading research papers (Yeah , we might be struggling to read most of the research papers out here but don’t worry about it as we have a Genie 🧞 called google ). Once , you sharp your maths , it will be quite easier to read it . [Recommended site for maths : https://ocw.mit.edu/index.htm , Discussion about papers : https://www.youtube.com/user/keeroyz].
As we know only god is true perfect , my posts can also have some weakness .Please kindly provide your feedback so that i can improve myself. You can contact me at : nirajan.data@gmail.com
