Hi HN,
I have started my study on DeepLearning from March.
Initially, I went with no Math courses like fast.ai and Started working on code. But to implement something new I needed a Deeper Knowledge on Research papers so I have completed the following courses.<p>1. Andrew Ng's DeepLearning Specialization<p>2. NLP from Stanford.<p>then I went to https://www.deeplearningbook.org book by Ian Goodfellow.
But the unsupervised learning part too abstract. that I feel like I am still missing the foundation for understanding deeplearnning.<p>Any recommendation?
You can not really appreciate the beauty of machine learning without the maths behind it. I don't know what your maths background is but if I had to recommend, a good understandig of Linear Algebra makes all the difference when you read ML papers. I would storngly recommend Gilbert Strang's two courses on Linear Algebra [1] and Matrix Methods[2].<p>Everything else that mostly cares about application is easy to find on the internet (see the r/MachineLearning subreddit).<p>[1] <a href="https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/" rel="nofollow">https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...</a>
[2] <a href="https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/" rel="nofollow">https://ocw.mit.edu/courses/mathematics/18-065-matrix-method...</a>