Fast introduction to deep learning in Python, with advanced math and some machine learning backgrounds, but not much Python experience I've the following somewhat unusual background and I've managed (probably by luck) to get an industry job of a computer vision researcher using deep learning.

My background: I've a PhD in pure math, and have the following machine learning experience: linear and logistic regressions, support vector machines (SVM), linear and quadratic discriminant analyses (LDA/QDA), decision trees, PCA. I'm familiar with the mathematical theory of all the above, and implemented the LDA/QDA, SVM, PCA in matlab, where I'm proficient in.

However, my experience with Python and deep learning are very limited to zero. I don't know scikit learn, only somewhat familiar with NumPy arrays, but still have problems with the basics like concatenation with empty arrays. I don't know for examples the data structures like classes or lists in Python-just to give you an idea. I know what a neural network is, but don't know anything else, e.g. backpropagation or autoencoder. Hence I don't know the theory of deep learning as well.

Time constraint: So I'm in a situation where I'll have to gain some hands on experience in both Python as well as deep learning, possibly in 2 months (the company has a 4 months trial period and use only Python, but they'll get a sense of my learning in 2 months, hence I've to learn fast).

Question: Given my expertise and time constraints, could you mention a road map (if possible) so that I can get a fast introduction to deep learning and its Python libraries (e.g. Tensor Flow or Keras etc.)? I know there're tons of resources out there, but not all of them can be learnt within a short time span.

Thanks so much in advance!!!
Stanfords CS231n has a great python+numpy tutorial.
http://cs231n.github.io/python-numpy-tutorial/

As for Neural Networks and Keras + Tensorflow, I can recommend the Deep Learning specialization on Coursera. It is free for one week. If you do not want the certificates you can download all videos and stop.

If you want the certficates you can enroll in all courses simultaneously and finish them faster than advertised. They assume 3-6h per week for 16 weeks. If you have 6h per day, it is possible to go through a weeks content per day. Andrew Ng speaks clearly and the videos are understandable at 1.5x-2x speed.

As for the roadmap, i would suggest to


start with python + numpy until you have a basic understanding of array slicing and read up on unknown commands when you encounter them.
start a DL course of your choice but do not skip classes. That is I think it is important to know the basics even if you will only use high level frameworks later on.