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!!!