Hi,
I'd like to learn more about artificial neural networks, specifically pattern recognition algorithms. Unfortunately, I don't have much of a background in mathematics, so I'm looking for something that won't overburden me with too many technical details. Could someone recommend a fairly-light-yet-scientifically-rigorous read on the topic?
Thanks!
Deep Learning: A Practitioner's Approach
Book by Josh Patterson
It is good book for Neural Network...
Hi Dawid,
From my understanding of the subject, if you want to know what is happening behind the scenes and why you do certain things in artificial neural networks (presently, in a deep learning setting rather than a general machine learning setting) you should be comfortable with mathematics. But you do not have to be an expert at mathematics to get to know neural networks. Moderate familiarity with calculus, probability and linear algebra will suffice for one to understand the concepts well.
Having said that, currently, a part of the deep learning community is trying hard to make it available for the ''non-mathematics'' community. Practical Deep Learning For Coders (http://course.fast.ai/lessons/lessons.html) is a good resource to get started if you want to dive in directly and learn as you go on. From the first day onwards, you are instructed to build deep learning algorithms that are par with the state of the Art. The video lectures that you find there give you the reasoning behind why you do certain things in neural networks without digging down into hard core mathematics.
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