Deep Learning Frameworks (C2W3L10)

Key Takeaways

you've learn to implement deep learning algorithms more or less from scratch using Tyson and numpy and I'm glad you did that because I wanted you to understand what these deep learning algorithms are really doing but you find it lets you implement more complex models such as composition areas or recurrent neural networks well if you start to implant very large models that is increasingly not practical ways for most people it's not practical to instrument everything yourself from scratch fortunately there are now many good deep learning software frameworks that can help you implement these models to make an analogy I think that hopefully you understand how to do a matrix multiplication and you should be able to implement you know how to code to multiply two matrices yourself but as you build very large applications you probably not want to implement your own matrix multiplication function but instead you want to call a numerical linear algebra library they could do it more efficiently for you but it still helps you understand how multiplying two matrices were so I think deep learning has now matured to that point where it's actually more practical and you be more efficient doing some things with some of the deep learning frameworks so let's take a look at the frameworks out there today there are many deep learning frameworks that makes it easy for you to implement new networks and here are some of the leading ones each of these frameworks has a dedicated user and developer community and I think each of these frameworks is a credible choice for some subset of applications there are a lot of people writing articles comparing these deep learning frameworks and how well these people in frameworks changes and because these frameworks are often evolving in getting better at month-to-month I'll leave you to do a few internet searches yourself if you want to see the arguments on the pros and cons of some of these frameworks but I think many of these frameworks are evolving and getting better very rapidly so rather than to strongly endorsing any of the Australian works I want to share of you the criteria on I would recommend you use to choose framework one important criteria is the ease of programming and that means both developing the neural network and iterating on it as well as deploying it for production for actual use by you know thousands or millions or maybe hundreds of millions of users depending on what you're trying to do a second important criteria is running speeds especially training on large data sets some frameworks will let you run in training your network more efficiently than others and then one criteria that people don't often talk about but I think is important is whether or not the framework is truly open and for a framework to be truly open it means not only to be open source but I think it means good governance as well unfortunately in the software industry some companies have a history of open sourcing software but maintaining single corporation control of the software and then over some number of years as people start to use their software some companies have a history of gradually closing off what was open-source or perhaps moving functionality into their own proprietary cloud services so one thing I pay a bit of attention to is how much you trust that a framework will remain open source you know for a long time rather than just being under the control of a single company which for whatever reason may choose to close it off in the future even if the software is currently released under open source but at least in the short term depending on your preferences of language whether you prefer Python or Java or C++ or something else and depending on what application you're working on whether it's computer vision or natural language processing or online advertising or something else I think multiple of these frameworks could be a good choice so that's it on programming frameworks by providing a higher level of abstraction than just a numerical linear algebra library any of these programming worlds can make you more efficient as you develop machine learning applications