Deep Learning: Exploring High Level APIs of Knet.jl and Flux.jl in comparison to Tensorflow-Keras by Al-Ahmadgaid B.It is seen as a subset of artificial intelligence.Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning (ML) is the study of computer algorithms that improve automatically through experience.
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In part 1 of the Deep Learning in Production course, we defined the goal of this article-series which is to convert a python deep learning notebook into production-ready code that can be used to serve millions of users.
Its documentation is available at keras.io. It’s compatible with any version of Python from 2.7 to 3.5. Keras is distributed under the permissive MIT license, which means it can be freely used in commercial projects. This means that Keras is appropriate for building any deep learning model, from a memory network to a neural Turing machine.This series of articles will walk through a complete machine learning solution with a real-world dataset to let you see how all the pieces come together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem.
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