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AI, Machine Learning and Deep Learning are on the verge of innovating something that once upon a time seemed unthinkable. Deep learning is transforming heavily regulated industries like finance, trading, healthcare, and life sciences. For people who have not worked with Deep Learning yet, Keras library is good for a great start as it is designed for easy neural network assembly which comes with several pre-packaged network types like CNN’s in 2D and 3D flavours, long and short term neural networks and more general recurrent neural networks. Our expert panel suggests working with Keras Deep Learning library because implementing neural networks with Keras is straight-forward once you have determined what kind of neural network you want to build. Keras semantics are very layer-oriented that makes network assembly comparatively intuitive.
For Data Science enthusiasts who have Computer Vision and NLP as their bias, Python programming language’s Keras is a must to explore. It is the perfect library for implementing deep learning algorithms in Data Science projects. Keras has computation powers of the two powerful libraries Theano and TensorFlow imbibed in it, and thus, it allows training neural network models with few lines of code. Also, the Python library has simple methods which make it easy for deep learning researchers to design the layers of a neural network model.
You will not regret working on these deep learning project suggestions. See what all you will learn –
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