Deep learning emerged from that decade's explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.Source: Kdnugget
Speech recognition is the task of detecting spoken words. There are many techniques to do Speech Recognition. In this post, we will go through some background required for Speech Recognition and use a basic technique to build a speech recognition model. The code is available on GitHub. For the techniques mentioned in this post, check this Jupyter Notebook.Source: HOB
In machine learning, huge data goes into training models. Many times the model becomes complex so the cost of model training increases at a higher rate. Models of machine learning which are complex can easily be prepared only if you have years of experience. The models are not created very efficiently if the machine learning and artificial intelligence engineers do not have a good experience. This is how to transfer learning is more significant for deep learning.Source: HOB
Let's do a quick Turing Test. Below, you'll see ten machine learning project ideas. Five of them are generated by a human and five of them are generated by a neural network. Your task is to tell them apart.Source: HOB
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