Fundamentals of Deep Learning begins by explaining

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what a neural network is and how it is trained, and shows how to implement a network using TensorFlow. It then goes on to cover topics such as convolutional neural networks, representation learning, and sequence analysis. The book has a fairly practical approach and contains exercises with Python source code to perform deep learning activities using tools such as TensorFlow.

Overall, Fundamentals of Deep Learning is a great introduction to the topic, and its code examples will be very helpful for people interested in getting into this area. The only drawback is that the examples (available as a repository on Github) are a bit outdated since they are based on a pre-1.0 version of TensorFlow, and this causes some examples to not work directly algeria consumer email list with the most recent version of TensorFlow (1.2). The flaws are not serious and can be fixed without much trouble, but they can still encourage learning. The book was only published in June 2017, so hopefully the author will take the opportunity to update the examples.Data source and context
The figures and statistics shown in this report were generated based on data obtained during the SG 2017 Salary Survey, conducted in November 2017 through an open online survey. The survey received more than 2,000 responses, but the final data set after removing outliers was 1,760 responses, of which 1,679 were from people who indicated they resided in Mexico. By default, the statistics shown in this report are based on this subset of 1,679 data points.