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This is an excellent course for those who want to develop applications that use neural networks meaningfully. However, I did not find hints on solving the problem of what data to put on the input level.

For example, for a neural network that evaluates a chess position, there can be at least 4 different approaches to this: 64 numbers or codes that describe the content of each of the cells of the chessboard; 32 numbers describing the position of chess pieces (or maybe 64 again, if we describe the position of each piece by vertical and horizontal lines, and not by the single cell number; 10 64-bit sets that give the placement of the same type of pieces (5 types, each from a pawn to a king, taking into account 2 colors) on a chessboard (this is the representation used by the leading chess programs to maximize the speed of enumeration of possible lines of moves); finally, just a variable length standard FEN string, which gives the generally accepted description of a chess position (however, also line-by-line for each of the horizontals, i.e., consisting of 8 parts). Before doing this by trial and error, I would like to hear some kind of "philosophy" about this.

Also, at the end of this course, I would like to try to work with the code in Notebook, as it was in the previous ones.

In this course, the instructor from his experience gained through several machine learning and deep learning projects explains how to prioritize tasks in a big machine learning projects. This course does not introduce the reader to CNN or RNN but rather makes the user aware of some ML/DL tips to make the most efficient use of time and resources. Some of the most important questions addressed in this course are: 1) Why a single evaluation metric is important and what are some of the widely used metrics? 2) What is human-level performance and is it a good estimate of Bayes error? 3) What is Orthogonalization in the context of ML tasks and why is it important? 4) How to measure avoidable bias, variance error, data mismatch etc? 5) How to address data mismatch error? What is transfer learning and how is it different from multi-tasking 6) Whether one should opt for traditional or end-to-end deep learning approach?