发帖
雷达卡
Why another book on applied deep learning? That is the question I asked myself before
starting to write this volume. After all, do a Google search on the subject, and you will
be overwhelmed by the huge number of results. The problem I encountered, however,
is that I found material only to implement very basic models on very simple datasets.
Over and over again, the same problems, the same hints, and the same tips are offered.
If you want to learn how to classify the Modified National Institute of Standards and
Technology (MNIST) dataset of ten handwritten digits, you are in luck. (Almost everyone
with a blog has done that, mostly copying the code available on the TensorFlow web
site). Searching for something else to learn how logistic regression works? Not so easy.
How to prepare a dataset to perform an interesting binary classification? Even more
difficult. I felt there was a need to fill this gap. I spent hours trying to debug models
for reasons as silly as having the labels wrong. For example, instead of 0 and 1, I had
1 and 2, but no blog warned me about that. It is important to conduct a proper metric
analysis when developing models, but no one teaches you how (at least not in material
that is easily accessible). This gap needed to be filled. I find that covering more complex
examples, from data preparation to error analysis, is a very efficient and fun way to learn
the right techniques. In this book, I have always tried to cover complete and complex
examples to explain concepts that are not so easy to understand in any other way. It is
not possible to understand why it is important to choose the right learning rate if you
don’t see what can happen when you select the wrong value. Therefore, I always explain
concepts with real examples and with fully fledged and tested Python code that you
can reuse. Note that the goal of this book is not to make you a Python or TensorFlow
expert, or someone who can develop new complex algorithms. Python and TensorFlow
are simply tools that are very well suited to develop models and get results quickly.
Therefore, I use them. I could have used other tools, but those are the ones most often
used by practitioners, so it makes sense to choose them. If you must learn, better that it
be something you can use in your own projects and for your own career.
京公网安备 11010802022788号
