Deep Learning for Physical Scientists. Edward O. Pyzer-Knapp

throughout this book, I will be teaching you how to use a state of the art deep‐learning framework known as TensorFlow and provide real world examples. These examples will not be aimed at squeezing every last piece of performance out of the system, but instead at ensuring that you understand what is going on. Feel free to take these snippets and tune them yourself so that they work well for the problems you are tackling. Finally, I hope that you get as much fun out of coming on this journey with me, as I have had putting it together. I hope that this book inspires you to start breaking down barriers and drive innovation with data not just in your domain, but in everything you do.

2 Setting Up a Python Environment for Deep Learning Projects

      2.1 Python Overview

      Why use python? There are a lot of programming languages out there – and they all have their plus and minuses. In this book, we have chosen to use Python as our language of choice. Why is this?

      First of all, is the ease of understanding. Python is sometimes known as “executable pseudo code,” which is a reference to how easy it is to write basic code. Now this is obviously a slight exaggeration (and it is very possible to write illegible code in Python!), but Python does represent a good trade‐off between compactness and legibility. There is a philosophy which went into developing Python which states “There should be one (and preferably only one) obvious way to do a task.” To give you an illustrative example, here is how you print a string in Python:

       print("Hello World!")

      It is clear what is going on! In Java it is a little more obscure, to deal with system dependencies:

       system.out.println("Hello World!")

      And in C, it is not obvious at all what is going on (C is a compiled language so it really only needs to tell the compiler what it needs to do):

      “Hello World!" >> cout

      In fact, C code can be so hard to read that there is actually a regular competition to write obfuscated C code, so unreadable it is impossible to work out what is going on – take a look at https://www.ioccc.org/ and wonder at the ingenuity. So by choosing to use Python in this book even if you are not a regular Python user you should be able to have a good understanding of what is going on.

Second is the transferability. Python is an interpreted language, and you do not need to compile it into binary in order to run it. This means that whether you run on a Mac, Windows, or Linux machine, so long as you have the required packages installed you do not have to go through any special steps to make the code you write on one machine run on another. I recommend the use of a Python distribution known as Anaconda to take this to a new level, allowing very fast and simple package installation which takes care of package dependencies. Later on, in this chapter, we will step through installing Anaconda and setting up your Python environment.

      One other reason for using Python is the strong community, which has resulted in a huge amount of online support for those getting into the language. If you have a problem when writing some code for this book, online resources such as stackoverflow.com are full of people answering questions for people who have had the exact same problem. This community has resulted in the surfacing of common complaints, and the community collectively building solutions to make libraries for solving these problems and to deliver new functionality. The libraries publically available for Python are something quite special, and are one of the major reasons it has become a major player in the data science and machine learning communities.

2.2 Why Use Python for Data Science?

      Recently, Python has seen a strong emergence in the data science community, challenging more traditional players such as R and Matlab. Aside from the very intuitive coding style, transferability, and other features described above, there are a number of reasons for this. First amongst these is its strong set of packages aimed at making mathematical analysis easy. In the mid‐1990s the Python community strongly supported the development of a package known as numeric whose purpose was to take the strengths of Matlab's mathematical analysis packages and bring them over to the Python ecosystem. Numeric evolved into numpy, which is one of the most heavily used Python packages today. The same approach was taken to build matplotlib – which as the name suggests was built to take the Matlab plotting library over to python. These were bundled with other libraries aimed at scientific applications (such as optimisation) and turned into scipy – Python's premier scientific‐orientated package.

Having taken some of the best pieces out of Matlab, the Python community turned its attention to R; the other behemoth language of data science. Key to the functionality of R is its concept of the data frame, and the Python package pandas emerged to challenge in this arena. Pandas' data frame has proven extremely adept for data ingestion and manipulation, especially of time series data, and has now been linked into multiple packages, facilitating an easy end to end data analytics and machine learning experience.

      It is in the area of machine learning in which Python has really separated itself from the rest of the pack. Taking a leaf out of R's book, the scikit‐learn module was built to mimic the functionality of the R module caret. Scikit‐learn offers a plethora of algorithms and data manipulation features which make some of the routine tasks of data science very simple and intuitive. Scikit‐learn is a fantastic example of how powerful the pythonic method for creating libraries can be.

2.3 Anaconda Python

      2.3.1 Why Use Anaconda?

      When you first pick up this book, it may be tempting to run off and download Python to start playing with some examples (your machine may even have Python pre‐installed on it). However, this is unlikely to be a good move in the long term. Many core Python libraries are highly interdependent, and can require a good deal of setting up – which can be a skill in itself. Also, the process will differ for different operating systems (Windows installations can be particularly tricky for the uninitiated) and you can easily find yourself spending a good deal of time just installing packages, which is not why you picked up this book in the first place, is it?

      Anaconda Python offers an alternative to this. It is a mechanism for one‐click (or type) installation of Python packages, including all dependencies. For those of you who do not like the command line at all, it even has a graphical user interface (GUI) for controlling the installation and updates of packages. For the time being, I will not go down that route, but instead will assume that you have a basic understanding of the command line interface.

      2.3.2 Downloading and Installing Anaconda Python

      Detailed installation instructions are available on the anaconda website (https://conda.io/docs/user‐guide/install/index.html). For the rest of this chapter, I will assume that you are using MacOS – if you are not, do not worry; other operating systems are covered on the website as well.

      The first step is to download the installer from the Anaconda website (https://www.anaconda.com/download/#macos).

      Conda vs. Mini‐conda

      When you go to the website, you will see that there are two options for Anaconda; Conda; and Mini‐conda. Mini‐conda is a bare‐bones installation of Python, which does not have any packages attached. This can be useful if you are looking to have a very lean installation (for example, you are building a Docker image, or your computer does not have much space for programmes), but for now we will assume that this is not a problem, and use the full Anaconda installation, which has many packages preinstalled.

      You can select the