However, when concepts are familiar and the coding shows you a different way to tackle maths problems that you (may) remember from uni days, then this could be the light switch moment that brings code to life. \$\endgroup\$ – … The core of the pedagogy behind Doing Math With Python feels like algebra, which is a good thing since that should at least apply across every high-school equivalent math program. That’s what attracted me to read a book entitled Math for Programmers—not to learn maths but to see simple maths concepts turned into code and to use that information to improve my appreciation and knowledge of code and programming concepts. Subsequent chapters build on your nascent programming skills by exploring how Python visualization can help you create charts, graphs, and plots. If you�re not a high-school math student, teacher, or parent, there are a few reasons this book may appeal to you. It�s not the shortest distance between your budding math student and completing her homework, but it has the potential to make math seem a lot more interesting. It’s a pretty easy read if you want to skim it. You learn what a set is as a data structure but the examples don't really demonstrate why you might want a set. If you think you fit into it - just enough Python and just enough math - then you will find some of the ideas fun. Instead, it was a way to learn more about Python while using familiar maths concepts in linear algebra and calculus to appreciate what Python code is actually doing. Copyright © 2009-2020 Here is some sample code from this section of the book: MLP classifiers are a simple form of neural networks which are found everywhere today. Latest actuarial news, features and opinions delivered straight to your inbox. In my mind, this perfectly simulated the TV reception available in country Victoria in the 70s and 80s. You�ll quickly see how thinking of Python like a calculator is like using a Formula-1 race car to drive a few blocks and pick up groceries� The book quickly jumps from command-line exploration to writing simple programs that grab user input and use simple concepts like modules, classes, variables, and loops to perform tasks that would otherwise be incredibly time consuming by hand or with a calculator. If you are new to both coding and machine learning but can remember basic linear algebra and calculus then you can whiz through the text, code up the maths and check your understanding of Python coding in the early sections. If you’re already hard core in either machine learning theory or programming, this book probably isn’t for you. Your comment will be revised by the site if needed. Author: Amit SahaPublisher: No Starch PressPages:264 ISBN: 978-1593276409Print:1593276400Kindle:B014EELUFQAudience: Readers with just enough math and just enough Python.Rating: 4.5Reviewer: Mike James. Aaron Cutter, of the Actuaries Institute’s Data Analytics Practice Committee, provides a detailed review of Math for Programmers in the context of learning Python for Data Analytics. Already proficient with math "in theory" and want to learn how to translate math formulas and concepts into computer code. That was absolutely my challenge back in the early days of the modern WWW; Python was useful in a few select ways, but hobbyist projects (much less mainstream applications) were few and far between. Chapter 2 is about drawing graphics using Matplotlib. read more. Published: Tue 24 July 2018 By Amit Saha. Being able to calculate gradients in three and higher dimensions therefore becomes a prerequisite to being able to understand what is going on inside algorithms like Gradient Boosting Machines. Humble bundle. Therefore, if you are newish to programming, and in particular Python, I would recommend Math for Programmers – 3D graphics, machine learning, and simulation with Python” by Paul Orland. Doing Math with Python in Linux Geek Humble Bundle. In updates. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of … As long as you know enough of the theory then the programming exercises might be interesting enough to deepen your understanding. Doing Math With Python dedicates just a few pages to remedial language concepts before jumping into writing full programs. However this said it is clear that you are going to have to know quite a lot of Python to get anything much from this book. For example, facial recognition can be used to keep shop front doors locked to people not wearing masks— this is a real example from my son’s work. The example projects are gravitation and projectile motion. For example, many machine learning algorithms require maximizing or minimizing functions in multiple dimensions. Humble bundle. After all we go to a lot of trouble to implement the set as a data structure we have to make sure that duplicates are eliminated and that there no implied order. There’s a simple pleasure obtained when you can see in pictures the effects of your code. So example that make use of sets really need to have the property that the have duplicates that need to be removed and for which set operations are natural - unions, intersections and especially the Cartesian product. If the book expanded on these then it would be a much more valuable source book of examples. Not quite up to what you can do using Wolfram Alpha or Mathematica on a Raspberry Pi but interesting. I don’t know if you have found machine learning and programming books as dry as I have? The big problem with this book is that it is light on the math. Facial recognition algorithms typically use some form of neural network. This field is for validation purposes and should be left unchanged. Along the way, you�ll deepen your skills in the language. This is still just a fraction of Python�s true power, of course. I supplemented my understanding of Python functions by reading A Beginners Guide to Python 3 Programming. I'm not at all sure how you could make use of these in the classroom however unless your students also programme in Python. I then used the following code to add a random amount to each of the RGB components of each pixel in the image. For example, in the machine learning section, the following code creates a gradient descent model using a logistic classifier to find the greatest separation between BMW and Prius cars. \$\begingroup\$ Python 2 actually. There is quite a large portion of the book devoted to vector manipulation. CPD: Actuaries Institute Members can claim two CPD points for every hour of reading articles on Actuaries Digital. It’s sometimes difficult to get enthused reading a book that starts off with yet another ‘hello world’ code example. You could say the same for the math but there are more explanations of the math, they are just not adequate to cope with such a difficult subject. Because let’s face it: If you’re a hobbyist without a stream of gnarly problems that Python is uniquely equipped to solve, you won’t use what you learn. Math and Python? Doing Math with Python shows you how to use Python to delve into high school–level math topics like statistics, geometry, probability, and calculus. Not to say this is required reading for future data scientists, but it�s recognition that the world of Python has moved way past parsing log files and creating simple interactive CLI programs. The book contained a lot of sample code to explain concepts. If you are a math teacher with a knowledge of Python then this book might provide some useful practical examples of the two things working together. Here’s an example of doing multiplication in Python with two float values: k = 100.1 l = 10.1 print(k * l) Anaconda 5.0 release. As well as the basics we also take a look at optimization by way of gradient ascent. Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More! The book makes heavy use of visualisations, including to help readers step through the data, weights and biases in each layer of a neural network. Creative Commons Attribution-NonCommercial-No Derivatives CC BY-NC-ND Version 3.0 (CC Australia ported licence), Make Actuaries Generate Analytics: A Serial Twitter Analysis for the 2020 US Presidential Election by yDAWG Analytica, Actuarial graduates are being headhunted in the midst of a recession, When the algorithm fails to make the grade, General insurance sector performance analysis in annual Optima publication. Specialized topics and specialized tools in Python are addressed, and always with a nod toward cross-platform development. Published: Fri 29 March 2019 By Amit Saha. But if you have a passing interest in learning Python or you’re looking for your first foray into machine learning then this book is definitely worth a shot. A crossover book -  is this a good idea? New tools are accompanied by an explanation of how to install and use them, whether at a command-line/module/library level, or standalone products like Anaconda. With the aid of the Python programming language, you'll learn how to visualize solutions to a range of math problems as you use code to explore key mathematical concepts like algebra, trigonometry, matrices, and cellular automata. The same is true of math skills. It�s a terrifically practical way to jump into programming Python and has some especially nice points connecting the work you do here to the kind of analysis you might be called on to do in a business or research setting. The idea seems to be that the two help illuminate each other. Having said that, the book did provide a refresher on the product and chain rules and partial differentiation. Python’s Scikitlearn module uses the MLPClassifier to automate a lot of these steps.

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