Python crash course

What Hacker News says

1. AlexeyBrinApr 2024
   For hobby programming, Python is the best (unless you want to do web development in which case JavaScript is what you want). For Python, a really good book for beginners is Python Crash Course.
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2. wyclifMar 2024
   That's all good advice. You should definitely not read programming books like normal books. But I'll extend it somewhat:

   ~ Yes, always do all the exercises. It's important how you do them. No copypasting. Enter all the code examples in your text editor of choice and run them if possible. Experiment liberally and don't be afraid of errors; instead, adjust the program in response to the errors. Create a directory and save all your files; don't just keep overwriting the same exercise. This is so you can go back and review if necessary.

   ~ If possible, always use the PDF formatted version of the book. EPUB and other formats too often don't look good and aren't formatted as the author(s) intended, and PDFs tend to be easier to follow because the page is formatted the same as the print version.

   ~ I use the Pomodoro technique of working intensely on the book for 25-30 minutes and then taking a short break before continuing. This tends to help me focus and retain more of the book.

   Back when I was learning Python, I used two books in sequence, "Python Crash Course" by Matthes and "Think Python" by Downey. This turned out to be fortuitous, because until I started working through them I didn't know they are two completely different approaches: PCC teaches you how to program in Python and TP teaches you computer science using Python. Working through both books consecutively gave me a much better scope and understanding of the language to build on than using one book alone and stopping there.

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3. FullStackDev1Apr 2023
   I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way

   Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here's a detailed learning plan, broken down into four stages:

   Stage 1: Building a Strong Foundation

   Mathematics:

   Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang

   Calculus: "Calculus: Early Transcendentals" by James Stewart

   Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

   Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

   Programming:

   Python: "Python Crash Course" by Eric Matthes

   Git: "Pro Git" by Scott Chacon and Ben Straub

   Machine Learning & Data Science Basics:

   "Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas

   Stage 2: Learning Machine Learning and Deep Learning

   Online Courses:

   Coursera: "Machine Learning" by Andrew Ng

   Coursera: "Deep Learning Specialization" by Andrew Ng

   Fast.ai: "Practical Deep Learning for Coders"

   Books:

   "Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   Stage 3: Expanding Knowledge of AI Subfields

   Natural Language Processing (NLP):

   "Speech and Language Processing" by Daniel Jurafsky and James H. Martin

   "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

   Computer Vision:

   "Computer Vision: Algorithms and Applications" by Richard Szeliski

   "Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:

   "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

   "Deep Reinforcement Learning Hands-On" by Maxim Lapan

   Stage 4: Staying Current and Gaining Practical Experience

   Read research papers:

   Subscribe to the arXiv mailing list in your areas of interest Regularly read papers from conferences like NeurIPS, ICML, and ACL Participate in online competitions:

   Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio AIcrowd: Another platform for AI competitions Contribute to open-source projects:

   TensorFlow, PyTorch, or other popular AI frameworks Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation Network with AI professionals:

   Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/MachineLearning Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!

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4. an_aparallelFeb 2023
   as someone who's not a software developer, but has tried multiple times to get into programming over the years...

   all i can say is something really simple:

   *you need to try, and try again, and practise...regularly.

   Down time at work- has had me start on Harvards CS50x and CS50p. I switched between them as i thought starting with Python would be a bit gentler. Which then led me to supplement both of these with Python Crash Course (the book) - which has been just so awesome.

   I went from "ok, cool...hello world, whatever"...and "fucks sake, how do devs look at code Tand not get migraines"...to actually writing more than 5 lines of code, reading stuff on stack exchange and sort of following along.

   I dont have a particular knack for any of this -i'd argue the amount of times i've tried and failed miserably at it should be enough to make me stop permanently.

   But honestly - just doing it - every day, typing out every example of code in every example you read, running it over and over and over again...i'll be fucking damned if i wasnt leaps and bounds better than i was two months ago.

   I distinctly remember thinking - "is programming really just always going to stack overflow and looking up the answers"....to actually at least stopping, and thinking HARD about wtf I am trying to do...slowing my mind down, and walking through words slowly...

   So yeah -i'll say - for sure - I'm average as hell, and just trying every damn day has made a huge difference for me....and i've yet to scratch the surface! But comparing me with 2 months ago me, i couldnt be happier.

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5. rg111Jan 2023
   I recently finished the book that teaches programming by developing games using DragonRubyGameToolkit. Really loved this book.

   _____

   Python Crash Course by Eric Matthes has a section dedicated to projects.

   I really liked this book.

   This is what taught me Python.

   I knew C before.

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6. Hermann12Jan 2023
   Hi, I learned FORTRAN on University. After that I don’t programming, but as an engineer I searched some solutions for automatisation. So I have to learned programming again, because FORTRAN can’t solve my requirement.

   Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming, by Eric Matthes is my favourite for self study the basics.

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