system-design-interview
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system-design-interview
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Advice on Learning the Cloud
github - https://github.com/donnemartin/system-design-primer, https://github.com/checkcheckzz/system-design-interview, https://github.com/karanpratapsingh/system-design
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100+ Must Know Github Repositories For Any Programmer
12. System Design Interview
- Are there existing podcasts or videos that talk about the architecture of major applications?
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Interview Discussion - May 16, 2022
System design questions: at the senior level you'll definitely need to be able to demonstrate system design ability. Explaining your decision making process is key. There are many GitHub compilations for study material, like this one.
- Gehaltsvorstellung Senior Entwickler Google Deutschland?
- Comprehensive list of resources to prepare for job and overview of experience, compensation, and startup equity offers (currently in Bay Area - most companies are HQ'd in Bay Area, but positions are remote)
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System Design books/website/videos recommendation?
This is what I use. Make sure to watch the Harvard lecture video. https://github.com/checkcheckzz/system-design-interview
materials
- Think Python, 3rd Edition
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Collection of resources to get started on your programming journey
Python - Python.org - Official Python website with documentation and tutorials. - Codecademy Python Course - Real Python - Python tutorials and articles for all skill levels.
- I have started my Python self Learning Journey - Is one source enough?
- How to manage procrastination and lack of focus during learning?
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Other programing options?
Real Python (https://realpython.com/)
- Whats the best way to learn python for free?
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Best free sites to learn Python Courses
https://realpython.com are very good in my opinion.
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Recommended learning resources for rust
I know the official sites/books but am looking for something a bit more convenient and guided, similar to https://realpython.com/ which has really good text articles and also video courses covering specific topics.
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How do I get started with ML?
Learn Python: Python is the most popular language for ML and AI projects. Start by learning the basics of Python, then move on to more advanced topics. Some great resources for learning Python include: Codecademy's Python course: https://www.codecademy.com/learn/learn-python Real Python: https://realpython.com/ Mathematics: A solid understanding of mathematics, particularly linear algebra, calculus, probability, and statistics, is essential for ML. Here are some resources to help you learn: Khan Academy courses: Linear Algebra: https://www.khanacademy.org/math/linear-algebra Calculus: https://www.khanacademy.org/math/calculus-1 Probability and Statistics: https://www.khanacademy.org/math/statistics-probability 3Blue1Brown's YouTube series on Linear Algebra: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Data processing and manipulation: Familiarize yourself with popular Python libraries for data manipulation and analysis, such as NumPy, pandas, and matplotlib: NumPy: https://numpy.org/doc/stable/user/quickstart.html pandas: https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/index.html matplotlib: https://matplotlib.org/stable/tutorials/index.html Machine learning concepts: Learn about the basic concepts of ML, including supervised learning, unsupervised learning, and reinforcement learning. Some great resources include: Coursera's Machine Learning course by Andrew Ng: https://www.coursera.org/learn/machine-learning Google's Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course Fast.ai's Practical Deep Learning for Coders course: https://course.fast.ai/ Deep learning libraries: Get familiar with popular deep learning libraries such as TensorFlow and PyTorch: TensorFlow: https://www.tensorflow.org/tutorials PyTorch: https://pytorch.org/tutorials/ Specialize and work on projects: Choose an area of interest (such as natural language processing, computer vision, or reinforcement learning), and start working on projects to apply your skills. You can find datasets and project ideas from sources like: Kaggle: https://www.kaggle.com/ Papers With Code: https://paperswithcode.com/ Stay up-to-date and join the community: Follow ML blogs, podcasts, and conferences to stay current with the latest developments. Join ML communities and forums like r/MachineLearning on Reddit, AI Stack Exchange, or specialized Discord and Slack groups.
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FLOSSing for Lent 12/40 - Python
Real Python - A great place to learn python
What are some alternatives?
learnxinyminutes-docs - Code documentation written as code! How novel and totally my idea!
awesome-scalability - The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
missing-semester - The Missing Semester of Your CS Education 📚
codewars.com - Issue tracker for Codewars
developer-roadmap - Interactive roadmaps, guides and other educational content to help developers grow in their careers.
sdk - The Dart SDK, including the VM, dart2js, core libraries, and more.
low-level-design-primer - Dedicated Resources for the Low-Level System Design. Learn how to design and implement large-scale systems. Prep for the system design interview.
flexboxfroggy - A game for learning CSS flexbox 🐸
peps - Python Enhancement Proposals
futurecoder - 100% free and interactive Python course for beginners
awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.
W3Schools - W3Schools Full Offline Version