MHB How to deal with learning plateau in CS?

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SUMMARY

The discussion centers on overcoming a learning plateau in computer science, particularly for individuals working in machine learning. Key strategies identified include enrolling in quality courses, engaging in deliberate practice, and applying theoretical knowledge through practical coding projects. The participant emphasizes the importance of solving real-world problems to enhance understanding, especially in areas like Python programming and machine learning concepts. Acknowledging the need to revisit foundational mathematics and computer science subjects is also highlighted as a common experience among learners.

PREREQUISITES
  • Understanding of Python programming fundamentals
  • Familiarity with machine learning concepts such as supervised and unsupervised learning
  • Basic knowledge of object-oriented programming principles
  • Mathematical foundations relevant to computer science and artificial intelligence
NEXT STEPS
  • Explore advanced Python programming techniques and projects
  • Learn about practical applications of machine learning through hands-on projects
  • Study effective note-taking strategies for complex subjects
  • Investigate resources for mastering artificial intelligence concepts
USEFUL FOR

Individuals in the tech industry, particularly those in machine learning and software development, as well as students seeking to deepen their understanding of computer science and artificial intelligence.

shivajikobardan
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I have reached a place where I can no more learn new concepts. I feel totally stuck.How do I deal with learning plateau? How do I overcome this plateau. Few things that come in my mind-:

1) Follow a good course from a good teacher.

2) Read it aloud.

3) Practice the same thing multiple things to get insights. deliberate practice

What else can I do to overcome this learning plateau? https://drive.google.com/file/d/1fpksOvK5bLVC5C0njB-fNPgzOwj4GjqW/view These are the types of questions I need to deal with and where I am facing this learning plateau..
 
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I work in the tech industry for a machine learning software company. I also have a theoretical and not so practical interest in coding concepts and topics. In my path to learning Python really well over the last few years I've discovered that trying to solve actual problems is better for my learning than abstract hypotheticals. If I am learning about classes for example, I can cover the basics with some videos and tutorials, but unless I start coding object-oriented projects I won't care of have use for these concepts. Same thing for machine learning. I can answer all the questions in the world about unsupervised vs. supervised learning, defining NLP, etc. but if I don't use them I will not really appreciate or care about the nuances.

What do you want to do in CS?
 
Jameson said:
I work in the tech industry for a machine learning software company. I also have a theoretical and not so practical interest in coding concepts and topics. In my path to learning Python really well over the last few years I've discovered that trying to solve actual problems is better for my learning than abstract hypotheticals. If I am learning about classes for example, I can cover the basics with some videos and tutorials, but unless I start coding object-oriented projects I won't care of have use for these concepts. Same thing for machine learning. I can answer all the questions in the world about unsupervised vs. supervised learning, defining NLP, etc. but if I don't use them I will not really appreciate or care about the nuances.

What do you want to do in CS?
Currently my goal is to learn artificial intelligence and excel in theory. So that I am ready for advanced concepts. I have math foundations as I am engineering student and studied tons of mathematics. And was really good at it. But it feels like I need to re read all these math and cs subjects as I didn't make proper notes for this subject. Will take me full 1-2 year to do all this.. What do you think about this? Is this normal? Did you guys had to reread your college subjects again after graduation...I wish I made proper notes :(
 
We have many threads on AI, which are mostly AI/LLM, e.g,. ChatGPT, Claude, etc. It is important to draw a distinction between AI/LLM and AI/ML/DL, where ML - Machine Learning and DL = Deep Learning. AI is a broad technology; the AI/ML/DL is being developed to handle large data sets, and even seemingly disparate datasets to rapidly evaluated the data and determine the quantitative relationships in order to understand what those relationships (about the variaboles) mean. At the Harvard &...

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