Hello everyone! this is my first post on this community so i am really sorry if i did something wrong :) Today i just finished Phyton Fundamentals from kaggle that the Classroom recommended and actually, before this i have already done some Phyton course from learnphyton.org. But even after i finish both of this Phyton course i still feels like there is something that's not right in me. Everytime i do an exercise that the course provide if the exercise is too hard i ended up just going for the hint or asking ChatGPT for help or even worse just going straight into the solution. Which make me feels like i haven't really mastered the basics of Phyton. Well, that is my problem right now, i planned to just go forward and continuing the Classroom right now but if any you have insights/tips/recommendations to be shared with me, it will be really helpful and appreciated. Thanks for reading this post.

Open Source Society University has curated two free undergraduate level curriculums in both Data Science and Computer Science. These are free courses that you can follow with an LLM as your tutor to get a real college level education in the fields. Doing them, plus a few things useful or interesting on your own would surly lead to your being employable. High tech has always cared more about what you can do, not whether you had a degree, or where you got your education. The only cost is your time and effort. It's wild that technology has made getting a real college level education in a field cost you only time and effort. And, this is only one part of the beginning of how AI changes our world. If you are coming in with no background in these fields, and serious about becoming a professional, using these courses, and starting your own projects part way through, will probably start opening doors for you. And your tutor will never get tired of you asking questions, although they may rate limit you if you don't pay for their usage. LOL! An incredible opportunity!

Hey fellow data scientists! As we all know, a strong foundation in math and statistics is crucial for our work. I've been brushing up on my stats skills and recently started reviewing discrete math concepts. Today, I stumbled upon an excellent YouTube playlist that I think many of you might find valuable. The playlist covers discrete mathematics and is based on three books. I've included the links below: YouTube: link to the playlist Books: Discrete Mathematics and Its Applications Discrete Mathematics with Applications.pdf) Schaum's Outline of Discrete Mathematics I'm excited to dive deeper into these topics and thought I'd share this resource with the community. If you've already explored these books or have any favorite resources for discrete math, please share your thoughts and recommendations! Let's continue to learn and grow together! Happy learning everyone!

I strongly recommend the courses from DeepLearning.AI, I find them very useful and easy to follow 😄https://www.deeplearning.ai/courses/

Meta released Llama 3.2, with four models. Their smallest two are meant to run ON DEVICE, meaning your phone or your regular computer. LLM inference costs are driving towards zero, just power that you have on a normal device. More impressive than just the models are their 25 release partners, including Groq and AWS Bedrock, making these new models instantly available to anyone. My personal use, on PyroPrompts, shows Llama 3.2 1b to be super fast, over 2k tokens/second. Fast enough for realtime conversation (if you have text-to-voice). Fast enough for split-second decision making. The quality of the 1b mode is so-so, wasn’t blown away with its knowledge or ability to make up a story to convey a lesson. More testing is needed! I really like OpenAI’s Structured Outputs, would like similar support via Llama models, though it is achievable with Outlines by .txt. See the full announcement here: https://x.com/aiatmeta/status/1838993953502515702?s=46&t=k8fvndlsSKSnxvjP1AN1OA Have you tried Llama 3.2? What’s your experience with it?