Machine learning reddit

This budget will be used to run experiments of a few hours, experiments of one or more days will use the supercomputer. GPU clouds I found: Lambda. Linode. Paperspace. RunPod. Obviously there are big tech clouds (AWS, Google Cloud and Azure), but from what I've seen these other GPU Clouds are usually cheaper and less difficult to use. You who ...

Hello guys, I am new to reddit and to machine learning as well. Just yesterday I finished a Hackathon where me and my team made an image recognition AI using MobileNetV2. I don't …A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Here, you can feel free to ask any question regarding machine learning.Here are our top picks of Reddit’s machine learning datasets. Best Reddit Datasets for Machine Learning. Cryptocurrency Reddit Comments Dataset: Containing …Redirecting to /r/MachineLearning/new/.Hello guys, I am new to reddit and to machine learning as well. Just yesterday I finished a Hackathon where me and my team made an image recognition AI using MobileNetV2. I don't … coursera – machine learning (first three weeks) 100 page ML book. From now on, three areas of focus will be given for each level: Mathematics, Concrete ML knowledge, and Programming. Level 2 – Competent Developer. Have basic intuition about the math relevant for ML. Machine learning is much broader than this one approach. The crucial distinguishing feature of ML from data science is that machine learning's focus is on methods of learning from data. Unlike data models where the relationships of elements is pre-defined by programmers or architects, machine learning algorithms discover the patterns in the ...

After some digging, I narrowed it down to these two candidates: Linear Algebra and Optimization for Machine Learning: A Textbook by Charu C. Aggarwal. Introduction to Linear Algebra by Gilbert Strang. Would very much appreciate to hear your experience with either of them! EDIT: Wow, thank you guys!I spent a summer as a Data Scientist intern and now work as ML Engineer. If you enjoy coding more, do ML Engineer. ML Engineer is just a specialized Software Engineer. If you ever seen the role "Software Engineer - Machine Learning" that's pretty much interchangeable with ML Engineer. Most ML Engineers I've met come from having Software ...Machine learning is one field within the broader category of artificial intelligence. Machine learning involves processing a lot of data and finding patterns. Artificial Intelligence also includes purely algorithmic solutions. One of the earlier ones you learn in computer science is called min-max, which was commonly used in 2 player games like ...Here are some steps you can take to become a Machine Learning Engineer: Gain a Strong Foundation in Computer Science, Mathematics, and Statistics: A solid foundation in computer science, mathematics, and statistics is essential for becoming a Machine Learning Engineer. You can obtain this foundation through formal education, such as a degree in ...Hello, I'm a prospective Triton looking at what UC San Diego offers. I originally planned on a computer science major, but I was rejected from the department and ultimately chose this major (and looking into it more, this was something I was originally interested in (machine learning and artificial intelligence to create fully autonomous machines).If you are fine with spending 1-2 years grinding Leetcode for SDE in a super expensive MS ML/AI/DS program, fine. (fyi: interned at top comp and startups 3 times before masters, top gpa, applied for 300+ internships (a mix of MLE/SDE/DS), heard back from like 10, interviewed at 3, rescinded offer from 1, rejected from 1, accepted from 1 but not ...So please kindly ignore if there is anything not up to code (if there is any code). I just passed my AWS Machine Learning Specialty this morning (March 5th, 2022). While my memory is still fresh, I would like to provide some detailed suggestions for my fellow exam takers. Disclaimer 1: By no means I am encouraging anyone to prepare for the exam ...

Now my job is building machine learning models for huge datasets. I’m the old person that the newer engineers come to if they can’t figure something out. I can’t imagine that proofs would ever be an everyday thing in most machine learning programs. I honestly can’t remember the last time I did one. However I use math all the time.Here is the list of books that I gathered to add: The Elements of Statistical Learning. Second Edition. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Reinforcement learning, An Introduction. Second Edition. Richard S. Sutton and Andrew G. … Hopefully a masters program will give you some inkling as well. Master's or Ph.D. degrees sound great only if you wanna do in-depth studies. If you really want to learn more, then you should go for it, but remember it is time-consuming. So, rather than, I would suggest you also look for post-graduate courses. Here’s how to get started with machine learning algorithms: Step 1: Discover the different types of machine learning algorithms. A Tour of Machine Learning Algorithms. Step 2: Discover the foundations of machine learning algorithms. How Machine Learning Algorithms Work. Parametric and Nonparametric Algorithms. The common saying is "working with AI means spending 80% of your time working with data." Currently, working with AI means two things: either you do research (and you have to be somewhat exceptional for that), or you work in the "real world", which means you spend most of your time working with data. This is the impression I have gotten, and I ...

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Aug 29, 2022 ... [D] What are some dead ideas in machine learning or machine learning textbooks? · Initialize N instances of (the same) neural network. each ...Jul 17, 2021 ... r/MachineLearning Current search is within r/MachineLearning. Remove r/MachineLearning filter and expand search to all of Reddit. TRENDING ...Hi there, Deep learning is taking over a lot of other machine learning algorithms in industry. I was curious in what applications do other algorithms still outperform deep learning. And what algorithms are they?. I am mostly curious on this over in the industry world. If you could provide in the comments 1. The algorithm 2. The application and 3.Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back. r/cosplay. r/cosplay /r/cosplay: is a community where Cosplayers of all ages, and talent levels can post their work. Rules are strictly enforced , no NSFW, advertising, or pay sites of any kind ... We evaluate the Data Interpreter on various data science and real-world tasks. Compared to open-source baselines, it demonstrated superior performance, exhibiting significant improvements in machine learning tasks, increasing from 0.86 to 0.95. Additionally, it showed a 26% increase in the MATH dataset and a remarkable 112% improvement in open ...

Here is the list of books that I gathered to add: The Elements of Statistical Learning. Second Edition. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Reinforcement learning, An Introduction. Second Edition. Richard S. Sutton and Andrew G. …I'm interested in learning machine learning and data science and am thinking about trying to get a career as an engineer. I don't have a computer science degree though. ... CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. reddit's new API changes kill third party apps that offer accessibility ...Machine learning engineer here with no college degree! It is possible but the road is hard. Way harder than if you were to have the education appropriate for said position. I taught myself how to program 3 years ago after getting out of the Army. This was because I too was interested in machine learning and AI.Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back. r/buildapc. ... The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. --- If you have questions or ...Machine learning has become a hot topic in the world of technology, and for good reason. With its ability to analyze massive amounts of data and make predictions or decisions based...Jun 7, 2022 ... Reddit, Inc. © 2024. All rights reserved. r/learnmachinelearning. Join. Learn Machine Learning. A subreddit dedicated to learning machine ... We evaluate the Data Interpreter on various data science and real-world tasks. Compared to open-source baselines, it demonstrated superior performance, exhibiting significant improvements in machine learning tasks, increasing from 0.86 to 0.95. Additionally, it showed a 26% increase in the MATH dataset and a remarkable 112% improvement in open ... Reddit is a popular social media platform that boasts millions of active users. With its vast user base and diverse communities, it presents a unique opportunity for businesses to ...Machine learning, on the other hand, is applicable to datasets where the past is a good predictor of the future, like weather, electricity consumption, or foot traffic at a store. Always remember that all trading is fundamentally information arbitrage: gaining an advantage by leveraging data or insights that other market participants are missing.I'm interested in learning machine learning and data science and am thinking about trying to get a career as an engineer. I don't have a computer science degree though. ... CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. reddit's new API changes kill third party apps that offer accessibility ...Machine learning is in a state such that it is now practical usefully such that it might not be worth it to go to grad school for it. In the past, real world applications were few and grad school was the only way to "live the dream" as it were, but nowadays you can crack open weka/R, mangle data in hadoop and go to town without ever setting ...

Machine Learning is a very active field of research. The two most prominent conferences are without a doubt NIPS and ICML. Both sites contain the pdf-version of the papers accepted …

Other answers already mentioned there's an established ecosystem, but another important point is that Python can wrap libraries written in other faster programming languages. Most of numpy is written in C and Fortran, so this is why Python is good for ML even though it is slower than some other languages. 83. The post says "future." - Machine learning is about minimizing loss. In deep learning it propagates this through linear, lstm, and conv layers. - However, the differentiable programming ecosystem will move beyond these rigid confines to minimize loss in any function. In those cases, the language choice should not be driven by what language has the most advanced libraries. And my gut feeling is that people rush to Python when in fact for their context (and assuming they already know the Java ecosystem and not so much the Python one) the ROI won't be good. wildjokers. •.20th_Century_Flute • 7 yr. ago. "The training process of a machine learning algorithm is the optimization of the parameter's model so the desired output (which is the output we know from the data), and the actual output (which is the output predicted …You're gonna have a bad time with the nitty gritty without calc knowledge. If you want to study machine learning to actually use it and apply it without understanding 100% of WhatsApp going on, yes you definitely could. You just need some basic python skills and need to learn sklearn. Hi all, I following many of these channels of youtube, some of these are really great! I prefer Daniel Bourke, he is very motivating! I am building-up my own youtube channel for Data Science, Machine Learning, Deep Learning and related topics - technical videos and advices on the daily routine. The course experience for online students isn’t as polished as the top three recommendations. It has a 4.43-star weighted average rating over 7 reviews. Mining Massive Datasets (Stanford University): Machine learning with a focus on “big data.”. Introduces modern distributed file systems and MapReduce. Machine learning is one field within the broader category of artificial intelligence. Machine learning involves processing a lot of data and finding patterns. Artificial Intelligence also includes purely algorithmic solutions. One of the earlier ones you learn in computer science is called min-max, which was commonly used in 2 player games like ...

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Unlike Twitter or LinkedIn, Reddit seems to have a steeper learning curve for new users, especially for those users who fall outside of the Millennial and Gen-Z cohorts. That’s to ...Without further ado, here are my picks for the best machine learning online courses. 1. Machine Learning (Stanford University) Prof. Andrew Ng, instructor of the course. … ADMIN MOD. [D] ICLR 2024 decisions are coming out today. Discussion. We will know the results very soon in upcoming hours. Feel free to advertise your accepted and rant about your rejected ones. Edit 2: AM in Europe right now and still no news. Technically the AOE timezone is not crossing Jan 16th yet so in PCs we trust guys (although I ... The completely new version of Fast.ai 's super popular Practical Deep Learning for Coders course was just put online today. This is the course I recommend the most to people wanting to learn how to create real deep learning models. They've apparently re-written the whole course from the ground up. This is great. r/MachineLearning. This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. [R] Towards understanding deep learning with the natural clustering prior (PhD thesis) r/math. This subreddit is for discussion of mathematics. The common saying is "working with AI means spending 80% of your time working with data." Currently, working with AI means two things: either you do research (and you have to be somewhat exceptional for that), or you work in the "real world", which means you spend most of your time working with data. This is the impression I have gotten, and I ... On Reddit. 2.6M Members. Community Topics. View details for Data Science. Data Science. 26 communities for Data Scientists. View details for Machine Learning. Machine Learning. ... The post says "future." - Machine learning is about minimizing loss. In deep learning it propagates this through linear, lstm, and conv layers. - However, the differentiable programming ecosystem will move beyond these rigid confines to minimize loss in any function. ….

Without further ado, here are my picks for the best machine learning online courses. 1. Machine Learning (Stanford University) Prof. Andrew Ng, instructor of the course. My first pick for best machine learning online course is the aptly named Machine Learning, offered by Stanford University on Coursera. Hopefully a masters program will give you some inkling as well. Master's or Ph.D. degrees sound great only if you wanna do in-depth studies. If you really want to learn more, then you should go for it, but remember it is time-consuming. So, rather than, I would suggest you also look for post-graduate courses. 20th_Century_Flute • 7 yr. ago. "The training process of a machine learning algorithm is the optimization of the parameter's model so the desired output (which is the output we know from the data), and the actual output (which is the output predicted …Unlike Twitter or LinkedIn, Reddit seems to have a steeper learning curve for new users, especially for those users who fall outside of the Millennial and Gen-Z cohorts. That’s to ... A place for beginners to ask stupid questions and for experts to help them! /r/Machine learning is a great subreddit, but it is for interesting articles and news related to machine learning. Here, you can feel free to ask any question regarding machine learning. 5. Open Source Libraries: Familiarize yourself with popular libraries like TensorFlow and PyTorch for deep learning, scikit-learn for machine learning, and OpenCV for computer vision. 6. Stay Updated: Follow AI and machine learning blogs, podcasts, and conferences to stay up-to-date with the latest advancements. 7.Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back. r/buildapc. ... The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. --- If you have questions or ...Redirecting to /r/MachineLearning/new/.If you’re itching to learn quilting, it helps to know the specialty supplies and tools that make the craft easier. One major tool, a quilting machine, is a helpful investment if yo... r/machinelearningmemes. End-to-End MLOps platforms such as Kubeflow, MLflow, and SageMaker streamline machine learning workflows, from data preparation to model deployment. These platforms include components such as source control, test and build services, deployment services, model registry, feature store, ML metadata store, and ML pipeline ... Machine learning reddit, [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1]