Machine learning reddit

Try to do a couple of machine learning projects. Reason being, for backend development, you may not need a project for internship or even a job, but, for machine learning, it is highly recommended to have some projects in your portfolio which can make you stand out among there, be it an internship or a job or a gig. All the best.

Knowledge of "hard" mathematics that can underpin machine learning (e.g. advanced linear algebra, geometry focused on graph theory, symbolic/numeric/automatic diff) 1 == Good, you won't find it in any books or courses, or if you do find it in some books (e.g. fastai books or courses) then those are hard to find, incomplete and usually despised ...Mar 7, 2016 ... ... deep learning celebrities : r/MachineLearning ... Remove r/MachineLearning filter and expand search to all of Reddit ... machine learning landmark.This brought me to the AMD MI25, and for $100 USD it was surprising what amount of horsepower, and vRAM you could get for the price. Hopefully my write up will help someone in the machine learning community. Let me know if you have any questions or need any help with a GPU compute setup. I'd be happy to assist! A Roadmap for Beginners in Machine Learning with many valuable resources for any ML workers or enthusiasts + how to stay up-to-date with news This guide is intended for anyone having zero or a small background in programming, maths, and machine learning. There is no specific order to follow, but a classic path would be from top to bottom. If you are looking to start your own embroidery business or simply want to pursue your passion for embroidery at home, purchasing a used embroidery machine can be a cost-effective ...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!

Reddit is a popular social media platform that has gained immense popularity over the years. With millions of active users, it is an excellent platform for promoting your website a... Related Machine learning Computer science Information & communications technology Technology forward back r/learnpython Subreddit for posting questions and asking for general advice about your python code. View community ranking In the Top 1% of largest communities on Reddit [D] Advanced resources for ML theory/math. So I have been working in ML for the past 3 years as a researcher and now PhD candidate, and though I have an understanding of intermediate level of the math behind most algorithms. ... There seems to be a lot of overlap between the ...Mar 7, 2016 ... ... deep learning celebrities : r/MachineLearning ... Remove r/MachineLearning filter and expand search to all of Reddit ... machine learning landmark.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 ...

So naturally, I don't really know where to begin this journey. I've researched for resources and roadmaps to learn machine learning and created my own basic roadmap just to get started. Math - 107 hours. Single-Variable Calculus - MIT ~ 29 hours. Multi-Variable Calculus - MIT ~ 29 hours.I can't give you the ulitmate roadmap for your introduction in Data Science field, but I can give you a good guide on how to start and make things easier. Firstly before even touching Machine Learning courses, you need to have a solid understanding of Python libraries like Numpy, Pandas, Matplotlib, Statistics (so as to not mess up ML later).Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder. Hi all! Here is a new self-supervised machine learning approach that captures word meaning with concise logical expressions. The logical expressions consist of contextual words like “black,” “cup,” and “hot” to ... Related Machine learning Computer science Information & communications technology Technology forward back r/OMSA The Subreddit for the Georgia Tech Online Master's in Analytics (OMSA) program caters for aspiring applicants and those taking the edX MicroMasters programme. 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.

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No. R is maybe a bit better for explorations, but its productization is rly bad. Python has both. It can be used both for explorations and for developing final product. And its getting even better at both those tasks. Programming in Python since ~2003, using R for machine learning since ~2012. I wrote a blog post about why using docker for your ML workspace makes sense and included a step-by-step guide on how to do it. It was inspired by a tweet by Jeremy Howard and another blog post introducing docker for ML. I think docker is great for a few reasons, namely the fact that it standardizes your environment, makes it easy to ...PhDs are indeed quite competitive, as others have described. On the brighter side though, many universities have started to offer masters programs in Data Science & ML (e.g. USF ), which typically have a higher intake (i.e. less competition) compared to PhD programs, and focus on practical application of Data Science & ML, rather than research. 1. 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 ...

The final capstone project in Coursera's Machine Learning and similar specializations is a worthwhile investment, IMHO. So, I probably wouldn't worry much about an individual course certificate, unless you plan to complete a series of courses and do the capstone project. 4.Bifrost Data Search is an initiative to aggregate, analyse and deliver the world's image datasets straight into the hands of AI developers. You can search from over 1000 listings paired with rich information and in-depth analyses. It’s 100% free and we’re always adding more datasets and features. This is just a beta release, and we’d love ...The second edition also covers Generative Learning to a deeper extent as well as productionalizing learning algorithms. If you're looking for an RL reference, Sutton and Barto is the gold standard. OpenAI gym/rllib/stablebaselines are …Here are our top picks of Reddit’s machine learning datasets. Best Reddit Datasets for Machine Learning. Cryptocurrency Reddit Comments Dataset: Containing …A Machine Learning project is an order of magnitude more difficult to deliver than a software engineering project. Model drift, ethical implications of dataset outliers, driving project decisions that are centered around mathematics, all of that is insanely difficult.I think that the new major breakthroughs will be in the cross-pollination between domains between ML and specific application domains. The general knowledge and techniques about ML is vastly increasing, however, for specific domains, such as healthcare or other high-stake applications, the ML adoption rate is far below other applications domains.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...569 votes, 81 comments. 387K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learningYeah I see. My question is more like, which book would be good for obtaining a solid understanding of the different ML techniques (including mathematical descriptions, algorithmic analysis, exercises with a solutions manual) that could pave the way for a more analytical and mathematical understanding of ML potentially far into the future (like in some parts of …A subreddit for weekly machine learning paper discussions. Started by the people from /r/MachineLearning If you want to get started with Machine Learning, try /r/LearnMachineLearningThis brought me to the AMD MI25, and for $100 USD it was surprising what amount of horsepower, and vRAM you could get for the price. Hopefully my write up will help someone in the machine learning community. Let me know if you have any questions or need any help with a GPU compute setup. I'd be happy to assist!To help you, I've compiled an up-to-date list of 20+ active machine learning and data science communities grouped by platform. 1. Reddit. Reddit is a powerhouse for many active forums dedicated to all areas across AI, machine learning, and data science. Here's a list: r/machinelearning (2M+ members) r/datascience (500K+ members)

Intel continues to snap up startups to build out its machine learning and AI operations. In the latest move, TechCrunch has learned that the chip giant has acquired Cnvrg.io, an Is...

So even if you go to industry after your PhD, you will be able to learn new technical material efficiently, which is a great skillset. Because yes, your dissertation topic you will probably never use in industry, but you have the ability to absorb new material without formal courses. 6. LegacyAngel • 3 yr. ago.A Machine Learning project is an order of magnitude more difficult to deliver than a software engineering project. Model drift, ethical implications of dataset outliers, driving project decisions that are centered around mathematics, all of that is insanely difficult.Mar 7, 2016 ... ... deep learning celebrities : r/MachineLearning ... Remove r/MachineLearning filter and expand search to all of Reddit ... machine learning landmark. The real learning starts when you begin to absorb someone else's concept then turn it into your own so you can work on your own projects. 4.5) [Optional] There are tons of specialized fields in ML, you should have enough foundations and intuitions to go in more specialized fields. eg computer vision, robotics etc. How strong are the magnets in an MRI machine? Can they pull a watch of your arm or even more? Learn just how strong MRI magnets are on this page. Advertisement ­The biggest and mos...There's really a few different things you could learn with AWS. Machine Learning training using GPU instances. This will likely be the easiest to learn, and it essentially just means allocating a server with a GPU (usually something like a K80 or P100 for $1-3/hr, prorated to the minute), setting it up, and training on it.Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their …Tips for Learning AI: Start with the basics: Learn the necessary math, programming, and ML concepts. Work on projects: Apply your knowledge to real-world problems to solidify your understanding. Join a community: Engage with like-minded individuals to share ideas, resources, and support.The book Pattern Recognition and Machine Learning by Christopher Bishop, not free but one of the best starting point. The book Bayesian Reasoning and Machine Learning by David Barber. The book The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman.

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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 ... No. R is maybe a bit better for explorations, but its productization is rly bad. Python has both. It can be used both for explorations and for developing final product. And its getting even better at both those tasks. Programming in Python since ~2003, using R for machine learning since ~2012. 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 ... There are a few tricks you can do with conda to make life a bit simpler, here is my run-done: Use miniconda instead of anaconda. Use conda-forge channel instead of defaults for the latest packages. (My usual channel priority is pytorch > conda-forge > defaults ) Never install packages in base.Jul 17, 2021 ... r/MachineLearning Current search is within r/MachineLearning. Remove r/MachineLearning filter and expand search to all of Reddit. TRENDING ... These models are tools to improve your NLP workflow. So yes it’s still required to learn ML. Instead of using 100 different models for 100 different tasks, we now can use 1 model for 100 tasks. That’s what’s the hype’s all about. But it’s still far from achieving a state where it can create good models for some tasks. 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. Sort by: Add a Comment. Hopp5432. • 2 yr. ago. You try all of them and select the one with the best metrics depending on you’re use case. Example is when building a classification model I would try logistic regression, LDA/QDA, naive bayes, SVM, trees, XGB, Adaboost etc and then evaluate them on a metric like the AUC ROC. Afterwards I ...Reddit, often referred to as the “front page of the internet,” is a powerful platform that can provide marketers with a wealth of opportunities to connect with their target audienc...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 ...Machine learning algorithms are at the heart of many data-driven solutions. They enable computers to learn from data and make predictions or decisions without being explicitly prog... ….

In numerical analysis and computer science, a sparse matrix or sparse array is a matrix in which most of the elements are zero. By contrast, if most of the elements are nonzero, then the matrix is considered dense. The number of zero-valued elements divided by the total number of elements (e.g., m × n for an m × n matrix) is called the ...To keep a consistent supply of your frosty needs for your business, whether it is a bar or restaurant, you need a commercial ice machine. If you buy something through our links, we... Furthermore, it is a necessity when constructing models based on optimization techniques for machine learning problems (such as logistic regression for multi-class classification), which rely heavily on first principles in mathematics (often involving derivatives) but can provide good results through the explicit minimization of a function. Apparently Radeon cards work with Tensorflow and PyTorch. But if you don't use deep learning, you don't really need a good graphics card. If you just want to learn machine learning Radeon cards are fine for now, if you are serious about going advanced deep learning, should consider an NVIDIA card. ROCm library for Radeon cards is just about 1-2 ... Yeah I see. My question is more like, which book would be good for obtaining a solid understanding of the different ML techniques (including mathematical descriptions, algorithmic analysis, exercises with a solutions manual) that could pave the way for a more analytical and mathematical understanding of ML potentially far into the future (like in some parts of …Using Machine Learning to Solve Reddit’s “Rating-less ” Problem. Looking at the way in which Reddit’s marketplaces work led me to construct an algorithm to help solve the …Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/ITCareerQuestions This subreddit is designed to help anyone in or interested in the IT field to …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).Machine learning algorithms are at the heart of many data-driven solutions. They enable computers to learn from data and make predictions or decisions without being explicitly prog...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. 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]