Reddit machine learning

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03-Oct-2020 ... During my last interview cycle, I did 27 machine learning and data science interviews at a bunch of companies (from Google to a ~8-person YC- ...It'll set you back a lot of money, but it's an investment in time and money, and in theory should return ten times as much. #39 in Best of Coursera: Reddsera has aggregated all Reddit submissions and comments that mention Coursera's "Machine Learning" specialization from University of Washington.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.2. irvcz. • 4 yr. ago. I like to say (is not completely true) that python is a general porpuse language with libraries for statistics while R is a statistical language with libraries for general porpuse. Said that, python is more popular, and therefore has more libraries. But something that I feel R surpasses pyton (in my experience) is the ... 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. 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. The Impact of Machine Learning on Economics. Machine Learning Methods Economists Should Know About. Machine Learning and Causal Inference for Policy Evaluation. I would note, though that economists use machine learning for different purposes than most data scientists. We're usually interested in causal inference and less so in predicting things ...Looking for ways to increase your business revenue this summer? Get a commercial shaved ice machine. Here are some of the best shaved ice machines. If you buy something through our...

machine learning fields are trying to establish best practices rn, and bio programs are having a reproducibility crisis, but there is work being done to try to clean up the worst examples. there's always a possibility of a winter for anything. after the dot com crash in the 2000s, tens of thousands of tech workers were laid off.Large Hydraulic Machines - Large hydraulic machines are capable of lifting and moving tremendous loads. Learn about large hydraulic machines and why tracks are used on excavators. ... r/learnmachinelearning: A subreddit dedicated to learning machine learning. Like the title said, I’m working on a research about Sparse Mixture of Experts and need to survey and choose a toolkit to build my research code base. Large Hydraulic Machines - Large hydraulic machines are capable of lifting and moving tremendous loads. Learn about large hydraulic machines and why tracks are used on excavators. ... Build a TensorFlow Image Classifier in 5 Min video. Deep Learning cheat-sheets covering Stanford's CS 230 Class cheat-sheet. cheat-sheets for AI, Neural Nets, ML, Deep Learning & Data Science cheat-sheet. Tensorflow-Cookbook cheat-sheet. Deep Learning Papers Reading Roadmap list ★. Papers with Code list ★. Redirecting to /r/MachineLearning/new/.

This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic.WikiBox. • • Edited. If you use some library for AI and machine learning, chances are good that this library was written in C or C++ and that you use this library from some other language, like Python. So even if the top-level program is written in Python, lower levels libraries and drivers are very likely to be compiled and written in C or ...In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental …Although machine learning might not sound too complex, there is a shitton of theory behind it. Just keep yourself busy with learning something new every day or two and you should be golden. Reply reply 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.

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Talking to a friend that’s struggling with their mental health is tricky. You might be concerned about saying the wrong thing or pestering them with too many phone calls and texts....30-Dec-2022 ... Think of it like this - ML is mostly concerned with prediction, while statistics also cares about interpretability. As a result, most ML methods ... Machine Learning Hard Voting and Soft Voting. Ensemble Learning in the field of Machine Learning is using multiple Machine Learning models. and aggregating the predictions of each model to make the final prediction. Aggregating basically. means combining the predictions in some way to form the final prediction. Yes but it's very difficult. I did it because I was luckily assigned to the right team as an intern. Hato_UP • 5 mo. ago. In my experience, it is worth it. A lot of ML shops filter out candidates without advanced education, simply because there are already so many candidates WITH advanced education. If you want to just reduce the chances of ...I would argue that learning machine learning with ONLY python is kind of useless for practical senses like getting a job or making useful projects. Even if you could've done it somehow you really wouldn't know how it works and how to make further progress. ... Dude this sub Reddit is about learning not discuss politics Reply reply More replies.Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition) (Aurélien Géron) Approaching (Almost) Any Machine Learning Problem (Abhishek Thakur) Feel free to comment below and add new book recommendations. Honest opinion: Except Andriy Burkov (not-really ...

Hello. I am very interested in learning ML and AI. I did take a basics course still in the beginning of university, and I would like to deepen my knowledge on this topic, which I … A user shares a list of online courses for machine learning, deep learning, and machine learning in production. Other users comment and suggest additional resources, such as MIT's ML course on Edx and YouTube videos. I compiled a list of machine learning courses with video lectures. The list includes some introductory courses to cover all the basics of machine learning. More interesting might be the more advanced and graduate-level courses, that are typically harder to find. I will continue to update this list, as I find suitable material. To become a Machine Learning Engineer, one should follow a structured path that combines education, hands-on experience, and continuous learning. Begin by acquiring a strong foundation in mathematics, statistics, and computer science, as these are fundamental to understanding the underlying principles of machine learning.Abstract : Despite their tremendous success in many applications, large language models often fall short of consistent reasoning and planning in various (language, embodied, and social) scenarios, due to inherent limitations in their inference, learning, and modeling capabilities. In this position paper, we present a new perspective of machine ...Referenced Symbols. +0.35%. The Federal Trade Commission has launched an inquiry into Reddit’s licensing of user data to artificial-intelligence companies — just …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).When you're ready to tackle implementation of ML algorithms yourself, you should be able to do it from a pretty anemic guide. I implemented my recommender system from a single equation. The water simulation I did in college was the same, come to think of it. If an algorithm seems impenetrable, and you need a line-by-line guide, maybe you need ...Reddit disclosed the Federal Trade Commission is looking into its sale, licensing or sharing of user-generated content with third parties to train artificial …Go to learnmachinelearning. r/learnmachinelearning. A subreddit dedicated to learning machine learning. MembersOnline. •. Ishannaik. ADMIN MOD. A Clear roadmap to …

22-Jul-2022 ... r/MachineLearning Current search is within r/MachineLearning. Remove r/MachineLearning filter and expand search to all of Reddit. TRENDING ...

Of the mathematical background needed for Machine Learning, what should be order to study Linear Algebra, Statistics, Probability, and Multivariate Calculus. I have a basic undertsanding of these areas, but want to get into depth. Any resources, esp textbooks, would be welcome too. Linear Algebra, Multivariate Calculus, Probability, Statistics.Hello. I am very interested in learning ML and AI. I did take a basics course still in the beginning of university, and I would like to deepen my knowledge on this topic, which I …Open-Source. 9 1. r/machinelearningnews: We are a community of machine learning enthusiasts/researchers/journalists/writers who share interesting news and articles….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 current ML projects and future efforts in the space. Based on a talk given by Anand Mariappan, the Senior Director of ML at Reddit, at ODSC West 2018, we’ll cover ... The most often recommended textbooks on general Machine Learning are (in no particular order): Hasti/Tibshirani/Friedman's Elements of Statistical Learning FREE; Barber's Bayesian Reasoning and Machine Learning FREE; Murphy's Machine Learning: a Probabilistic Perspective; MacKay's Information Theory, Inference and Learning Algorithms FREE Linear regression is a type of machine learning. It's probably the most simplistic kind, but that works when the dataset is linear and/or you want to analyze basic feature importance. There are hundreds of various other ML algorithms: Neural networks allow us to work with pictures and images, creating models that can predict/identify objects and situations. When possible, these guides have stuck closely to the views of established Machine Learning engineers and researchers. In other places, the author has forwards their view of things. Please feel free to submit feedback and improvements for these any parts of these guides. 1. Getting Into ML: High Schoolers Guide. 2. ML is applied stats. ML has a stronger focus on prediction and not so much about describing data distributions and metrics. Seems to contradict itself by showing a diagram where statistics and machine learning do not intersect - and then going on the show the use of statistics in machine learning.

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Sort by: cthorrez. • 6 yr. ago. There is a huge oversaturation of people who took a Coursera or edex class with no experience or theoretical knowledge applying to machine learning engineering positions. There is an undersaturation of people with master's and PhDs in machine learning who can actually perform good research and development in ... ADMIN MOD. [D] A Super Harsh Guide to Machine Learning. Discussion. First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7-8. If you don't understand it, keep reading it until you do. You can read the rest of the book if you want. You probably should, but I'll assume you know all of it. Hands-on ML with scikit learn, keras and TF, 2nd edition (it is substantially better than the previous edition) by Géron. The hundred page ML Book by Burkov. Introduction to ML 4th edition by Alpaydin. These for me are the best books to start with, then you move to more complex and funny books like Murphy or Bishop.If you only plan on using other people's fully developed code, you probably don't need to learn the math. But then you really don't know machine learning then, you just understand how to use software libraries and abstractions on top of machine learning algorithms. Although I personally enjoy learning to understand the mathematics behind ML, I ...Machine Learning Hard Voting and Soft Voting. Ensemble Learning in the field of Machine Learning is using multiple Machine Learning models. and aggregating the predictions of each model to make the final prediction. Aggregating basically. means combining the predictions in some way to form the final prediction.As a part of the Reddit Machine Learning Engineer interview, you will need to go through multiple interview rounds: 1. Phone screening - The phone screening is a quick call to discuss your background and ML experience.. 2. Technical Round- You will be asked to build a machine learning model based on data provided by the interviewer.This round is …That is actually the most recommended starter course for ML. It touches a fair spectrum of ML algorithms, includes the prerequisite math/stats materials and has some useful practical tips and insights. Some people dislike the choice of matlab/octave for the programming exercises (for which you need only the very basics of the language), but if ...Other than than those two, the others that helped me were Applied Predictive Modeling (Kuhn and Johnson), Introduction to Machine Learning (Alpaydin), Machine Learning Refined (Watt et al.). And then of course Mathematics for Machine Learning (Deissenroth et al.). Bayesian Reasoning and Machine Learning is also great (Barber) but more … I use machine learning for my long options portfolio, I use classifiers to establish potential group of candidates then predictors for placing the orders, stop loss is a simple ATR band, wider for calls, narrower for puts, Daily data set with price derivatives and fundamental analysis data to better time entry. Define the Problem. As you may have guessed I was tasked with using machine learning to do what you just tried to do above! In other words, creating a classification model that can distinguish which of two subreddits a post belongs to. The assumption for this problem is that a disgruntled, Reddit back-end developer went into … ….

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 ... r/learnmachinelearning: A subreddit dedicated to learning machine learning. Like the title said, I’m working on a research about Sparse Mixture of Experts and need to survey and choose a toolkit to build my research code base. The Impact of Machine Learning on Economics. Machine Learning Methods Economists Should Know About. Machine Learning and Causal Inference for Policy Evaluation. I would note, though that economists use machine learning for different purposes than most data scientists. We're usually interested in causal inference and less so in predicting things ...Machine learning resources for beginners. Hi all, here's a list of free resources I made for my data science studies (I'm just starting out). There are courses, tutorials, and videos that I think are pretty decent and are all free. While the main focus is on data science, there are quite a bit of machine learning resources as well so I wanted ...Go to learnmachinelearning. r/learnmachinelearning. A subreddit dedicated to learning machine learning. MembersOnline. •. Ishannaik. ADMIN MOD. A Clear roadmap to …Best Machine Learning Courses for Beginners, Advanced in 2024 - : r/learnmachinelearning. r/learnmachinelearning. r/learnmachinelearning. • 3 min. ago. I compiled a list of machine learning courses with video lectures. The list includes some introductory courses to cover all the basics of machine learning. More interesting might be the more advanced and graduate-level courses, that are typically harder to find. I will continue to update this list, as I find suitable material. For now, this is the proposed process: Each week a new voting thread is set up. The proposal with the most upvotes at the end of the week (say Friday or Saturday) will be the upcoming week's paper. A discussion thread for it will be created in r/mlpapers , which will then be crossposted to r/MachineLearning. Reddit machine learning, [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]