Reddit machine learning.

Thank you. 262 votes, 23 comments. 387K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learning.Given the nature of machine learning tasks, I'm prioritizing not just raw processing power, but also substantial memory capacity to support the intensive data processing involved. …limiting NNs to a few special use cases is wrong. NNs may be one of the most versatile tools in machine learning. RNNs are great for time series for instance. there’s more than CNNs and image classifiers. Shoot.. I took a whole graduate level class last semester where we did nothing but build NNs to do everything from mazes to algorithmic ...03-Jun-2023 ... Not too late, but first start with the basics: Math & coding, then worry about learning ML. No point trying to get into the NFL without first ...Hello, learners of machine learning We are glad to announce a dedicated Discord server for r/LearnMachineLearning. You can join through https://discord.gg/G3rvFKF. Discord, a real-time communication tool, can complement our subreddit in several ways: Non-technical discussion involving machine learning

Reddit disclosed the Federal Trade Commission is looking into its sale, licensing or sharing of user-generated content with third parties to train artificial …Coursera Machine Learning by Andrew NG (Stanford) - it is more theoretical course, ±3month long. Without using Python, but Octave/Matlab . Python assignments for the machine learning class can be found in this github repo . Coursera Applied Machine Learning in Python (University of Michigan) - smaller course in terms of time, 4 weeks, …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)

Shopping for a new washing machine can be a complex task. With so many different types and models available, it can be difficult to know which one is right for you. To help make th...

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 ...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.The best way to get neural networks is to perceive them as: chain rule + dynamic programming. (1) Formulate a mathematical model that is differentiable wrt parameters that define its behaviour: f(x;W) where x is the inputs, and W is the parameters.Instead of wasting time gaming, watching tik Tok and Facebook (and Reddit). Focus on math and science. Get a hobby that interests you and enjoy your youth. Go to college and study some combination of computer science, statistics, physics, economics, engineering, or math. Good luck.Given the nature of machine learning tasks, I'm prioritizing not just raw processing power, but also substantial memory capacity to support the intensive data processing involved. I'd love to hear your thoughts, suggestions, and any improvements you might have in mind to optimize this setup for ML applications.

Specialization - 3 course series. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

Machine Learning 111 reddit 1. Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, …

Reddit announced Thursday that it would buy , a platform for running machine learning experiments, for an undisclosed amount. Spell was founded by former …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.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 …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.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 ...17-Sept-2021 ... That's one of the most accurate stuff I've read on Reddit. They do not have any hesitation to say I don't care what you do to your face ...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 ...

30-May-2023 ... Work is quite demanding so whatever time I get, I try to search for new stuff happening in Computer Vision/Deep Learning space. I usually rely ... 5. r/MachineLearning is a Subreddit for Data Scientists and ML Engineers with roughly 2.6M members. It uses a forum format for communication. The subreddit to disc. Coursera Machine Learning by Andrew NG (Stanford) - it is more theoretical course, ±3month long. Without using Python, but Octave/Matlab . Python assignments for the machine learning class can be found in this github repo . Coursera Applied Machine Learning in Python (University of Michigan) - smaller course in terms of time, 4 weeks, …Both programs are good for ML. It just depends more on what you want to do in ML. If you want to know more about the why & how models work then OMSA has more on that (math). If you like more of the computational and deployment side, then OMSCS is a better fit. soulyent • 3 mo. ago. •.Given the nature of machine learning tasks, I'm prioritizing not just raw processing power, but also substantial memory capacity to support the intensive data processing involved. …The machine learning model will score each comment as being a normal user, a bot, or a troll. Try it out for yourself at reddit-dashboard.herokuapp.com . To set your expectations, our system is designed as a proof of concept.

Best Machine Learning Courses for Beginners, Advanced in 2024 - : r/learnmachinelearning. r/learnmachinelearning. r/learnmachinelearning. • 3 min. ago.

Getting started in machine learning can be a daunting task, but there are many resources available to help you learn the fundamentals and start building your own projects. ... You can find communities on social media platforms like Twitter and Reddit, as well as on forums like GitHub and Kaggle. Some great communities to check out include: r ...For several reasons, I'm going to buy an Apple laptop. I realize that Apple laptops are possibly not the best laptop for machine learning, and doing ML on a laptop is generally sub-optimal. I'll probably run my most intense ML in the cloud, but I'd still like a machine that can some machine learning locally. My budget is about $3000.04-Mar-2023 ... There is a stupid amount you have to know, in addition to needing good communication and soft skills. You probably would take a pay cut. Doesn't ...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.You are much better off just using Google Colab or Kaggle notebooks. If you have to train models very often (like everyday) and 24GB from a RTX3090 or better a RTX4090 is enough, a dedicated computer is the most cost effective way in the long run. If you cant afford a RTX3090 and 12GB is enough, a 3060 with 12GB will do (for ML we usually …Given the nature of machine learning tasks, I'm prioritizing not just raw processing power, but also substantial memory capacity to support the intensive data processing involved. I'd love to hear your thoughts, suggestions, and any improvements you might have in mind to optimize this setup for ML applications.

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 ...

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 …

Anything to do with machine learning (especially deep learning) and Keras/TensorFlow. Users share projects, suggestions, tutorials, and other insights. Also, users ask and …I am considering applying to both LinkedIn and CapitalOne for a Machine Learning Engineer position and am curious if anyone with experience at either company can weigh in and share their experiences or insights. I have career experience doing ML and am confident I can get a position at either company.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. Learn the essential AI tools and packages. Knowing the right tools and packages is crucial to your success in AI. In particular, Python and R have emerged as the leading languages in the AI community due to their simplicity, flexibility, and the availability of robust libraries and frameworks. While you don’t need to learn both to succeed in AI. fifthsquad. For begginers: •Hands-On Machine Learning with Scikit Learn, Keras and Tensorflow (3rd Ed.) - (This was actually my favourite one, as it covers a lot of topics) •And Introduction to Statistical Learning with Applications in R (2nd Ed.) - (If you like R) •Deep Learning with Python (2nd Ed.) •Deep Learning - (A classic from ...Acer nitro 5 would be an obvious choice as it has a gpu and training deep learning models require gpu. Although m1 macbook has been given the tensorflow support it still has to go a long way. Windows + cuda is better for deep learning, but you having “begun your ML journey”, not sure how much of that you will do.Hello, learners of machine learning We are glad to announce a dedicated Discord server for r/LearnMachineLearning. You can join through https://discord.gg/G3rvFKF. Discord, a real-time communication tool, can complement our subreddit in several ways: Non-technical discussion involving machine learning 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. 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...If you work with metal or wood, chances are you have a use for a milling machine. These mechanical tools are used in metal-working and woodworking, and some machines can be quite h...

Buying a used sewing machine can be a money-saver compared to buying a new one, but consider making sure it doesn’t need a lot of repair work before you buy. Repair costs can eat u...Think about what your 'w' parameters are actually doing. You're taking the first column of features, multiplying it by the first w parameter. Then you're taking the second column of features, multiplying it by the second parameter and so on and then adding all that up together. Let's say that F=4.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 ...Instagram:https://instagram. the the secretpetrol injector cleanerhiyo drinkim lost 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. bachelor.in paradise 2023nutella pringles 03-Jun-2023 ... Not too late, but first start with the basics: Math & coding, then worry about learning ML. No point trying to get into the NFL without first ... 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. top whiskey r/learnmachinelearning: A subreddit dedicated to learning machine learning. Editing Guide and Rules. Mark a beginner-friendly resources by formatting it with bold.A beginner-friendly resource should specifically be designed for beginners, or its materials should be blatantly easy enough for beginners to pick up 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. 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 …