Reddit machine learning

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.

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...Since, I am a beginner, need help from students of machine learning. Please suggest some great awesome resources (course, book, blogs, etc) which will help me to go from basic to advanced covering each and every topic or concept. ... Stumbled on this a while ago on Reddit and really liked the way it is structured and how it gives a scale of the ...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 ...etc. To summarize, as much linear algebra as possible, statistics, probability theory, basic optimization, and basic multivariable calculus. More advanced ML will require more advanced math, but you can worry about that when you get there. moombai • 5 yr. ago.Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. “In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done,” said MIT Sloan professor.

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 ...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 ...Thank you. 262 votes, 23 comments. 387K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learning.I want to learn machine learning just to make some AIs to play video games for me, improve macros, or just use it to mess around and make hobby projects like programs that search the web for me. I just finished learning multivariable calculus and portions of linear algebra and probability theory, but I do not enjoy the math so much.r/MachineLearning is a Subreddit for Data Scientists and ML Engineers with roughly 2.6M members. It uses a forum format for communication. In their own words. The subreddit to … 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

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 ...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....Best Machine Learning Courses for Beginners, Advanced in 2023 - : r/learnmachinelearning. r/learnmachinelearning • 5 min. ago. by Lakshmireddys. View community ranking In the Top 1% of largest communities on Reddit.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 ...Machine learning has its origins in artificial intelligence and tends to emphasize AI applications more. For example, although both data mining and machine learning work on text data, sentiment analysis is a bit more common in data mining and machine translation applications are more common in machine learning.“Python Machine Learning” by Sebastian Raschka and “Python for Data Analysis” by Wes McKinney are good introductions to lots of libraries in Python that will make your life easier when doing ML. So thats for the hands-on part. For theory, “Machine Learning” by …

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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. I know the trivial stuff of mlops life cycle and tools, but I'm still not really good in software engineering practices and the "engineering" part of machine learning. The thing is, I think that mlops, deep learning and GenAI evolves really fast, and most tools become deprecated quickly (at least I feel it) There are many good courses on machine learning available online. Some of the most popular ones include: Skillpro's Machine Learning course by by Juan Galvan: skillpro.io. Coursera's Machine Learning course by Andrew Ng: coursera.org. Fast.ai's Practical Deep Learning for Coders course: course.fast.ai. Use machine learning (online logistic regression) to approximate the metric because it is expensive to compute. Adjust the heurstic to maximize that metric, which in turn makes their algorithm faster. They got 2nd place in one of the SAT2017 competitions, but still, pretty sweet, paper was accepted to the conference. 2.18-Sept-2022 ... Remove r/MachineLearning filter and expand search to all of Reddit ... r/MachineLearning icon. Go to MachineLearning ... machine learning projects?

Because all of those things you mentioned are, well, machine learning. If what I'm assuming is true, then I'd suggest that you start looking into tools to automate your process and making a pipeline. That's what I've been doing and it's helped me get familiar with things like Kubernetes, KubeFlow, Airflow, etc. Asleep-Dress-3578.Advice regarding math foundations for deep learning. Hello guys! I've been wanting to find my feet in machine learning - specially Deep learning - for quite a while and I feel I’m ready to take the plunge. Some background, I’ve a tradicional CS background (BS and MS in Computer Science) and, although I had to go through all the usual math ... ML in Windows, Bing, Visual Studio etc are made with ML.NET. Reply reply. PrototypeV5. •. Note: Not having all the libraries in C# is an opportunity to create them (which allows you a hands-on opportunity to understand the algorithms). Reply reply. Individual-Trip-1447. •. Yes, C# is suitable for AI (Artificial Intelligence). Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. …24 GB memory, priced at $1599 . RTX 4090's Training throughput and Training throughput/$ are significantly higher than RTX 3090 across the deep learning models we tested, including use cases in vision, language, speech, and recommendation system. RTX 4090's Training throughput/Watt is close to RTX 3090, despite its high 450W power consumption.Welcome to r/machine_learning! Here you can ask questions and learn about machine learning! Please take the poll to help us with some private stuff! Poll question: How likely are you to rate this to a friend or someone you know? 1 vote. 1. … 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. 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 …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.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.

I am not sure which degree is best for getting into machine learning the obvious choice seems to be computer science but I have seen people say that maths, statistics or data …

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.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 ... 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. 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.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.Start by writing, “machine learning” in the middle, and break off to its topics: unsupervised, supervised, reinforced, causal etc. then from those break them into topics: clustering, linear regression etc. breaking things up in this way will go from a larger topic down to the individual tasks and actions that are doable:The better you are at math, the more intuitive you will find working with machine learning models. If you suck at math, you can still use models and functions that other people have built, but will struggle to build and maintain your own. To be competitive in the job market, you need to be really quite good at math.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 ...Instead, you combine best practices to create an algorithm effectively. Then you create a production ready solution (as a micro-service or on device) and make sure that it's performing as expected. Including monitoring, retraining, and other types of maintenance. 6.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 …

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What should I do. Where should I start. I know a good amount of python and js. Currently in 189, and I agree. It's a good baseline for if you're entirely lost and need some reinforcement/starter of where to develop strong ML skills, but as for learning the actual skills lmao good luck learning all that on your own. 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 ★.Welcome to r/machine_learning! Here you can ask questions and learn about machine learning! Please take the poll to help us with some private stuff! Poll question: How likely are you to rate this to a friend or someone you know? 1 vote. 1. …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...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, 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 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 know the trivial stuff of mlops life cycle and tools, but I'm still not really good in software engineering practices and the "engineering" part of machine learning. The thing is, I think that mlops, deep learning and GenAI evolves really fast, and most tools become deprecated quickly (at least I feel it)07-Jun-2022 ... But then I stumble on a reddit post that links 75 different github repos that have already implemented it. So the thought occurs to me, am I ... ….

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 ...I created a way to learn machine learning through Jupyter. Hey all, I’ve been working on a new way to help people practice machine learning concepts. Since most professionals in data science use Jupyter notebooks, I thought it’d be really cool for people to learn through interactive Jupyter notebooks as well. Here I’ve written an exercise ...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 ... 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 ... Welcome to r/machine_learning! Here you can ask questions and learn about machine learning! Please take the poll to help us with some private stuff! Poll question: How likely are you to rate this to a friend or someone you know? 1 vote. 1. …05-Jan-2024 ... What is the best way to learn machine learning? · Learn the Prerequisites. · Learn ML Theory From A to Z. · Deep Dive Into the Essential Topics...377K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learningIt'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. 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]