Machine learning reddit

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.

It is the single and the best Tutorial on Machine Learning offered by the IIT alumni and have minimum experience of 18 years in the IT sector. This course provides an in-depth introduction to Machine Learning, helps you understand statistical modeling and discusses best practices for applying Machine Learning. Sentdex. 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 ... 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. 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 ... 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 ... Sep 12, 2021 ... Deep learning is a subset of ML that use variants of Neural Network model. Other than deep network there are decision trees, linear regression, ...Check out Ace the Data Science Interview — it covers statistics, machine learning, and open-ended ML case study interview questions. The book focuses more on the foundations of the field + interview questions related to classical ML techniques, rather than something like reinforcement learning, because honestly, that's what 90% of Data Science & ML …

Machine Learning is mathematics first, and programming second. Machine Learning research is currently (and likely in future) dominated by Ph.D. graduates in Physics, Mathematics, Statistics, and Computer Science. Undergraduate studies in a quantitative discipline like mathematics, statistics, or physics will probably be the best place for you ...In today’s digital age, having a strong online presence is crucial for the success of any website. With millions of users and a vast variety of communities, Reddit has emerged as o...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...To enhance Reddit’s ML capabilities and improve speed and relevancy on our platform, we’ve acquired machine-learning platform, Spell. Spell is a SaaS-based AI platform that empowers technology teams to more easily run ML experiments at scale. With Spell’s technology and expertise, we’ll be able to move faster to integrate ML across our ...r/MachinesLearn: This is a subreddit for machine learning professionals. We share content on practical artificial intelligence: machine learning…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.When you don't understand a concept or don't remember something, stop it, take a book (or open YouTube) and learn about it. It will take time, but it's worth it. If you don't remember anything about linear algebra or calculus, open YouTube and find some video about it. After that, continue with Andrew ng.

I don't know which rankings you were looking at, but for machine learning research, Tuebingen is one of the best universities in Europe (or world-wide, for that matter). I can't say a lot about the quality of education, since I've not studied there myself. 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. 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 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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Alternatives to Reddit, Stumbleupon and Digg include sites like Slashdot, Delicious, Tumblr and 4chan, which provide access to user-generated content. These sites all offer their u...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.Hello everyone, I am about to start college as a computer science and math double major, and I want to eventually pursue a PhD in Machine Learning, but I am fairly new to the field and would like long term advice for a robust budget pc build that will be useful for my needs for atleast 4 years , and whether I should use multiple GPUs or a hybrid of a single gpu …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 ...

I would disagree with Python's library for Machine learning applications. Matlab has a very extensive statistical library with many machine learning algorithms readily available. With python you will probably be able to find many of them, but you will have to work for it. Try Hidden Markov models in Python or Random Forests or Auto regressive ...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.A robust machine learning engineering skill set is hard won, just like compilers, operating systems, or distributed systems skillsets. So while you (perhaps thankfully) don’t have to acquire a PHD, getting into ML engineering isn’t a walk in the park. Presented below is an inevitably incomplete, but still fleshed out list of resources for ...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.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 …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...Mar 7, 2016 ... ... deep learning celebrities : r/MachineLearning ... Remove r/MachineLearning filter and expand search to all of Reddit ... machine learning landmark.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.Machine learning projects have become increasingly popular in recent years, as businesses and individuals alike recognize the potential of this powerful technology. However, gettin...

r/learnmachinelearning. • 1 yr. ago. DeF_uIt. Is ML career worth it? Firstly I stuck with web backend development because of the huge pool of job openings and high payment. But then I'v got interested in machine learning (Deep learning, RL, CV actually all of that look attractive to me).

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.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 … 1)General Python programming. Usually leetcode type questions about implementing something in Python, or questions about Python's features. Also very helpful to know mundane stuff like pulling data from APIs, formatting strings, and so on. 2)General Machine Learning and statistics questions. These tended to be theoretical. 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 ...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.Mathematics for Machine Learning by Deisenroth. 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. I’ve read a lot of posts asking for recommendations for textbooks to learn the math behind machine learning so I figured I’d make a self-study guide that walks you through it all including the recommended subjects and corresponding textbooks. You should have more than enough mathematical maturity to work through ESL and the Deep Learning ... A person who is able to look at a business's data and needs, and can safely apply some relatively standard ML (including deep learning) to make things better and not worse, will be well compensated. Haskellol420 • 4 yr. ago. Machine Learning isn't a career (except research and other niche jobs).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).

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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 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. …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 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.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!Mar 4, 2023 ... The modelling part only takes up 20-30% of the job. Deep learning (apart from NLP) RL and CV are not as frequently used in industry. Most of the ...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 ...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.Symbolic reasoning consists of controlling specific kinds of discrete dynamic systems, and in that sense it isn’t any different from any other ML problem; you still need a state space embedding and algorithms for choosing actions. Although it’s a difficult area of research, it does not exist in opposition to deep learning.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...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 ...Jun 3, 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 ... If you are interested in learning Artificial Intelligence and Computer Science for FREE, you can checkout this list that we've made. You may not see some of the most popular courses that you may be familiar with (ex:IBM's) but those are free for like 7 days and than require payment. 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...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 find deeply …There are a lot of differences between MLOPs and the other types of infra/BE teams, as each of them are also pretty specialized. At the end of the day, I think it comes down to 1) who the team is designed to support/collaborate with and 2) what will they own. For 1), MLOps ppl will be interacting mostly with ML scientists/engineers, and so ...Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/nvidia A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more.I totally agree with you, I just wanted to point out that Siri is not even Apple’s main machine learning product and there is much more (e.g. lots of computer vision). Then I double checked the fact and found out about acquisiton of Siri, hence the edit.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 ... 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]