Reddit machine learning - 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.

 
7 Best Free Machine Learning Courses Online might know in 2022 -. Machine learning Computer science Information & communications technology Technology. 0 comments Best Top New Controversial Q&A. Add a Comment.. Micro saas

Next, grasp the basics of machine learning. Familiarize yourself with libraries like NumPy, pandas, and scikit-learn. Online courses like those on Coursera by Andrew Ng or edX by MIT can provide a structured learning path. ... Reddit's #1 spot for Pokémon GO™ discoveries and research. The Silph Road is a grassroots network of trainers whose ... r/machinelearningmemes. End-to-End MLOps platforms such as Kubeflow, MLflow, and SageMaker streamline machine learning workflows, from data preparation to model deployment. These platforms include components such as source control, test and build services, deployment services, model registry, feature store, ML metadata store, and ML pipeline ... 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 ...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.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.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.A website’s welcome message should describe what the website offers its visitors. For example, “Reddit’s stories are created by its users.” The welcome message can be either a stat...Yeah, the MacBook Pro (with me) is really great. The only concern that I have is that, as far as I know, the GPU doesn't support pytorch or other deep learning framework. Yes, it's true that training in the cloud is becoming the norm, but it is helpful to debug the model locally and then train in the cloud.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. 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 ... 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 …Build Help. For a new desktop PC build need a CPU (< $500 budget) for training machine learning. tabular data - train only on CPU. Text/image- train on GPU. I will use the desktop PC for gaming 30% of the time mostly AAA titles. Also general applications on windows and Ubuntu should also work well. Will use a single NVIDIA GPU likely RTX 4070 ...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- ...Beginner. A beginner is a programmer with an interest in machine learning. They may have started to read a book, Wikipedia page, or taken a few lessons in a …Michaels is an art and crafts shop with a presence in North America. The company has been incredibly successful and its brand has gained recognition as a leader in the space. Micha...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.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.The machine learning pipeline consists of 5 executions that exchange data through Valohai pipelines . Each execution is a Python CLI and you can find the code of each one on …a) Learning to read mathematical notation fluently. b) Learning to program. By the time you enter the workforce, a lot of stuff that is now state of the art in ML will be outdated. But being able to read and understand the latest ML research (a) and being able to solve problems with code (b) will always be valuable.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 ...Definitely the day-to-day foot soldiers of applied machine learning in industry aren’t computing Riemann integrals or talking about Hessian matrices. But the concepts listed in this visual aren’t just useless fluff. They really are the foundation of how machine learning works, both in theory and in practice.I like to listen to the following on my runs: Lex Fridman Podcast. Towards Data Science. The TWIML AI Podcast (formerly this week in machine learning and AI Podcast) Hidden Layers. DeepMind The Podcast. AI in Business. These are the ones that I have listend to at least a few episodes. levon9. 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 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 ...What kind of machine learning are you going for (Deep learning, Tree-based, ARIMA etc) ... More importantly however, the behavior of reddit leadership in implementing these changes has been reprehensible. This sub will be private for at least a week from June 12th. For more info go to /r/Save3rdPartyApps/ &#x200B; https://redd.it/144f6xm/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.Both levels of the nested cross-validation used class-stratified random splits. So the splits were IID: independent and identically distributed. The test data looked like the validation data which looked like the training data. This is both unrealistic and precisely how most peer-reviewed publications evaluate when they try out machine learning.27-Nov-2021 ... The dirty little secret of machine learning is that implementing it is not that hard. There's a reason people can learn it from scratch in ...Next, grasp the basics of machine learning. Familiarize yourself with libraries like NumPy, pandas, and scikit-learn. Online courses like those on Coursera by Andrew Ng or edX by MIT can provide a structured learning path. ... Reddit's #1 spot for Pokémon GO™ discoveries and research. The Silph Road is a grassroots network of trainers whose ... 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. 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.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. …Having recently worked with a machine learning consultancy in Melbourne I found there were two roles data scientists : people with a statistical and mathematical background who could also code, they worked on keeping up to date with research, defining the problem to be solved, exploratory data analysis, model selection and training, proof of concept demoOften deep learning won't be a great fit for the latter, but it might be for the former. There's no one-size-fits-all MLE role. Learning is not about spending 3 months understanding matrix calculus and gradient descent. That's nice to know, but more important is learning full stack MLE, including deployment, data, tuning, etc.02-Mar-2021 ... There is no problem with the paper-first approach. In fact, some advocate that it's a good practice (see https://www.microsoft.com/en-us/ ...Machine learning itself is also very broad, and has many of its own subfields. If you're asking about what kind of education to get, or what kind of project to get started with, please tell us a little bit about which branch of AI you're thinking about. ... This rule is part of Reddiquette which is under Post Creation and only editable by ...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 up22-Mar-2023 ... I've not seen an AI actually do research, let alone in ML. Even GPT4 is citing wrong sources and regurgitating old facts instead of creating new ...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. CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. reddit's new API changes kill third party apps that offer accessibility features, mod tools, and other features not found in the first party app. 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.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. Begin by grasping the fundamental concepts of mathematics, particularly linear algebra, and calculus, which serve as the backbone of machine learning algorithms. Familiarize yourself with programming languages such as Python, as it is widely used in the machine learning community. Explore popular machine learning libraries like TensorFlow and ... It is impossible to cover all that in one year, even studying it full-time. Alone the Murphy book in your list has more than 1100 pages. The PGM book is similarly thick. The list is also excessively broad. You should focus in an area that interests you (from the points 16 to 26) and develop an expertise.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 ...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 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.Both levels of the nested cross-validation used class-stratified random splits. So the splits were IID: independent and identically distributed. The test data looked like the validation data which looked like the training data. This is both unrealistic and precisely how most peer-reviewed publications evaluate when they try out machine learning. I work as a software engineer in machine learning mainly for R&D computer vision models. The day goes: 08 - Check results from model trained overnight, understand them, document. Open-Source. 9 1. r/machinelearningnews: We are a community of machine learning enthusiasts/researchers/journalists/writers who share interesting news and articles…. 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. The deep learning specialization? (conflicted on this one because I think it'd be too soon) Read hands-on machine learning with scikit-learn, keras, and tensorflow. Any advice would greatly help and sorry if this is a repetitive post, I tried looking for any posts on the new 2022 course but couldn't find any.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) …Data mining: A human looking for something in a large dataset. Machine learning: Computer programs (AIs) that learn from a large dataset to produce similar, original results. 4. EgNotaEkkiReddit • 3 yr. ago. They are related, but not all data mining is ML and not all ML is data mining. Data Mining is a wide field that involves finding ...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 ...IMO best plan is to buy a cheap but solid laptop e.g. macbook air and spend the rest of the money on cloud computing. Second this. For cloud check out Google Colab first (free/cheap), or once you outgrow it check out https://gpu.land/. It's a side project of mine - we've got Tesla V100s at 1/3 the cost of AWS/Google.Michaels is an art and crafts shop with a presence in North America. The company has been incredibly successful and its brand has gained recognition as a leader in the space. Micha...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. My problem with machine learning is the fundamental nature of 'learning'. As humans, we have imagination and can innovate. I can't even hypothesize how you would build a model to do that. 2, 3 and 4 seems like an enormous amount of technical debt being added. You need to have ci/cd pipeline templates ready for projects. Yes, ML is very much possible to be self taught, with the amount of online blogs and free courses on Coursera, it is very much possible. You can check out the popular Andrew NG's Machine Learning course from Coursera and then move on to deep learning.ai course. Another very detailed and in depth ML course will be from NPTEL.Reddit disclosed the Federal Trade Commission is looking into its sale, licensing or sharing of user-generated content with third parties to train artificial …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 upI 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).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 ...a) Learning to read mathematical notation fluently. b) Learning to program. By the time you enter the workforce, a lot of stuff that is now state of the art in ML will be outdated. But being able to read and understand the latest ML research (a) and being able to solve problems with code (b) will always be valuable.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 reply27-Nov-2021 ... The dirty little secret of machine learning is that implementing it is not that hard. There's a reason people can learn it from scratch in ...Data mining: A human looking for something in a large dataset. Machine learning: Computer programs (AIs) that learn from a large dataset to produce similar, original results. 4. EgNotaEkkiReddit • 3 yr. ago. They are related, but not all data mining is ML and not all ML is data mining. Data Mining is a wide field that involves finding ...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).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 ...Machine learning projects have become increasingly popular in recent years, as businesses and individuals alike recognize the potential of this powerful technology. However, gettin...Open-Source. 9 1. r/machinelearningnews: We are a community of machine learning enthusiasts/researchers/journalists/writers who share interesting news and articles….Data mining: A human looking for something in a large dataset. Machine learning: Computer programs (AIs) that learn from a large dataset to produce similar, original results. 4. EgNotaEkkiReddit • 3 yr. ago. They are related, but not all data mining is ML and not all ML is data mining. Data Mining is a wide field that involves finding ...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.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 …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. …I originally wanted to put together a list of the major cloud providers ML resources. Then it took on a life of its own. Let me know if you have (+/-) suggestions. ML in the cloud training. Google. Google ML Crash Course. Google AI Education. Azure. …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.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 ...16-Jun-2023 ... Very little. A lot of data cleaning, summary statistics, A/B testing, slicing n dicing, and then a decent bit of linear modeling and validation ...Beginner. A beginner is a programmer with an interest in machine learning. They may have started to read a book, Wikipedia page, or taken a few lessons in a …fturla. • 2 yr. ago. The best value GPU hardware for AI development is probably the GTX 1660 Super and/or the RTX 3050. The best overall consumer level without regard to cost is the RTX 3090 or RTX 3090ti. If you want better performance, the Nvidia workstation and server line of GPU products will give you a substantially better performance ...Find the best posts and communities about Machine Learning on Reddit.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 ...For example, ML can be used to improve cybersec by learning from past attacks and identifying and responding to threats real-time. On the other hand, cybersecurity is also important for ensuring privacy and security of data and machine learning models. I'm actually also interested in the intersection of privacy and ML.What Can You Expect? -Diverse Topics: From fundamental algorithms to cutting-edge techniques. -Project-Based Learning: Hands-on projects to apply ML in real-world scenarios. -Collaboration and Networking: An opportunity to connect with like-minded individuals. We WANT Your Input!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 ...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 ...Yes, ML is very much possible to be self taught, with the amount of online blogs and free courses on Coursera, it is very much possible. You can check out the popular Andrew NG's Machine Learning course from Coursera and then move on to deep learning.ai course. Another very detailed and in depth ML course will be from NPTEL.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 ...

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.. What can i make with ingredients

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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. https://mml-book.github.io/ Well, this is literally almost all the math necessary for machine learning. Covering everything in great detail requires more than ~400 pages, but overall this is the most detailed guide on the mathematics used in machine learning. 05-Jan-2024 ... Any good Udemy courses for machine learning (or other good resources for learning as a beginner? · Machine Learning A-Z™: Hands-On Python & R In ...Cleaning things that are designed to clean our stuff is an odd concept. Why does a dishwasher need washing when all it does is spray hot water and detergents around? It does though... 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. CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. reddit's new API changes kill third party apps that offer accessibility features, mod tools, and other features not found in the first party app. 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). With enough data, matrix multiplications, linear layers, and layer normalization we can perform state-of-the-art-machine-translation. Nonetheless, 2020 is definitely the year of transformers! From natural language now they are into computer vision tasks. Honestly, I had a hard time understanding its concepts. This post explains the transformer ... 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...Related Machine learning Computer science Information & communications technology Technology forward back r/slpGradSchool This subreddit has been created specifically for speech-language pathology students to converse about the graduate school application process and for current and former students to discuss, anonymously, the schools of their …A website’s welcome message should describe what the website offers its visitors. For example, “Reddit’s stories are created by its users.” The welcome message can be either a stat...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.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-backed computer vision startup). Afterwards, I wrote an overview of all the concepts that showed up, presented as a series of tutorials along with practice questions at the end of each section. Here's an article I made in 2020 and recently updated that might help you! It is full of free resources going from articles, videos to courses and communities to join, and some really interesting (but paid) certifications you can do to improve your ML skills. There is no right or wrong order, you can skip the steps you already know and start ... 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 all good for getting your feet wet.Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. ... “Reddit gives us the opportunity to look at all these subreddits that are ....

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