Reddit machine learning.

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) 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. It's a fairly short, 300-ish pages book, but it offers good conceptual descriptions of AI/machine learning concepts, along with an interesting overview of the related technologies available in the Microsoft ecosystem. The code samples are a mix of C# and (inevitably) Python. 2. ryanwithnob.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.On the other hand deep learning is a subset of AI that you could totally skip altogether and specialize in ML or DS. If you need specific courses or books ive heard the hands on machine learning with sklearn, keras, tensor flow book is very good and if you prefer a course the andrew ng one is regarded as the best.

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 ...Thank you. 262 votes, 23 comments. 387K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learning.

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 ... Read our blog on the most important Machine Learning trends of 2023! Learn how IoT innovation and Automated ML are reshaping industries, and how ML democratization is making AI accessible to all! Find out how ethical guidelines and MLOps are shaping the future of AI for the better! Don't miss out on the insights shared by our Head of Emerging ...

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- ...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.C++ is used in the development of frameworks and libraries such as Tensorflow but as a user you don't need to know any C++. Yeah, this seems to be true of many high power computing applications. The building blocks of things like simulations, machine learning, encryption breaking, and genetic algorithms don't change that much. 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.

I used a 3060 for the first year of my PhD, it worked fine (can't compare with anything else though since I never used others). The ram was nice. I use 2 3070s, and it works fine. If you use frameworks, they might not support them yet, so you should look into that first, most have workarounds for that though.

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 ...

It's a fairly short, 300-ish pages book, but it offers good conceptual descriptions of AI/machine learning concepts, along with an interesting overview of the related technologies available in the Microsoft ecosystem. The code samples are a mix of C# and (inevitably) Python. 2. ryanwithnob. 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. 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. 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 …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.

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- ...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 …“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 … Redirecting to /r/MachineLearning/new/. 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...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...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 ...

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.

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 ...31-Jul-2023 ... To be fair, deep learning is working really really well. It's shattered all records across everything from computer vision to reinforcement ...Given this problem, it will be quite interesting to know if accurate predictions can be made using machine learning and the information that Reddit allows users to …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 ...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 ... There was a thread on here or r/datascience about how companies utilize machine learning in two ways: 1) to help sell the companies already existing product or service or 2) to build the companies new product or services. A vast majority of AutoML-conducive use cases fall into bin 1. 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. •.

A big "check mark" on the resume. It is highly performant and high volume - 300 transactions per second. Again, a big "check mark" on the resume. Machine Learning training, processing platform that scales to hundreds of transactions per second using containerized K8 API-first microservice architecture. A bagful but it sells.

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.

Related Machine learning Computer science Information & communications technology Technology forward back r/MLQuestions 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.Simple as that. So an alternative to deep learning is tree based methods and gradient boosted methods on top of those trees. XGBoost etc. These aren't technically deep learning but they have a ton in common. There’s living neurons in an artificial network that’s more of neuro/cognitive science.The common saying is "working with AI means spending 80% of your time working with data." Currently, working with AI means two things: either you do research (and you have to be somewhat exceptional for that), or you work in the "real world", which means you spend most of your time working with data. This is the impression I have gotten, and I ...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. 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. 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 ...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....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.Hugging Face 🤗 recently announced the Transformers Audio Course, a comprehensive guide to using the latest machine learning techniques for the most popular audio tasks. In this course, you'll gain an understanding of the specifics of working with audio data, learn about different transformer architectures, and train your own audio transformers, leveraging … 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 ... 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 ...

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.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.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. …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...Instagram:https://instagram. star trek nyota uhuraquincy restaurantssamsung fridge ice maker not workingserial key for windows 10 schwah • 2 yr. ago. Step 1: Use Python. All of the best ML libraries are Python. Prety much all of the compute heavy stuff you'd want to do should be through library implementations (which are written in highly optimized C++/CUDA) so you aren't going to see any performance benefit in writing in C++ vs Python.Find the best posts and communities about Machine Learning on Reddit. chris from married at first sightminimalist fashion 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 ... 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 … piano la la land city of stars 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 learningThe 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 ...