Reddit machine learning. If you are fine with spending 1-2 years grinding Leetcode for SDE in a super expensive MS ML/AI/DS program, fine. (fyi: interned at top comp and startups 3 times before masters, top gpa, applied for 300+ internships (a mix of MLE/SDE/DS), heard back from like 10, interviewed at 3, rescinded offer from 1, rejected from 1, accepted from 1 but not ... 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... 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. 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 ...22-Oct-2017 ... Getting Into ML Guides: Seems almost like everyone and their nana wants to 'do Machine Learning' these days. The following guides have been ...As a part of the Reddit Machine Learning Engineer interview, you will need to go through multiple interview rounds: 1. Phone screening - The phone screening is a quick call to discuss your background and ML experience.. 2. Technical Round- You will be asked to build a machine learning model based on data provided by the interviewer.This round is …06-Sept-2023 ... I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also ...22-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 ...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. When possible, these guides have stuck closely to the views of established Machine Learning engineers and researchers. In other places, the author has forwards their view of things. Please feel free to submit feedback and improvements for these any parts of these guides. 1. Getting Into ML: High Schoolers Guide. 2. 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!Are you a sewing enthusiast looking to enhance your skills and take your sewing projects to the next level? Look no further than the wealth of information available in free Pfaff s...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 …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 … The most often recommended textbooks on general Machine Learning are (in no particular order): Hasti/Tibshirani/Friedman's Elements of Statistical Learning FREE; Barber's Bayesian Reasoning and Machine Learning FREE; Murphy's Machine Learning: a Probabilistic Perspective; MacKay's Information Theory, Inference and Learning Algorithms FREE 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. 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.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. 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. There’s more to life than what meets the eye. Nobody knows exactly what happens after you die, but there are a lot of theories. On Reddit, people shared supposed past-life memories...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.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.... If you are fine with spending 1-2 years grinding Leetcode for SDE in a super expensive MS ML/AI/DS program, fine. (fyi: interned at top comp and startups 3 times before masters, top gpa, applied for 300+ internships (a mix of MLE/SDE/DS), heard back from like 10, interviewed at 3, rescinded offer from 1, rejected from 1, accepted from 1 but not ... Referenced Symbols. +0.35%. The Federal Trade Commission has launched an inquiry into Reddit’s licensing of user data to artificial-intelligence companies — just … 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. 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 ... 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 ...In this article, we will discuss more than 70 machine learning datasets that you can use to build your next data science project. Machine Learning Datasets. These are the datasets that you will probably use while working on any data science or machine learning project: Machine Learning Datasets for Data Science Beginners. 📷 1.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.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.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. …diablo 4 buildmeg 3 movie A laptop is perfectly capable of most non-deep learning data science tasks. For deep learning, you can still build the model and run through a few epochs to see if the losses are decreasing. At that point you could put the model on the cloud. In …Anything to do with machine learning (especially deep learning) and Keras/TensorFlow. Users share projects, suggestions, tutorials, and other insights. Also, users ask and …Best Machine Learning Courses for Beginners, Advanced in 2024 - : r/learnmachinelearning. r/learnmachinelearning. r/learnmachinelearning. • 3 min. ago.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.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 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) …Economics) You will likely need to demonstrate your command of the Machine Learning field and ability to conduct research within it. The latter challenge is beyond the scope of this guide. You have a PhD in a non-quantitative field. That program was likely not hugely contributive to Machine Learning unfortunately.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.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 ... golden state warriors starting lineuppink lemonade shoes 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 …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.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. 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. mine tanning 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 … 15.ai alternativeyellow maid of honor dresseswhere can i watch snl 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 ... 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 …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 … pacific northwest trail 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.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. def at pump near me 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 …Anything to do with machine learning (especially deep learning) and Keras/TensorFlow. Users share projects, suggestions, tutorials, and other insights. Also, users ask and …Here at Lifehacker, we are endlessly inundated with tips for how to live a more optimized life—but not all tips are created equal. The best ones are the ones that stick; here are t... 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. 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. is pride a sincrossout game Looking for ways to increase your business revenue this summer? Get a commercial shaved ice machine. Here are some of the best shaved ice machines. If you buy something through our...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. 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 ... 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. how much caffeine in mcdonalds frappe 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. 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....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.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 ... kayak coolerbed friend Some of the benefits to science are that it allows researchers to learn new ideas that have practical applications; benefits of technology include the ability to create new machine...22-Jul-2022 ... r/MachineLearning Current search is within r/MachineLearning. Remove r/MachineLearning filter and expand search to all of Reddit. TRENDING ...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 ...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 ...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 ...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 ...It's a rendering technique that uses differentiable equations. Of course this is used in machine learning, but the DR itself doesn't have any predictions or "intelligence". Neural rendering is rendering using deep learning. So, of course it should need to use some form of differentiable rendering, but it goes a bit farther. 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 ... Recommendations for learning mathematics for machine learning. I'm having a bit of a hard time keeping up with the Mathematics for Machine Learning Course by Andrew …Machine learning has become a hot topic in the world of technology, and for good reason. With its ability to analyze massive amounts of data and make predictions or decisions based...Sort by: cthorrez. • 6 yr. ago. There is a huge oversaturation of people who took a Coursera or edex class with no experience or theoretical knowledge applying to machine learning engineering positions. There is an undersaturation of people with master's and PhDs in machine learning who can actually perform good research and development in ... how to i upload photos to icloud 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 upIt 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.Advertising on Reddit can be a great way to reach a large, engaged audience. With millions of active users and page views per month, Reddit is one of the more popular websites for ...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. tai mai drink 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 ...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. Cohere's intelligent prior authorization solutions reduce administrative expenses while improving patient outcomes. The company is a winner of the TripleTree iAward and has been named to both Fierce Healthcare's Fierce 15 and CB Insights' Digital Health 150 lists. 🌎 Location: United States. 💵 Salary: USD 130k-160k. 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... mazda wagon 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, …Paper: https://arxiv.org/abs/2403.07815. Code: https://github.com/amazon-science/chronos-forecasting. Model weights: https://huggingface.co/collections/amazon/chronos-models …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 … Cohere's intelligent prior authorization solutions reduce administrative expenses while improving patient outcomes. The company is a winner of the TripleTree iAward and has been named to both Fierce Healthcare's Fierce 15 and CB Insights' Digital Health 150 lists. 🌎 Location: United States. 💵 Salary: USD 130k-160k. 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. 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 ... fix cracked glassfarmers market san jose 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 ...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.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:As a part of the Reddit Machine Learning Engineer interview, you will need to go through multiple interview rounds: 1. Phone screening - The phone screening is a quick call to discuss your background and ML experience.. 2. Technical Round- You will be asked to build a machine learning model based on data provided by the interviewer.This round is …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.18-Sept-2022 ... Remove r/MachineLearning filter and expand search to all of Reddit ... r/MachineLearning icon. Go to MachineLearning ... machine learning projects?Economics) You will likely need to demonstrate your command of the Machine Learning field and ability to conduct research within it. The latter challenge is beyond the scope of this guide. You have a PhD in a non-quantitative field. That program was likely not hugely contributive to Machine Learning unfortunately.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 ...Are you a programmer looking to take your tech skills to the next level? If so, machine learning projects can be a great way to enhance your expertise in this rapidly growing field...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...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 ...Find the best posts and communities about Machine Learning on Reddit. handcrafted drink starbucks Hand-on machine learning + Mathematics for machine learning. I want to learn machine learning and I've decided to pick the book "Hand-on machine learning with Scikit-Learn, Keras, and Tensorflow" (2nd Ed). However, I've read a bunch of other similar posts in this sub about its lack of theoretical and mathematical depth.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.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) … flaky salt 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 ...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.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.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) my dog diedpeanut butter breath strain Reddit iOS Reddit Android Rereddit Best Communities Communities About Reddit Blog Careers Press. ... has become increasingly intriguing — whether it be the development of new machine learning models to analyze data at a faster pace, the collection of data from multitudes of amateur stargazers, or even the use of cutting-edge data science ...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.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 … home alone.house 1 Introduction. The field of stress analysis and sentiment analysis of posts on microblogging sites has been blossoming in recent years. Mental stress leads to … Anything to do with machine learning (especially deep learning) and Keras/TensorFlow. Users share projects, suggestions, tutorials, and other insights. Also, users ask and answer any questions pertaining to ML with Keras. When possible, these guides have stuck closely to the views of established Machine Learning engineers and researchers. In other places, the author has forwards their view of things. Please feel free to submit feedback and improvements for these any parts of these guides. 1. Getting Into ML: High Schoolers Guide. 2. 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.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 ...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 ...Reddit iOS Reddit Android Rereddit Best Communities Communities About Reddit Blog Careers Press. ... has become increasingly intriguing — whether it be the development of new machine learning models to analyze data at a faster pace, the collection of data from multitudes of amateur stargazers, or even the use of cutting-edge data science ... 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. roaches exterminator Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. Deep Learning with Python by François Chollet. Pattern Recognition and Machine Learning by Christopher M. Bishop. Machine Learning by Kevin P. Murphy. The Hundred-Page Machine Learning Book by Andriy Burkov. When possible, these guides have stuck closely to the views of established Machine Learning engineers and researchers. In other places, the author has forwards their view of things. Please feel free to submit feedback and improvements for these any parts of these guides. 1. Getting Into ML: High Schoolers Guide. 2. 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 ... whats the difference between pansexual and bisexual Go to learnmachinelearning. r/learnmachinelearning. A subreddit dedicated to learning machine learning. MembersOnline. •. Ishannaik. ADMIN MOD. A Clear roadmap to …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 ...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/ ​ https://redd.it/144f6xm/ light wash denim 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 ...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/ ...The course experience for online students isn’t as polished as the top three recommendations. It has a 4.43-star weighted average rating over 7 reviews. Mining Massive Datasets (Stanford University): …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 projects have become increasingly popular in recent years, as businesses and individuals alike recognize the potential of this powerful technology. However, gettin...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, … Anything to do with machine learning (especially deep learning) and Keras/TensorFlow. Users share projects, suggestions, tutorials, and other insights. Also, users ask and answer any questions pertaining to ML with Keras. The course experience for online students isn’t as polished as the top three recommendations. It has a 4.43-star weighted average rating over 7 reviews. Mining Massive Datasets (Stanford University): …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 ...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 ...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.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/ ...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.Mathematics also plays a vital role in machine learning. It would help if you had a strong command of statistics, linear algebra, calculus, probability, and optimization theory. If your technical knowledge is weak, make your maths part strong. Then there is data engineering, machine learning, and deep learning involved in the process.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 ... horizon call of the mountaingremlins 2 film 1. Getting Into ML: High Schoolers Guide. 2. Getting Into ML: Engineers Guide. 3. Getting Into ML: Academics/Researchers Guide. 4. Getting Into ML: Hackers Guide. Looking for … does espn+ include espn 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 ...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. …Machine learning resources for beginners. Hi all, here's a list of free resources I made for my data science studies (I'm just starting out). There are courses, tutorials, and videos that I think are pretty decent and are all free. While the main focus is on data science, there are quite a bit of machine learning resources as well so I wanted ...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 replyWell defined machine learning projects for resume. I am trying to get a job as a data scientist. Although I know most of the underlying mathematical and statistical fundamentals and have a pretty good research experience in causal identification (I am an economics grad), I don't have any work experience developing an end-to-end machine learning ...This is thousands of pages. Algebra, Topology, Differential Calculus, and Optimization Theory. For Computer Science and Machine Learning. Jean Gallier and Jocelyn Quaintance Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA. e-mail: [email protected] machine learning + Mathematics for machine learning. I want to learn machine learning and I've decided to pick the book "Hand-on machine learning with Scikit-Learn, Keras, and Tensorflow" (2nd Ed). However, I've read a bunch of other similar posts in this sub about its lack of theoretical and mathematical depth.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.This is Jeremy Howard's advice as well: "train a lot of models". So I recommend you spend most of your time doing practical implementations and learning that way: Kaggle problems, reimplementing research that interests you, or repurposing existing tools to solve a slightly different problem. The_Amp_Walrus.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. I am using my current workstation as a platform for machine learning, ML is more like a hobby so I am trying various models to get familiar with this field. My workstation is a normal Z490 with i5-10600, 2080ti (11G), but 2x4G ddr4 ram. The 2x4G ddr4 is enough for my daily usage, but for ML, I assume it is way less than enough. 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. I’d also recommend Intro to Statistical Learning if OP wants an introductory book on ML theory. The people who wrote ISLR are the same who wrote “Elements of Statistical Learning” (ESLII) which is around the same level of difficulty as PRML. They specifically wrote ISLR because ESLII was too tough for most undergrads to read in a timely ... 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...Here is the list of all ~1,600 ICML 2023 (International Conference on Machine Learning) papers and a highlight for each of them. ICML 2023 will take place from July 23 at Hawaii. In addition, here is the link of "search within venue service" that can be used to find papers within ICML-2023 related to a specific topic, e.g. "diffusion model":06-Sept-2023 ... I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also ...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 ... I’d also recommend Intro to Statistical Learning if OP wants an introductory book on ML theory. The people who wrote ISLR are the same who wrote “Elements of Statistical Learning” (ESLII) which is around the same level of difficulty as PRML. They specifically wrote ISLR because ESLII was too tough for most undergrads to read in a timely ... Mathematics also plays a vital role in machine learning. It would help if you had a strong command of statistics, linear algebra, calculus, probability, and optimization theory. If your technical knowledge is weak, make your maths part strong. Then there is data engineering, machine learning, and deep learning involved in the process. spectrum voicemailplanting laurel plants Other than than those two, the others that helped me were Applied Predictive Modeling (Kuhn and Johnson), Introduction to Machine Learning (Alpaydin), Machine Learning Refined (Watt et al.). And then of course Mathematics for Machine Learning (Deissenroth et al.). Bayesian Reasoning and Machine Learning is also great (Barber) but more … I am using my current workstation as a platform for machine learning, ML is more like a hobby so I am trying various models to get familiar with this field. My workstation is a normal Z490 with i5-10600, 2080ti (11G), but 2x4G ddr4 ram. The 2x4G ddr4 is enough for my daily usage, but for ML, I assume it is way less than enough. Machine learning models can find patterns in big data to help us make data-driven decisions. In this skill path, you will learn to build machine learning models using regression, classification, and clustering. Along the way, you will create real-world projects to demonstrate your new skills, from basic models all the way to neural networks. Cohere's intelligent prior authorization solutions reduce administrative expenses while improving patient outcomes. The company is a winner of the TripleTree iAward and has been named to both Fierce Healthcare's Fierce 15 and CB Insights' Digital Health 150 lists. 🌎 Location: United States. 💵 Salary: USD 130k-160k. 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! lan speed test 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...22-Oct-2017 ... Getting Into ML Guides: Seems almost like everyone and their nana wants to 'do Machine Learning' these days. The following guides have been ... 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. 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. chicory coffee new orleansgood vegan meals 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. I use machine learning for my long options portfolio, I use classifiers to establish potential group of candidates then predictors for placing the orders, stop loss is a simple ATR band, wider for calls, narrower for puts, Daily data set with price derivatives and fundamental analysis data to better time entry. what comes in a happy meal Other than than those two, the others that helped me were Applied Predictive Modeling (Kuhn and Johnson), Introduction to Machine Learning (Alpaydin), Machine Learning Refined (Watt et al.). And then of course Mathematics for Machine Learning (Deissenroth et al.). Bayesian Reasoning and Machine Learning is also great (Barber) but more …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 ...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...Reddit iOS Reddit Android Rereddit Best Communities Communities About Reddit Blog Careers Press. ... has become increasingly intriguing — whether it be the development of new machine learning models to analyze data at a faster pace, the collection of data from multitudes of amateur stargazers, or even the use of cutting-edge data science ...Machine learning models can find patterns in big data to help us make data-driven decisions. In this skill path, you will learn to build machine learning models using regression, classification, and clustering. Along the way, you will create real-world projects to demonstrate your new skills, from basic models all the way to neural networks. escape room san franciscoair duct cleaning scams 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 ...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 ...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/ ...This is thousands of pages. Algebra, Topology, Differential Calculus, and Optimization Theory. For Computer Science and Machine Learning. Jean Gallier and Jocelyn Quaintance Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA. e-mail: [email protected] 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 …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)What is machine learning? Machine learning combines computer science, artificial intelligence, and statistics to quickly process large volumes of data and teach systems how to recognize patterns in data sets. It has a wide range of applications, from guiding decision-making to building chatbots and self-driving cars.If you think that scandalous, mean-spirited or downright bizarre final wills are only things you see in crazy movies, then think again. It turns out that real people who want to ma...Learn Machine Learning. A subreddit dedicated to learning machine learning. 374K Members. 273 Online. Top 1% Rank by size. Related. Machine learning Computer science Information & communications technology Technology. r/mlops. 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. 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 …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 ...Jun 16, 2022 · 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 ... 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 ...Here is the list of all ~1,600 ICML 2023 (International Conference on Machine Learning) papers and a highlight for each of them. ICML 2023 will take place from July 23 at Hawaii. In addition, here is the link of "search within venue service" that can be used to find papers within ICML-2023 related to a specific topic, e.g. "diffusion model":Sort by: cthorrez. • 6 yr. ago. There is a huge oversaturation of people who took a Coursera or edex class with no experience or theoretical knowledge applying to machine learning engineering positions. There is an undersaturation of people with master's and PhDs in machine learning who can actually perform good research and development in ...22-Jul-2022 ... r/MachineLearning Current search is within r/MachineLearning. Remove r/MachineLearning filter and expand search to all of Reddit. TRENDING ... rug cleaning san antoniobest weightlifting shoes I am using my current workstation as a platform for machine learning, ML is more like a hobby so I am trying various models to get familiar with this field. My workstation is a normal Z490 with i5-10600, 2080ti (11G), but 2x4G ddr4 ram. The 2x4G ddr4 is enough for my daily usage, but for ML, I assume it is way less than enough. how much can you make doing uber eats 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. 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 ...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 …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.Machine learning resources for beginners. Hi all, here's a list of free resources I made for my data science studies (I'm just starting out). There are courses, tutorials, and videos that I think are pretty decent and are all free. While the main focus is on data science, there are quite a bit of machine learning resources as well so I wanted ...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) …Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/gamedev The subreddit covers various game development aspects, including programming, design, writing, art, game jams, postmortems, and marketing.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.Often 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. I use machine learning for my long options portfolio, I use classifiers to establish potential group of candidates then predictors for placing the orders, stop loss is a simple ATR band, wider for calls, narrower for puts, Daily data set with price derivatives and fundamental analysis data to better time entry. 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 ...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).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...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 by Aurélien Géron. Deep Learning with Python by François Chollet. Pattern Recognition and Machine Learning by Christopher M. Bishop. Machine Learning by Kevin P. Murphy. The Hundred-Page Machine Learning Book by Andriy Burkov.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.377K subscribers in the learnmachinelearning community. A subreddit dedicated to learning machine learningHere'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 ... 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. 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 ...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. application for meditationmr. and mrs. smith television show 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 ... Anything to do with machine learning (especially deep learning) and Keras/TensorFlow. Users share projects, suggestions, tutorials, and other insights. Also, users ask and answer any questions pertaining to ML with Keras. 22-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 ...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. …Here's a list of the presented sites (only the AWS one was part of the description): Google dataset search. kaggle. Nasa Earth Data. AWS Open Data. Azure Open Datasets. FBI Crime Data Explorer. Data.world. CERN open data. 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. 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). his and her marriage novel For classification and regression problems with tabular data, the use of tree ensemble models (like XGBoost) is usually recommended. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use-cases. In this paper, we explore whether these deep models should be a …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 ...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). pork tenderloin smoker recipedeath of a brother quotes ---2