Machine learning reddit

Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back. r/buildapc. ... The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. --- If you have questions or ...

View community ranking In the Top 1% of largest communities on Reddit [D] Advanced resources for ML theory/math. So I have been working in ML for the past 3 years as a researcher and now PhD candidate, and though I have an understanding of intermediate level of the math behind most algorithms. ... There seems to be a lot of overlap between the ...It depends on the quality of your data, and also the type of data. Nowadays a lot of new techniques in the industry, helping add more architectures and learning methods for every task. Check out huggingface.co if you haven't already. It's … Only deep learning is really better in Python. Advanced statistics and new papers on that realm are much faster integrated to R on the other hand. Deep learning vs adv. Stats For "normal" machine learning use R works as well as Python. R has many packages which might cause confusion compared to Python having pretty much everything in scikit-learn. Machine learning is much broader than this one approach. The crucial distinguishing feature of ML from data science is that machine learning's focus is on methods of learning from data. Unlike data models where the relationships of elements is pre-defined by programmers or architects, machine learning algorithms discover the patterns in the ... 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 ... For basic machine learning I still think Bishops "Pattern Recognition and Machine Learning" is a very good probabilistic book and "The Elements of Statistical Learning" and the more beginner friendly "An Introduction to Statistical Learning: With Applications in R" are great from a risk minimization point of view.View community ranking In the Top 1% of largest communities on Reddit [D] Advanced resources for ML theory/math. So I have been working in ML for the past 3 years as a researcher and now PhD candidate, and though I have an understanding of intermediate level of the math behind most algorithms. ... There seems to be a lot of overlap between the ...

Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/ITCareerQuestions This subreddit is designed to help anyone in or interested in the IT field to … Related Machine learning Computer science Information & communications technology Technology forward back r/OMSA The Subreddit for the Georgia Tech Online Master's in Analytics (OMSA) program caters for aspiring applicants and those taking the edX MicroMasters programme. Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder. Hi all! Here is a new self-supervised machine learning approach that captures word meaning with concise logical expressions. The logical expressions consist of contextual words like “black,” “cup,” and “hot” to ... No. R is maybe a bit better for explorations, but its productization is rly bad. Python has both. It can be used both for explorations and for developing final product. And its getting even better at both those tasks. Programming in Python since ~2003, using R for machine learning since ~2012. Oct 22, 2017 ... Getting Into ML Guides: Seems almost like everyone and their nana wants to 'do Machine Learning' these days. The following guides have been ... Murphy's Machine Learning: a Probabilistic Perspective; MacKay's Information Theory, Inference and Learning Algorithms FREE; Goodfellow/Bengio/Courville's Deep Learning FREE; Nielsen's Neural Networks and Deep Learning FREE; Graves' Supervised Sequence Labelling with Recurrent Neural Networks FREE; Sutton/Barto's Reinforcement Learning: An ...

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 ...There's really a few different things you could learn with AWS. Machine Learning training using GPU instances. This will likely be the easiest to learn, and it essentially just means allocating a server with a GPU (usually something like a K80 or P100 for $1-3/hr, prorated to the minute), setting it up, and training on it.Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/nvidia A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more.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 … Machine learning is much broader than this one approach. The crucial distinguishing feature of ML from data science is that machine learning's focus is on methods of learning from data. Unlike data models where the relationships of elements is pre-defined by programmers or architects, machine learning algorithms discover the patterns in the ...

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Reddit announced Thursday that it would buy Spell, a platform for running machine learning experiments, for an undisclosed amount.. Spell was founded by former …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 ...But most of my interest was for the mathematics behind Machine Learning and AI. And most of the ML projects are just programming on keras and stuff. Like there can be maths involved here, just not the heavy kind like we learn in theory, so is there usually much research going on under AI making or refining mathematical algorithms for AI ...A Machine Learning project is an order of magnitude more difficult to deliver than a software engineering project. Model drift, ethical implications of dataset outliers, driving project decisions that are centered around mathematics, all of that is insanely difficult.4tomorrow678. • 1 yr. ago. Python is widely used for machine learning due to its simple and easy-to-read syntax, and its strong community support. It allows developers to easily build and prototype machine learning models and perform data analysis tasks efficiently.

MICCAI and IPMI are A tier conferences in medical image computing (lot of similar themes as AI/ML are applied in these papers) Some applications conferences similar to CVPR or ACL that typically feature ML: FAccT, RecSys, WSDM, TheWebConf, SIGIR, ICDM.The goal is to get the model deployed as cheaply as possible, with as low downtime as possible. However, when trying various tech, I encountered various problems: Using Cloud Functions (GCP Functions, AWS Lambda): Low memory (max 2-4GB), quick timeouts, costs scale with time. Kubernetes Cluster: Managed cluster costs >$100, and add on top the ...Secondly, learning and education is not a baby feeding session nor is it a quick hit with the golden solution. It is the pursuit of finding answers and solutions wherever they may be and using many different sources, however lengthy (e.g. a book) or a 13-minute YouTube clip (which you can scrub through to the end by the way, where the ...Here are our top picks of Reddit’s machine learning datasets. Best Reddit Datasets for Machine Learning. Cryptocurrency Reddit Comments Dataset: Containing …A Machine Learning project is an order of magnitude more difficult to deliver than a software engineering project. Model drift, ethical implications of dataset outliers, driving project decisions that are centered around mathematics, all of that is insanely difficult.I wrote a blog post about why using docker for your ML workspace makes sense and included a step-by-step guide on how to do it. It was inspired by a tweet by Jeremy Howard and another blog post introducing docker for ML. I think docker is great for a few reasons, namely the fact that it standardizes your environment, makes it easy to ...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. 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 ... At the company I work at, we've hired candidates who have gone on to be fantastic machine learning researchers without asking them for a GitHub repo or 3 years of Kaggle history. None of that crap. All you need to be successful (and what we look for) is have a solid understanding of the background maths (elements of calculus, linear algebra ...When you don't understand a concept or don't remember something, stop it, take a book (or open YouTube) and learn about it. It will take time, but it's worth it. If you don't remember anything about linear algebra or calculus, open YouTube and find some video about it. After that, continue with Andrew ng.

Here we go again... Discussion on training model with Apple silicon. "Finally, the 32-core Neural Engine is 40% faster. And M2 Ultra can support an enormous 192GB of unified memory, which is 50% more than M1 Ultra, enabling it to do things other chips just can't do. For example, in a single system, it can train massive ML workloads, like large tra

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.Reddit announced Thursday that it would buy Spell, a platform for running machine learning experiments, for an undisclosed amount.. Spell was founded by former …The book Pattern Recognition and Machine Learning by Christopher Bishop, not free but one of the best starting point. The book Bayesian Reasoning and Machine Learning by David Barber. The book The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman.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...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...After some digging, I narrowed it down to these two candidates: Linear Algebra and Optimization for Machine Learning: A Textbook by Charu C. Aggarwal. Introduction to Linear Algebra by Gilbert Strang. Would very much appreciate to hear your experience with either of them! EDIT: Wow, thank you guys!View community ranking In the Top 1% of largest communities on Reddit [D] Advanced resources for ML theory/math. So I have been working in ML for the past 3 years as a researcher and now PhD candidate, and though I have an understanding of intermediate level of the math behind most algorithms. ... There seems to be a lot of overlap between the ...

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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. Hydraulic machines do most of the heavy hauling and lifting on most construction projects. Learn about hydraulic machines and types of hydraulic machines. Advertisement ­From backy...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...Aug 12, 2021 ... r/MachineLearning Current search is within r/MachineLearning. Remove r/MachineLearning filter and expand search to all of Reddit. TRENDING ...I 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).I spent a summer as a Data Scientist intern and now work as ML Engineer. If you enjoy coding more, do ML Engineer. ML Engineer is just a specialized Software Engineer. If you ever seen the role "Software Engineer - Machine Learning" that's pretty much interchangeable with ML Engineer. Most ML Engineers I've met come from having Software ...Check out Ace the Data Science Interview — it covers statistics, machine learning, and open-ended ML case study interview questions. The book focuses more on the foundations of the field + interview questions related to classical ML techniques, rather than something like reinforcement learning, because honestly, that's what 90% of Data Science & ML …The final capstone project in Coursera's Machine Learning and similar specializations is a worthwhile investment, IMHO. So, I probably wouldn't worry much about an individual course certificate, unless you plan to complete a series of courses and do the capstone project. 4.Mar 4, 2023 ... The modelling part only takes up 20-30% of the job. Deep learning (apart from NLP) RL and CV are not as frequently used in industry. Most of the ...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. 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. ….

This is more specific to deep learning but obviously many concepts apply to wider machine learning. This is supposed to be THE book. Freely available. Written by, among others, Ian Goodfellow; the creator of GANs. It’s actually pretty good. It’s about exactly the amount of maths you need to understand deep learning. 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. Machine Learning is a very active field of research. The two most prominent conferences are without a doubt NIPS and ICML. Both sites contain the pdf-version of the papers accepted there, they're a great way to catch up on the most up-to-date research in the field. ... This subreddit is temporarily closed in protest of Reddit killing third ...MICCAI and IPMI are A tier conferences in medical image computing (lot of similar themes as AI/ML are applied in these papers) Some applications conferences similar to CVPR or ACL that typically feature ML: FAccT, RecSys, WSDM, TheWebConf, SIGIR, ICDM.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... These models are tools to improve your NLP workflow. So yes it’s still required to learn ML. Instead of using 100 different models for 100 different tasks, we now can use 1 model for 100 tasks. That’s what’s the hype’s all about. But it’s still far from achieving a state where it can create good models for some tasks. I was facing a similar choice after a Bachelors in the UK. Landed pretty much a dream job in a small consulting company focusing on data science & machine learning. It's amazing - you still keep learning new things just like you would doing your degree but you also see a real impact of your work. Plus instead of paying for the degree you get paid. Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/buildapc Planning on building a computer but need some advice? There are many good courses on machine learning available online. Some of the most popular ones include: Skillpro's Machine Learning course by by Juan Galvan: skillpro.io. Coursera's Machine Learning course by Andrew Ng: coursera.org. Fast.ai's Practical Deep Learning for Coders course: course.fast.ai. Machine learning reddit, [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1], [text-1-1]