Machine learning reddit.

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 ... 5. Open Source Libraries: Familiarize yourself with popular libraries like TensorFlow and PyTorch for deep learning, scikit-learn for machine learning, and OpenCV for computer vision. 6. Stay Updated: Follow AI and machine learning blogs, podcasts, and conferences to stay up-to-date with the latest advancements. 7.Here are our top picks of Reddit’s machine learning datasets. Best Reddit Datasets for Machine Learning. Cryptocurrency Reddit Comments Dataset: Containing …Data mining: A human looking for something in a large dataset. Machine learning: Computer programs (AIs) that learn from a large dataset to produce similar, original results. EgNotaEkkiReddit. • 3 yr. ago. They are related, but not all data mining is ML and not all ML is data mining. Data Mining is a wide field that involves finding ...

A linear classifier is the hello world of machine learning. If you're interested in robotics is specifically you'll want to learn Reinforcement Learning which is probably the most difficult area of ML to get into. Unfortunately Reinforcement Learning (RL) falls …

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 ... Apple released TensorFlow support for the M1 Neural Chip (see my comment above). But since this would use system memory afaik, model complexity would indeed be limited. Though one can already fit very capable models within e.g., 4GB Neural Chip memory. Basic models yes, but for SOTA models not nearly enough.

There are a few tricks you can do with conda to make life a bit simpler, here is my run-done: Use miniconda instead of anaconda. Use conda-forge channel instead of defaults for the latest packages. (My usual channel priority is pytorch > conda-forge > defaults ) Never install packages in base.Basically, if you are implementing and training from scratch, focus on something you can train with a smallish dataset in a reasonable period of time. I would generally steer away from LLMs and object detection / segmentation models as they require more resources to train that are commonly available! 22. TheInfelicitousDandy.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).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 ...Sep 12, 2021 ... Deep learning is a subset of ML that use variants of Neural Network model. Other than deep network there are decision trees, linear regression, ...

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.

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!

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 ... 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. 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 ...Here are some steps you can take to become a Machine Learning Engineer: Gain a Strong Foundation in Computer Science, Mathematics, and Statistics: A solid foundation in computer science, mathematics, and statistics is essential for becoming a Machine Learning Engineer. You can obtain this foundation through formal education, such as a degree in ...Deep Learning Specialization on Coursera. 5 courses and you pay $50/month until you finish them. Echoing previous comments, I would not take this for the “certificate” but for the knowledge. If you need help getting started on projects, take these courses then …

Reddit is a popular social media platform that boasts millions of active users. With its vast user base and diverse communities, it presents a unique opportunity for businesses to ...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 ... 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. I think that the new major breakthroughs will be in the cross-pollination between domains between ML and specific application domains. The general knowledge and techniques about ML is vastly increasing, however, for specific domains, such as healthcare or other high-stake applications, the ML adoption rate is far below other applications domains.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.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...r/learnmachinelearning: A subreddit dedicated to learning machine learning.

r/learnmachinelearning. • 1 yr. ago. DeF_uIt. Is ML career worth it? Firstly I stuck with web backend development because of the huge pool of job openings and high payment. But then I'v got interested in machine learning (Deep learning, RL, CV actually all of that look attractive to me). Tips for Learning AI: Start with the basics: Learn the necessary math, programming, and ML concepts. Work on projects: Apply your knowledge to real-world problems to solidify your understanding. Join a community: Engage with like-minded individuals to share ideas, resources, and support.

Matlab's pretty cool for learning concepts without as much library overhead, it's really not hard to pick up. If you're decent at coding, you'll likely find you can blow through assignment style problems pretty quick, at least if they're linear algebra related. If you'd rather do them in a more useful framework though, you can always do the ...However, machine learning (ML)–based approaches have been previously applied to identify misinformation on Twitter regarding controversial topic domains and rumors regarding a range of topics . ML involves the use of algorithms and statistical modeling that provide the ability to automatically conduct tasks and learn without using explicit ...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.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.In numerical analysis and computer science, a sparse matrix or sparse array is a matrix in which most of the elements are zero. By contrast, if most of the elements are nonzero, then the matrix is considered dense. The number of zero-valued elements divided by the total number of elements (e.g., m × n for an m × n matrix) is called the ... Specialization - 3 course series. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. Project. The deployment of ML models in production is a delicate process filled with challenges. You can deploy a model via a REST API, on an edge device, or as as an off-line unit used for batch processing. You can build the deployment pipeline from scratch, or use ML deployment frameworks. In my new mini-series, you'll learn best practices to ...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.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).

A linear classifier is the hello world of machine learning. If you're interested in robotics is specifically you'll want to learn Reinforcement Learning which is probably the most difficult area of ML to get into. Unfortunately Reinforcement Learning (RL) falls …

Yes. AI is hard. Right now, the people doing real AI stuff are people with PhDs or PhD students. Once the hard part of AI is done, it's not that hard for any dumb developer to wrap an app around the model to do some neat things with it. It's the developing and training the model that is the hard part.

Reddit announced Thursday that it would buy Spell, a platform for running machine learning experiments, for an undisclosed amount.. Spell was founded by former …Try the Stanford class on machine learning on YouTube, it's also by Andrew Ng but is more in depth, has more maths and IMO is all around better. Coursera Machine Learning is good but I feel the notation on neural networks is somewhat convoluted and it's taught in Matlab/Octave (which can be alright depending on your background, but it was a bit ...The secret to improving the predictive ability of machine learning is the sometimes deceptively obvious. The answer is feature engineering. You and cardiologist (in this case) need to think about what clues does a human use for making this decision that is not directly available in all the data that you are providing and then transform the data as necessary …Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/cybersecurity This subreddit is for technical professionals to discuss cybersecurity news, research, threats, etc.I also do a bunch of ML research in Python, as the deep learning stack (particularly for distributed problems) is just not there on the JVM. The Python ecosystem still has better data frames & plotting, as well as the aforementioned distributed deep learning stack, but you can do many things in scikit-learn just as well in Java. Hopefully a masters program will give you some inkling as well. Master's or Ph.D. degrees sound great only if you wanna do in-depth studies. If you really want to learn more, then you should go for it, but remember it is time-consuming. So, rather than, I would suggest you also look for post-graduate courses. 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 ...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 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 ... r/MLjobs: A place where redditors can post ML-related jobs, resumes, and career discussion.May 30, 2023 ... You can learn machine learning without being strong in math by focusing on practical implementations, utilizing high-level libraries, ...Offer 1: Data Scientist at a big Oil and Gas Corp. The job profile involves research in Process Mining. Offer 2: Machine Learning Engineer at a popular Analytics Consulting Firm. The profile involves deploying machine learning and deep learning models using Kubernetes, Heroku, Dask, etc. Both options are at my choice of location and Offer 2 is ...

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...Basically, if you are implementing and training from scratch, focus on something you can train with a smallish dataset in a reasonable period of time. I would generally steer away from LLMs and object detection / segmentation models as they require more resources to train that are commonly available! 22. TheInfelicitousDandy.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 ...Instagram:https://instagram. breakfast st louisgold rush getawaysrestaurants in foxboro macoconut milk smoothie 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. coursera – machine learning (first three weeks) 100 page ML book. From now on, three areas of focus will be given for each level: Mathematics, Concrete ML knowledge, and Programming. Level 2 – Competent Developer. Have basic intuition about the math relevant for ML. meal prep breakfastbbysaf Other answers already mentioned there's an established ecosystem, but another important point is that Python can wrap libraries written in other faster programming languages. Most of numpy is written in C and Fortran, so this is why Python is good for ML even though it is slower than some other languages. 83.The completely new version of Fast.ai 's super popular Practical Deep Learning for Coders course was just put online today. This is the course I recommend the most to people wanting to learn how to create real deep learning models. They've apparently re-written the whole course from the ground up. This is great. bose quietcomfort earbuds 3 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. Additional: ColumbiaX [edX] - Machine Learning. Next, you have to learn to build ML pipelines (Details can be found here ) Finally, you have to : Find your preferred data. Clean/Transform the data. Choose Algorithms for the data or write your own to get your desired results. Visualizing Results. 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...