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Anaconda Assistant brings productive artificial intelligence to Python and data analysis and makes working with data easy for anyone. You can access this feature for free in your Anaconda notebook!
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This wonderful resource was launched by Quantum Stat and includes information such as article titles, abstracts, authors, and links to related articles and code repositories. You also have the possibility of quick search and connection diagrams between similar articles
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π¦Ύ Made With ML : Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.
A 100% free course that will help you learn how to write production-grade MLOps code.
The course will teach you everything from design, modeling, testing, working with learning models and much more for free!
More than 35 thousand stars on Github
Learn how to design, develop, deploy, and operate production-grade ML applications.
βͺ Course
βͺ Overview
βͺ Jupyter notebook
π More likes π β‘οΈ more posts
βοΈ http://www.tg-me.com/codeprogrammer β
A 100% free course that will help you learn how to write production-grade MLOps code.
The course will teach you everything from design, modeling, testing, working with learning models and much more for free!
More than 35 thousand stars on Github
Learn how to design, develop, deploy, and operate production-grade ML applications.
βͺ Course
βͺ Overview
βͺ Jupyter notebook
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The year 2024 is the year of cryptoπ Many coins are set to rocket by 20-100x from their current value. This is not an opportunity to be missed!
Harry shares life hacks for smart investing and selecting the top-tier projects during the bull runπ₯π₯Everyone can build significant capital, even if you're a beginner
Join my friend's channel to stay on top of the trends and not miss out on profitable projects: https://www.tg-me.com/+MCO0nfuEfW4wYTlk
Harry shares life hacks for smart investing and selecting the top-tier projects during the bull runπ₯π₯Everyone can build significant capital, even if you're a beginner
Join my friend's channel to stay on top of the trends and not miss out on profitable projects: https://www.tg-me.com/+MCO0nfuEfW4wYTlk
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I use this website that turns skills into profit.
But 99% freelancers will never tell you about this secret tool (no one likes competition)
I have been using Feedcoyote for many months. It's an ultimate Business Network & Collaboration platform for business owners and freelancers
It helps you boost your earnings and effortlessly collaborate with freelancers and solopreneurs.
Sign up now at onelink.to/qk5kkq πΌπ
But 99% freelancers will never tell you about this secret tool (no one likes competition)
I have been using Feedcoyote for many months. It's an ultimate Business Network & Collaboration platform for business owners and freelancers
It helps you boost your earnings and effortlessly collaborate with freelancers and solopreneurs.
Sign up now at onelink.to/qk5kkq πΌπ
Questions Based on Resumes
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Forwarded from Data Science Paid (Books & Courses)
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PayPal.Me
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Python | Machine Learning | Coding | R pinned Β«β
Good evening: We have launched an urgent donation campaign in order to continue our channels with the momentum you are accustomed to. Contribute if you think our work deserves thanks. π₯ BTC: bc1qgjmr3ffh48jw5vw2tqad9useumutt5tql0pa6w π² USDT: TMzAr8AFcβ¦Β»
π¨π»βπ» With support for more than 200 different mathematical operations, PyTorch is one of the most powerful open source and computational libraries based on Python for machine learning. You don't need to look for different sources to learn this popular library, but the website of this library covers more than what you need.
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Some Helpful Data Science Projects for Beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
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βοΈ http://www.tg-me.com/codeprogrammer β
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https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
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β‘οΈ Graph Machine Learning
Free advanced course: Machine learning on graphs .
The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore.
βͺ Introduction
βͺ Dive into graphs
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb
- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb
- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb
βͺ Graph clustering
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb
- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb
- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb
- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb
https://pic.twitter.com/vSXCx364pe
βͺ Lectures 4 Graph SVM
- Lab1 : Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb
- Lab2 : Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb
- Lab3 : Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb
- Lab4 : Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb
Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf
π‘ Github
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https://www.tg-me.com/DataScienceT
Free advanced course: Machine learning on graphs .
The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore.
βͺ Introduction
βͺ Dive into graphs
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb
- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb
- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb
βͺ Graph clustering
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb
- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb
- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb
- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb
https://pic.twitter.com/vSXCx364pe
βͺ Lectures 4 Graph SVM
- Lab1 : Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb
- Lab2 : Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb
- Lab3 : Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb
- Lab4 : Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb
Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf
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π¨π»βπ» In the latest data science report 2024 , Python is still the top programming language for data science with 56.7% . Here I have put a list of the best Python repositories for data science , which will improve your coding skills and guide you on the path to data science mastery.
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πThere is news that a young guy together with a team of the best programmers in Europe, invented a unique way to earn money, thanks to which everyone can earn from 300 dollars a day, having only a smartphone. π₯ Together they created a Telegram channel, a closed community, where they tell about their strategy. Training is fast and easy, absolutely everyone will master it, you do not need special skills and knowledge. πThe creator of the channel told us that he earns from 1000 dollars a day and it is absolutely real and available to everyone. He was able to change his life, drives expensive cars, travels, buys himself anything he wants and he assures that he can help to reach a high income to all those in need. βοΈNow there is a new recruitment for training in the team, all you need to do is to subscribe to his Telegram channel, hurry up, the number of places is limited.
π https://www.tg-me.com/+kA13cOZrpz4wNzI1
π https://www.tg-me.com/+kA13cOZrpz4wNzI1
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In 1989, Yann LeCun and his team trained a LeNet 1 CNN, which was able to detect handwriting.
They published a video showing how this model can read the numbers that were written manually on a piece of paper, and then the model gives the numbers electronically.
The Convolution Neural Network CNN algorithm is considered one of the algorithms that has influenced the world and we find it nowadays in many fields.
In general, everything that can be predicted from an image or video is a CNN.
Many researchers relied on this algorithm and derived many of the most famous models from it (ResNet, DenseNet, MobileNet, SqueezeNet, VGG)
There are many models that come under the name CNN
γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ γ°οΈ
π More likes π¦ β‘οΈ more posts
βοΈ http://www.tg-me.com/codeprogrammer β
They published a video showing how this model can read the numbers that were written manually on a piece of paper, and then the model gives the numbers electronically.
The Convolution Neural Network CNN algorithm is considered one of the algorithms that has influenced the world and we find it nowadays in many fields.
In general, everything that can be predicted from an image or video is a CNN.
Many researchers relied on this algorithm and derived many of the most famous models from it (ResNet, DenseNet, MobileNet, SqueezeNet, VGG)
There are many models that come under the name CNN
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A curated list of articles, models, api, code examples, courses, datasets dedicated to large language models.
This is a well- structured academic selection.
β’ Github
Other highly specialized awesome repositories dedicated to LLM:
β’ Awesome-LLM-hallucination
β’ Awesome-hallucination-detection
β’ Awesome ChatGPT Prompts
β’ Awesome ChatGPT
β’ Awesome Deliberative Prompting
β’ Instruction-Tuning-Papers
β’ LLM Reading List
β’ Reasoning using Language Models
β’ Chain-of-Thought Hub
β’ Awesome GPT
β’ Awesome GPT-3
β’ Awesome LLM Human Preference Datasets
β’ RWKV-howto
β’ ModelEditingPapers
β’ Awesome LLM Security
β’ Awesome-Code-LLM
β’ Awesome-LLM-Compression
β’ Awesome-LLM-Systems
β’ Awesome-LLM-Healthcare
β’ Awesome-LLM-Inference
β’ Awesome-LLM-3D
β’ LLMDatahub
β’ Language models for Russian language
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Acquired global fresh database Please send me a sample Scammer please don't waste your time @yuefu666
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India's Largest Free Webinar on LLMs especially focused on the recently released LLAMA-3 by Meta.
How do you use these models?
How can you create apps with them?
Join our free workshop on to learn how to use Llama 3 and create apps with it.
Register here: https://www.buildfastwithai.com/events/llama-3-deep-dive
You can connect with Founder;
https://www.linkedin.com/in/satvik-paramkusham/
This Event is especially designed for people interested in the field of AI, ML, GenAI & LLMs.
How do you use these models?
How can you create apps with them?
Join our free workshop on to learn how to use Llama 3 and create apps with it.
Register here: https://www.buildfastwithai.com/events/llama-3-deep-dive
You can connect with Founder;
https://www.linkedin.com/in/satvik-paramkusham/
This Event is especially designed for people interested in the field of AI, ML, GenAI & LLMs.