Forwarded from Learn Python Coding
Here's a small fact about Python 🐍
The
It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
Without it, you would have to retrieve the value separately using
Have you ever used the
#Python #Programming #WalrusOperator #Coding #TechFacts #Python3
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The
:= operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus 🦭It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
while (line := input("Say something: ")) != "quit":
print(f"You said: {line}")Without it, you would have to retrieve the value separately using
input(), and then check it.Have you ever used the
:= operator in your code?#Python #Programming #WalrusOperator #Coding #TechFacts #Python3
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AI PYTHON 🌟
You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 15 chats.
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Forwarded from Machine Learning
OpenAI researcher Alice Liu went through 57 interviews before being hired, and then openly shared her entire preparation and job search journey.
If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.
You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.
Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms
Mathematics:
https://alisawuffles.notion.site/math-notes
Analysis of the job search and interview process:
https://alisawuffles.github.io/blog/job-search/
https://xn--r1a.website/MachineLearning9🫀
If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.
You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.
Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms
Mathematics:
https://alisawuffles.notion.site/math-notes
Analysis of the job search and interview process:
https://alisawuffles.github.io/blog/job-search/
https://xn--r1a.website/MachineLearning9
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Forwarded from Machine Learning
Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
https://xn--r1a.website/MachineLearning9
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
https://xn--r1a.website/MachineLearning9
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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✅ https://xn--r1a.website/Codeprogrammer
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If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://xn--r1a.website/CodeProgrammer
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://xn--r1a.website/CodeProgrammer
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📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics:
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
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arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
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You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.
Create your own AI agent inside Telegram in about a minute, and create small tools with it right in the chat.
▫️ describe a tool in a sentence and it writes, runs and returns the working script
▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting
▫️ drop in a traceback or a repo link and get the fix, not a lecture
▫️ swap the model per task with one command, so cheap work runs cheap
▫️ remembers your stack, your conventions and your project for months
▫️ voice in, answer back — describe the task on the way home, read the result when you are back
Setup takes a minute: open the link and name your agent.
Free to use — no card needed.
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A collection of explanations for key concepts and scientific papers in machine learning:
https://github.com/dair-ai/ML-Papers-Explained
https://github.com/dair-ai/ML-Papers-Explained
GitHub
GitHub - dair-ai/ML-Papers-Explained: Explanation to key concepts in ML
Explanation to key concepts in ML. Contribute to dair-ai/ML-Papers-Explained development by creating an account on GitHub.
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"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://xn--r1a.website/CodeProgrammer🤩
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://xn--r1a.website/CodeProgrammer
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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✅ https://xn--r1a.website/Codeprogrammer
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Forwarded from Machine Learning
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I found a great resource for interactive learning about machine learning and AI – VizLearn.
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
https://xn--r1a.website/MachineLearning9
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
https://xn--r1a.website/MachineLearning9
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