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Machine Learning with Python
23 сент., 01:22
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Machine Learning with Python
18 сент., 16:15
переслано из @pythonre
Here's a small fact about Python 🐍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✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2ATelegramAI PYTHONYou’ve been invited to add the folder “AI PYTHON 🌟”, which includes 15 chats.Machine Learning with Python pinned a photo
18 сент., 06:02
Machine Learning with Python
18 сент., 06:02
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17 сент., 09:23
Machine Learning with Python
17 сент., 05:32
Machine Learning with Python
15 сент., 19:50
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15 сент., 05:42
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13 сент., 20:02изменён
If you fully understand this article, you will understand inference better than 90% of people.And by the way, this is just the first material in the AI Performance Engineering repository.It's scary to think how much knowledge is contained in the rest.https://github.com/wafer-ai/gpu-perf-engineering-resources
Machine Learning with Python
13 сент., 17:31
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Machine Learning with Python
13 сент., 05:23
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12 сент., 09:29
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12 сент., 08:51изменён
😰 "Python for Data Science" Cheat Sheet👨🏻💻 This file is a "comprehensive cheat sheet" for data scientists. Whenever you forget how to join data or customize a chart while coding, just refer to it.⬅️ Chapter 1: All NumPy functions for creating arrays and broadcasting.⬅️ Chapter 2: Everything about Pandas, from selecting rows and columns (loc/iloc) to handling time series.⬅️ Chapter 3: A complete catalog of charts (scatter plots, bar charts, histograms, pie charts).⬅️ Chapter 4: The golden section! A summary table listing all the important commands in one place.https://t.me/CodeProgrammerMachine Learning with Python
12 сент., 08:43
🎓 Deep Learning for Images with PyTorch: CNNs to GANs #Data_Science #DataCamp 🏫 Platform: DataCamp 🆓 100% FREE ━━━━━━━━━━━━━━━━━━━━ 📝 Course Details: This advanced computer vision course delivers a hands-on exploration of PyTorch across all major visionMachine Learning with Python
12 сент., 08:38
переслано из @courses27
🎓 Deep Learning for Images with PyTorch: CNNs to GANs#Data_Science #DataCamp🏫 Platform: DataCamp 🆓 100% FREE━━━━━━━━━━━━━━━━━━━━ 📝 Course Details: This advanced computer vision course delivers a hands-on exploration of PyTorch across all major vision tasks. From Convolutional Neural Networks (CNNs) for image classification to advanced segmentation masks and Generative Adversarial Networks (GANs), it prepares practitioners for complex computer vision engineering tasks. Who It's For Advanced PyTorch practitioners, computer vision engineers, and machine learning research engineers seeking deep technical expertise in image processing and synthesis. Key Takeaways • CNNs & Object Detection: Train CNNs for binary and multi-class classification, leverage pre-trained models, and evaluate object detection using bounding boxes. …📢 Channel: https://t.me/Courses27
Machine Learning with Python
12 сент., 08:37
переслано из @courses27
🎓 Deep Learning for Text with PyTorch: NLP & Transformers#Data_Science #DataCamp🏫 Platform: DataCamp 🆓 100% FREE━━━━━━━━━━━━━━━━━━━━ 📝 Course Details: Natural Language Processing has undergone massive advancements, and this advanced PyTorch course takes learners through the evolution of text modeling. Moving from standard tokenization and RNNs to modern Transformer architectures and attention mechanisms, it delivers a comprehensive blueprint for deep NLP application design. Who It's For Advanced machine learning engineers and NLP specialists who want to master PyTorch text preprocessing, recurrent networks, Transformers, and transfer learning. Key Takeaways • Text Preprocessing & Encodings: Master tokenization, stemming, lemmatization, One-Hot, Bag-of-Words, and TF-IDF encodings for neural networks. …📢 Channel: https://t.me/Courses27
Machine Learning with Python
12 сент., 05:38
переслано из @machinelearning9
This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.https://github.com/Nicolepcx/transformers-the-definitive-guidehttps://t.me/MachineLearning9 🤩
Machine Learning with Python pinned Deleted message
12 сент., 03:45
Machine Learning with Python
11 сент., 16:08
🧲 Your agent writes the tool. You keep the terminal closed.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 backSetup takes a minute: open the link, start theMachine Learning with Python
11 сент., 06:07изменён
Professor Steve Branton from the Mechanical Engineering Department at the University of Washington has uploaded a complete course on control theory for master's and doctoral students to YouTube. It's free.The course is called Control Bootcamp.It covers topics such as linear systems, stability and eigenvalues, controllability and observability, pole placement, the Kalman filter, LQR/LQG, robust control, and MPC – all explained sequentially with examples in Matlab.Branton is the Boeing Professor of AI & Data-Driven Engineering at the University of Washington. He holds a bachelor's degree in mathematics from Caltech, with a specialization in control and dynamical systems, and a Ph.D. in mechanical and aerospace engineering from Princeton.Playlist: https://youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m
