Ex_Files_Text_Analytics_Predictions_Python_EssT.zip
112.2 KB
π
Text Analytics and Predictions with Python Essential Training
π Topics: #MachineLearning, #Python, #TextAnalytics
https://t.me/Python53
π Topics: #MachineLearning, #Python, #TextAnalytics
https://t.me/Python53
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Forwarded from Machine Learning with Python
Design patterns are proven solutions to common problems in development. If you've ever found yourself constantly writing the same thing when creating objects or struggling with managing different types of objects, then the factory pattern might be exactly what you need.
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
https://t.me/CodeProgrammer
In this tutorial:
https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/
you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks.
The code can be found on #GitHub
https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory
https://t.me/CodeProgrammer
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Ex_Files_Python_for_Data_Science_Tips.zip
4.8 MB
π
Python for Data Science Tips, Tricks, and Techniques
π Topics: #DataScience, #Python
https://t.me/Python53
π Topics: #DataScience, #Python
https://t.me/Python53
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π
Deep Learning with Python: Convolutional Neural Networks
π Gain hands-on experience building, training, and evaluating convolutional neural networks (CNNs) using Python for image classification, object detection, and segmentation.
π Topics: #CNN, #DeepLearning, #Python
π Gain hands-on experience building, training, and evaluating convolutional neural networks (CNNs) using Python for image classification, object detection, and segmentation.
π Topics: #CNN, #DeepLearning, #Python
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Python Tip: Operator Overloading
This is a very important concept in Python.
π https://t.me/Python53
This is a very important concept in Python.
Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action.ο»Ώ
Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.
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π¬ Want exam help? Chat with an admin now!
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I am proud to share a transformative journey focused on elevating our codebase to new heights of readability and long-term maintainability. By aligning our development priorities with the core pillars of functional programming, we are unlocking a powerful new chapter in our engineering evolution. π‘π
This isn't just about writing code; it's about reimagining our workflow to drive maximum impact and foster a culture of clean, sustainable innovation. Let's embrace this opportunity to optimize our technical stack and empower our team to build the future, one function at a time! ππ§ #FunctionalProgramming #Python #Innovation #GrowthMindset #TechLeadership
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Programming Foundations: Data Structures
π Learn how to effectively use and implement key data structures through hands-on exercises and real-world examples in Python.
π Author: Kathryn Hodge
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π Topics: Data Structures, Python
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π Learn how to effectively use and implement key data structures through hands-on exercises and real-world examples in Python.
π Author: Kathryn Hodge
π° Level: Beginner
β° Duration: 2h 46m
π Topics: Data Structures, Python
#DataStructures #Python #Programming #Coding #LearnToCode #Tech
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But only a few actually understand how it works under the hood. βοΈ
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
π What you will learn:
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If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini β this material is worth watching. π
π Link to the playlist:
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I came across a great find from Vizuara β a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. π§ β¨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. βοΈ
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
π What you will learn:
β The architecture of Transformer ποΈ
β The internal structure of GPT
β Tokenization and BPE π§©
β Attention mechanisms π
β The process of training an LLM π
β Full implementations in Python π
β Suitable for:
β’ ML engineers
β’ AI enthusiasts
β’ Developers entering the GenAI field
β’ Anyone who is tired of explaining AI as a "black box" π΅οΈ
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini β this material is worth watching. π
π Link to the playlist:
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