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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars.
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We gain useful knowledge —
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🤖 Best GitHub repositories to learn AI from scratch in 2026
If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:
1) Karpathy – Neural Networks: Zero to Hero
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero
2) Hugging Face Transformers
The main library of modern NLP/LLM: models, tokenizers, fine-tuning
https://github.com/huggingface/transformers
3) FastAI – Fastbook
Practical DL training through projects and experiments
https://github.com/fastai/fastbook
4) Made With ML
ML as an engineering system: pipelines, production, deployment, monitoring
https://github.com/GokuMohandas/Made-With-ML
5) Machine Learning System Design (Chip Huyen)
How to build ML systems in real business: data, metrics, infrastructure
https://github.com/chiphuyen/machine-learning-systems-design
6) Awesome Generative AI Guide
A collection of materials on GenAI: from basics to practice
https://github.com/aishwaryanr/awesome-generative-ai-guide
7) Dive into Deep Learning (D2L)
One of the best books on DL + code + assignments
https://github.com/d2l-ai/d2l-en
Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.
#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
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If you work with AI, agents, or APIs, this is the foundation that developers in top companies are currently going through.
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https://anthropic.skilljar.com/claude-with-the-anthropic-api
https://anthropic.skilljar.com/introduction-to-model-context-protocol
https://anthropic.skilljar.com/claude-in-amazon-bedrock
https://anthropic.skilljar.com/claude-with-google-vertex
https://anthropic.skilljar.com/model-context-protocol-advanced-topics
https://anthropic.skilljar.com/claude-code-in-action
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Kaggle offers interactive courses that will help you quickly understand the key topics of DS and ML.
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📱Cheat sheet on string methods in Python
1. Makes the first letter capitalized
.capitalize()
.lower()
.upper().center(10, '*')
.count('0').find()
.index()
.replace()
.split().isalnum()
.isnumeric()
.islower()
.isupper()
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Data scientists are in high demand right now: there's just too much data to analyze.
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These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth
Lecture 1 - Transformer: https://lnkd.in/dGnQW39t
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Stop using if obj == None, use if obj is None
In Python, when you write:
obj == None
obj is the value None. Instead, you're asking if the object is equal to None.obj is None
obj == None
__eq__ method on the object. That is, the object itself decides what it means to be "equal to None". And this method can be overridden.obj is an instance of a class in which __eq__ is implemented so that when compared with None, it returns True (even if the object is not actually None), then obj == None may mistakenly give True.class Weird:
def __eq__(self, other):
return True # Always asserts that it's equal
obj = Weird()
print(obj == None) # True
print(obj is None) # False
obj == None returns True due to the custom behaeqf the __eq__ operator in the class.obj == None, the result is not always predictable.obj is None
is operator, which cannot be overridden. This means that the result will always be the same and predictable.is operator checks the identity of objects, that is, whether two references point to the same object. Since None is a singleton (the only instance), obj is None is the correct and most efficient way to perform such a check.obj is None instead of obj == None for predictability and efficiency.
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🔖 An excellent resource for learning about neural networks
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Find and use it — https://mlu-explain.github.io/neural-networks/
tags: #AI #ML #PYTHON
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👍 A fresh deep learning course from MIT is now available publicly
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https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
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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.
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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.
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🐱 5 of the Best GitHub Repos
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Neural Networks: How They Learn and Predict
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9 key concepts of artificial intelligence, explained in 7 minutes
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