Kaggle offers interactive courses that will help you quickly understand the key topics of DS and ML.
The format is simple: short lessons, practical tasks, and a certificate upon completion โ all for free.
Inside:
โข basics of Python for data analysis;
โข machine learning and working with models;
โข pandas, SQL, visualization;
โข advanced techniques and practical cases.
Each course takes just 3โ5 hours and immediately provides practical knowledge for work.
tags: #ML #DEEPLEARNING #AI
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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 โ 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 โ 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
https://t.iss.one/CodeProgrammer
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๐ A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
โก๏ธ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
โก๏ธ Link to the course
tags: #Python #DataScience #DeepLearning #AI
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How a CNN sees images simplified ๐ง
1. Input โ Image breaks into pixels (RGB numbers)
2. Feature Extraction
ยท Convolution โ Detects edges/patterns
ยท ReLU โ Kills negatives, adds non-linearity
ยท Pooling โ Shrinks data, keeps what matters
3. Fully Connected โ Flattens features into meaning
4. Output โ Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically โ edges โ shapes โ objects
Pixels to predictions. That's it. ๐
#DeepLearning #CNN #ComputerVision #AI
https://t.iss.one/CodeProgrammer
1. Input โ Image breaks into pixels (RGB numbers)
2. Feature Extraction
ยท Convolution โ Detects edges/patterns
ยท ReLU โ Kills negatives, adds non-linearity
ยท Pooling โ Shrinks data, keeps what matters
3. Fully Connected โ Flattens features into meaning
4. Output โ Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically โ edges โ shapes โ objects
Pixels to predictions. That's it. ๐
#DeepLearning #CNN #ComputerVision #AI
https://t.iss.one/CodeProgrammer
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