The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
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🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
- deep-learning
- deep-learning-tutorial
- pytorch
- gan
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
TranslatedAiLearning: Data Analysis + Machine Learning in Action + Linear Algebra + PyTorch + NLTK + TF2
- fp-growth
- apriori
- mahchine-leaning
- naivebayes
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Pocket Flow: Codebase to Tutorial
TranslatedPython open-source project "The Road to Self-Taught Programming", with step-by-step tutorials for beginners: AI Lab, treasure videos, data structures, study guides, hands-on machine learning, hands-on deep learning, web crawlers, big-company interview experiences, programmer life, resource sharing.
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
Deep Learning Tutorial notes and code. See the wiki for more info.
A 100-day challenge exploring IoT and embedded systems using ESP32, ESP8266, and Raspberry Pi Pico with MicroPython. Each day covers a new sensor or module with complete code, circuit diagram, and explanation.
Train and Deploy an ML REST API to predict crypto prices, in 10 steps
Application of asyncio to hardware interfaces. Tutorial and code.
- micropython
- asyncio
- embedded
Bringing thousands of learners together to unpack DeepInfer through peer-reviewed explanations, documentation, and hands-on examples.