Machine Learning notebooks for refreshing concepts.
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Updated
Aug 24, 2021 - Jupyter Notebook
Machine Learning notebooks for refreshing concepts.
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
Various notebooks we're working on from either our blog cfe.sh/blog or as general research.
AI projects in python, mostly Jupyter notebooks.
Notes & Code to go over "Grokking Deep Learning" Book by Andrew Trask
Hola, en este repositorio encontrarás los cuadernos realizados en el canal Dot CSV.
Practical Computer Vision Bootcamp with notebooks, quizzes, and videos
Practical financial data science examples applying statistics, time series analysis, graph analytics, backtesting, machine learning, natural language processing, neural networks and LLMs
My personal notes, presentations, and notebooks on everything Deep Learning.
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
A collection of Bayesian data analysis recipes using PyMC3
An interactive HTML pretty-printer for machine learning research in IPython notebooks.
Analytics labs notebooks for Statistics and Business School students
Jupyter Notebook notes on Andrej Karpathy's videos and the tutorial series, "Neural Networks: Zero to Hero."
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
Machine Learning Engineer Nanodegree portfolio, which includes projects and their notebooks/reports.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.
Mask RCNN implementation on a custom dataset! All incorporated in a single python notebook!
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