Deep Learning for humans
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Updated
Sep 19, 2024 - Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data. Data scientists perform data analysis and preparation, and their findings inform high-level decisions in many organizations.
Deep Learning for humans
scikit-learn: machine learning in Python
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Streamlit — A faster way to build and share data apps.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
💫 Industrial-strength Natural Language Processing (NLP) in Python
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
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.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Data Apps & Dashboards for Python. No JavaScript Required.
matplotlib: plotting with Python
Best Practices on Recommendation Systems
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
Topic Modelling for Humans
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
🦉 ML Experiments and Data Management with Git