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.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Machine Learning Toolkit for Kubernetes

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Try out deep learning models online on Google Colab

This repos contains notebooks for the Advanced Solutions Lab: ML Immersion

Collection of google colaboratory notebooks for fast and easy experiments

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)

💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️

A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book

TensorFlow and Deep Learning Tutorials

Data Science Notebook on a Classification Task, using sklearn and Tensorflow.

A curated list of applied machine learning and data science notebooks and libraries across different industries.

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Start Tensorboard in Jupyter Notebook

Convolutional Neural Network for Human Activity Recognition in Tensorflow

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

📓 deepleaning and cv notes.