Deep Learning for humans
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Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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 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.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Deep learning library featuring a higher-level API for TensorFlow.
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
Hierarchical Attentive Recurrent Tracking
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env