Metadata-Version: 2.1
Name: paiutils
Version: 4.0.0
Summary: An artificial intelligence utilities package built to remove the delays of machine learning research.
Home-page: https://github.com/Tiger767/PAI-Utils
Author: Travis Hammond
License: UNKNOWN
Description: ![](./logo.png)
        
        Programming Artificial Intelligence Utilities is a package that aims to make
        artificial intelligence and machine learning programming easier through
        abstractions of extensive APIs, research paper implementations, and data
        manipulation.
        
        Package Features
        - Analytics
          - Plotting of data through embedding algorithms, such as Isomap and TSNE
        - Audio
          - Recording and playing
          - Volume, speed, and pitch manipulation
          - Trimming and Splitting
          - Spectrogram, Fbanks, and MFCC creation
          - Audio file conversions
        - Image
          - Simplified OpenCV Interface
        - Autoencoder
          - Trainer and Predictor
          - Trainer with extra decoder
          - VAE Trainer
        - Evolution Algorithm
          - One dimensional evolution algorithm
          - Hyperparameter tuner
        - GAN
          - GAN Trainer
          - GANI Trainer (GAN which takes provided Inputs)
          - Cycle GAN Trainer
          - Predictors
        - Neural Network
          - Trainer and Predictor
          - Dense layers that combine batch norm
          - Convolution layers that combine batch norm, max pooling, upsampling, and transposing
        - Reinforcement
          - OpenAI Gym wrapper
          - Multi-agent adverserial environment
          - Greedy, ascetic, and stochastic policies
          - Noise policies
          - Exponential, linear, and constant decay
          - Normal memory and efficient time distributed memory (for stacked states)
          - Agents
            - QAgent: Q-learning with a table
            - DQNAgent Q-learning with a neural network model
            - PGAgent: State to action neural network model (Actor) trained with
                       policy gradients
            - DDPGAgent: State to continous action space neural network model trained
                         with deterministic policy gradients
        - Reinforcement Agents
          - DQNPGAgent: Combination of a DQN and PG agent into one agent
          - A2CAgent: Advantage Actor Critic agent
          - PPOAgent: Proximal Policy Optimization agent
          - TD3Agent: Twin Delayed DDPG Agent
          - PGCAgent: Continuous variant of PGAgent
          - A2CCAgent: Continuous variant of A2CAgent
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: tf
Provides-Extra: tf_gpu
Provides-Extra: tfp
Provides-Extra: gym
Provides-Extra: pa
Provides-Extra: wv
