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Tutorials

The following tutorials are geared to help you get more out of the SignalPop AI Designer.

    • Create and Train a GPT model to learn Shakespeare
    • Create a Sequence-to-Sequence Chat-bot
    • Debug complex AI Solutions
    • Detect objects from images using Single-Shot Multi-Box Detection (SSD)
    • Detect object in a video using Single-Shot Multi-Box Detection (SSD)
    • Create a Triplet Net to learn MNIST using only 1% of the images
    • Create a Siamese Net to learn MNIST
    • Create a Neural Style Transfer
    • Create and Train a Sigmoid based Policy Gradient RL Model on Cart-Pole
    • Create and Train a Softmax based Policy Gradient RL Model on Cart-Pole
    • Create and Train a Sigmoid based Policy Gradient RL Model on ATARI Pong
    • Create and Train a Noisy-Net based Deep Q-Learning RL Model on ATARI Breakout
    • Create and Train an LSTM based Recurrent Model on Shakespeare
    • Create and Train an LSTM_SIMPLE based Recurrent Model on Shakespeare
    • Create and Train a Domain-Adversarial Neural Network
    • Create and Train the ResNet-56 on Cifar-10
    • Create and Train a Deep Convolution Auto-Encoder with Pooling on MNIST

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SignalPop makes deep learning easier for Windows Developers through its innovative products and services.

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