Deep Learning (from basics)

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Following topics are covered as part of the course

  • Explore building blocks of neural networks

    • Data representation, Tensor, Back propagation

  • Keras

    • Dataset, Applying Keras to cases studies, over fitting / under fitting

  • Artificial Neural Networks (ANN)

    • Activation functions

    • Loss functions

    • Gradient Descent

    • Optimizer

  • Image Processing

    • Convnets (CNN), hands-on with CNN

  • Text and Sequences

    • Text data, Language Processing

    • Recurrent Neural Network (RNN)

    • LSTM

    • Bidirectional RNN

  • Gradients and Back Propagation – Mathematics

Instructors: Shrirang Korde


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