In This Talk, We will be discussing different problems and The different focus areas of Deep Learning. This Session will focus on intermediate learners looking to learn deeper in Deep Learning. We, Will, Be taking the different Tasks and seeing which deep Neural Network Architecture can solve this problem and also learn about the different neural network architectures for the same task.

 
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Outline/Structure of the Talk

The Presentation will start with some problems or tasks at hand or the fields in which deep learning can be applied like time series predictions and then we will be going through all the architectures of the Deep Neural Networks Related to the tasks.

A very High-Level Outline of the talk:

  • Introduction To Some Deep Learning Tasks
    • Image recognition and Computer Vision
    • Time Series Prediction
    • Natural Language Processing
    • Encoding Data
  • Image Recognition and Computer Vision
    • CNN
    • R-CNN
    • Mask R-CNN
    • GANs (Generative Adversarial Neural Networks)
  • Time Series Prediction
    • RNN
    • LSTM
  • Natural Language Processing
    • RNN (Recursive)
    • Sequence To Sequence Model
  • Encoding Data
    • Auto-Encoders
    • Embeddings

Learning Outcome

The Audience will learn about the architecture of different neural networks used for different Tasks In Deep Learning.

Target Audience

Mostly some data scientists , ML enthusiasts and people who want to Enhance Their Knowledge about the different neural network architecturesSome Knowledge of neural networks.

Prerequisites for Attendees

Some Knowledge of neural networks and a basic understanding of Machine Learning will be helpful but not a necessity.

schedule Submitted 1 month ago

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