Online Course

FSDS_3_Applied Deep Learning using Python

This Applied Deep Learning course focusses on the Deep Learning architectures used for Classification and Regression Problems..

Python Deep learning Computer vision Cnn Rnn Lstm Nlp Model deployment Time series analysis
Included in Course

  • video_library Rich Learning Content

  • assessment Quizzes & Projects

  • verified_user Taught by Industry Pros

  • forum Student Support Community

  • grade Certificate of Completion

About the Course

Module 1: Introduction to Artificial Neural Networks

  • - Introduction to Neuron & Artificial ArNeural Networks (ANN)
  • - Activation Functions, Loss Functions & Optimizers in DL
  • - ANN Training by Gradient Descent & Back-Propagation
  • - Building Feed-Forward NN using Python & Keras with TensorFlow Background
  • - Using Tensorboard

Module 2: Convolutional Neural Networks

  • - CNN Basics and mathematics.
  • - Implementing Convolutional Neural Networks using TF-Keras
  • - CNN using PyTorch
  • - Optimization of NN (Regularization, Batch Norm, Dropout etc.) & Hyperparameter Tuning
  • - Image Data Augmentation
  • - Transfer Learning in CNN (Resnet50, VGG16 etc.)
  • - Computer Vision Applications using Advanced CNN (R-CNN, Mask CNN, Fast R-CNN etc)
  • - Convolution Filter Visualizations using Saliency Maps etc.

Module 3: Recurrent Neural Networks (RNN)

  • - Recurrent Neural Networks and Vanishing Gradient Problem
  • - Sequence Learning through LSTMs & GRU Cells
  • - Applications LSTMs & GRUs to Sentiment Analysis & Text Classification using BoW and Word Embedding models
  • - Time Series Analysis 

Module 4: Model Deployment

  • - Introduction to Flask REST APIs
  • - Deployment of Deep Learning Models using Flask

Course Leads

  • Prashant Sahu
    Prashant Sahu

    Staff