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Artificial Neural Networks(ANN) Made Easy

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Course Covers below topics in detail

  • Quick recap of model building and validation

  • Introduction to ANN

  • Hidden Layers in ANN

  • Back Propagation in ANN

  • ANN model building on Python

  • TensorFlow Introduction

  • Building ANN models in TensorFlow

  • Keras Introduction

  • ANN hyper-parameters

  • Regularization in ANN

  • Activation functions

  • Learning Rate and Momentum

  • Optimization Algorithms

  • Basics of Deep Learning

Pre-requite for the course. 

  • You need to know basics of python coding

  • You should have working experience on python packages like Pandas, Sk-learn

  • You need to have basic knowledge on Regression and Logistic Regression

  • You must know model validation metrics like accuracy, confusion matrix

  • You  must know concepts like over-fitting and under-fitting

  • In simple terms, Our Machine Learning Made Easy course on Python is the pre-requite.

Other Details

  • Datasets, Code and PPT are available in the resources section within the first lecture video of each session.

  • Code has been written and tested with latest and stable version of python and tensor-flow as of Sep2018