Artificial Neural Networks(ANN) Made Easy


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
Who this course is for:
  • Beginners in Machine Learning
  • Beginners in TensorFlow
  • Beginners in Deep Learning
  • Data Science Aspirants
  • Computer Vision students
  • Engineering , Mathematics and science students
  • Data Analysts and Predictive Modelers


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