Artificial Neural Networks(ANN) Made Easy

Learn ANN Model Building and Fine-tuning ANN hyper-parameters on Python and TensorFlow
Instructed by: Statinfer Solutions | Subject: Teaching & Academics , Science

Artificial Neural Networks(ANN) Made Easy

About This Course

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

Outcome

  1. ANN Introduction
  2. ANN Model Building
  3. ANN Hyper parameters
  4. Fine-tuning and Selecting ANN models
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Course Info

Artificial Neural Networks(ANN) Made Easy Artificial Neural Networks(ANN) Made Easy Reviewed by Acamig Courses on March 27, 2019 Rating: 5
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