Artificial Neural Networks(ANN) Made Easy

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

Learn ANN Model Building and Fine-tuning ANN hyper-parameters on Python and TensorFlow

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What you will learn

• ANN Introduction
• ANN Model Building
• ANN Hyper parameters

Rating: 3.8

Level: Beginner Level

Duration: 5.5 hours

Instructor: Statinfer Solutions