Monday, March 4, 2019

Artificial Neural Networks(ANN) Made Easy

Artificial Neural Networks(ANN) Made Easy
Learn ANN Model Building and Fine-tuning ANN hyper-parameters on Python and TensorFlow
Artificial Neural Networks(ANN) Made Easy

What you'll learn
ANN Introduction
ANN Model Building
ANN Hyperparameters
Fine-tuning and Selecting ANN models
Shallow and Deep Neural Networks
Building ANN Models in Python, TensorFlow, and Keras

Basic High School Mathematics
Basic Statistics like Mean, Median, and Variance

Course Covers below topics in detail

A 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 the basics of python coding

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

You need to have basic knowledge of 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.

The 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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