What you'll learn:
- ANN Introduction
- ANN Model Building
- ANN Hyper parameters
- Fine-tuning and Selecting ANN models
- Shallow and Deep Neural Networks
- Building ANN Models in Python, TensorFlow and Keras
Course Covers below topics in detail
Quick recap of model building and validation
Introduction to ANN
Hidden Layers in ANN
Back Propagation in ANN
ANNmodel building on Python
TensorFlow Introduction
BuildingANNmodels in TensorFlow
Keras Introduction
ANNhyper-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
