Artificial Neural Networks(ANN) Made Easy
- 4.2
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Brief Introduction
Learn ANN Model Building and Fine-tuning ANN hyper-parameters on Python and TensorFlowDescription
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
Requirements
- Requirements
- Basic High School Mathematics
- Basic Statistics like Mean, Median and Variance
Statinfer Solutions
4.2 Raiting
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