Catalog ¶

NeuralNetwork Abstract and Links for each page

Basic

ReNom Basic Calculation using Auto Differentiation
This is an introduction of ReNom basic calculation using auto differentiation.

Functional Model
An introduction of how to build functional model.

Inference mode
Inference mode used for Dropout and Batch Normalization.

Auto Encoder
This tutorial introduces the auto encoder technique.

Trainer
An introduction of trainer function. Trainer function simplify training loop.

Saving and Loading Models
An introduction of saving and loading learned models.

Stochastic Gradient Descent(SGD) Settings
Effects of Stochastic gradient descent settings

Introduction to the Loss Function
Introduction to two basic loss funcions, and related activation functions

Hyperparameter Search
Hyperparameter search example, using the MNIST data

Correlation Coefficient and Coefficient of Determination
This is a simple introduction to correlation coefficients and coefficients of determination, using the Boston housing price dataset.

Precision and Recall
Precision and recall evaluation, using the “Adult Data Set.”

Weight Decay
How to use weight decay with ReNom using fully connected neural network model to mnist.

Dropout
Dropout using fully connected neural network model to mnist.

Concatenate Layer Output
Concatenate layer output using fully connected neural network model to mnist.


Preprocessing

Time Series Interpolation
Time series interpolation

Completion to numerical data and categorical data
Completion to numerical data and categorical data, using pseudo missing data

Merge the Categorical Data and the Mumeric Data
How to merge the categorical data and the numeric data using adult dataset.

Normalization and Standardization
Learning come to slow case, and prevent to use normalization and standardization.

Onehot Conversion for Categorical Data
The good point of onehot vector to learn

Preprocessing for Embedding Layers
We will explain preprocessing needed to use embedding layer on Renom.

Resize Preprocessing for Image Classification
Resize preprocessing for image classification using caltech dataset.

Zoom Preprocessing for Image Classification
Zoom preprocessing for image classification using caltech dataset

Flip Preprocessing for Image Classification
Flip preprocessing for image classification using caltech dataset

Rotate Preprocessing for Image Classification
Rotate preprocessing for image classification using caltech dataset

Shift Preprocessing for Image Classification
Shift preprocessing for image classification using caltech dataset

ColorJitter Preprocessing for Image Classification
ColorJitter preprocessing for image classification using caltech dataset

Crop Preprocessing for Image Classification
Crop preprocessing for image classification using caltech dataset


Time Series Data

Image Data

Digit Image Classification
Digit image classification problem using fully connected neural network model to mnist.

Image classifier
An introduction of Convolutional neural network and how to use GPU.

image Binary Classification
Image binary classification problem using Convolution neural network model to Caltech 101

Neural Style Transfer
Implementation of “Image Style Transfer Using Convolutional Neural Networks.”

Using Pretrained Caffe model in ReNom
Loading the weights of pretrained Caffe model into ReNom model.

Object detection using YOLO
An example of object detection


Clustering

Regression

Generative Model

TDA Basic

How to use ReNom TDA
An introduction of how to use ReNom TDA.

Search Categorical Data
An introduction of searching topology.

MNIST Dataset Mapping
An introduction of MNIST dataset mapping using ReNom TDA.

Convert Excel to CSV
An introduction of converting Excel data to csv file.

How to use ReNom TDA GUI
An introduction of how to use ReNom TDA GUI.


TDA Case Study