Problem statement

Now is the time to design a multilayer perceptron and train it on a tabular dataset.

The dataset contains 6000 observations. Each observation has six numerical features (v0 – v5) and belongs to one of three classes (0, 1, 2).

v0	v1	v2	v3	v4	v5	class
1.218	0.345	0.855	0.309	0.428	0.299	2
0.648	0.893	0.777	1.282	-2.196	1.031	2
0.187	1.812	1.792	-0.811	0.116	-0.336	2
...	...	...	...	...	...	...

Introduction

Getting Started with Images

Image I/O and Annotations

Color Spaces and Thresholding

Convert Color Spaces, Threshold

Smoothing and Masking

Detection of Features

Image Registration

3D Vision

Getting Started with Neural Networks

Convolutional Neural Networks

Project: Create and Train a CNN for Classification

Object Detection and Semantic Segmentation

Cats vs Dogs Classification with Convolutional Neural Networks

Dataset Annotation

Final Remarks

Recognize Handwritten Digits Using a Deep Neural Network

Exercise: Create and Train an MLP

Problem statement

Observations Features and Class