1. We will use Flower classification dataset

a. https://www.kaggle.com/competitions/tpu-getting-started

2. Your goal is improving the average accuracy of classification.

a. You SHOULD use google collab as the main computing. (Using Kaggle is okay)

b. You SHOULD create a github reposit for the source code

i. Put a readme file for execution

c. You SHOULD explain your source code in the BLOG.

d. Try experimenting with various hyperparameters

i.

Network topology

1. Number of neurons per layer (for example, 100 x 200 x 100, 200 x

300 x 100...)

2. number of layers (For example, 2 vs 3 vs 4 ... )

3. shape of conv2d

ii.

While doing experiments, make sure you record your performance such

that you can create a bar chart of the performance

iii.

An additional graph idea might be a training time comparison

Do some research on ideas for improving this.

iv.

e. You can refer to the code or tutorial internet. But the main question you have to

answer is what improvement you made over the existing reference.

i. Make sure it is very clear which lines of code is yours or not. When you

copy the source code, add a reference.

3. Documentation is the half of your work. Write a good blog post for your work and

step-by-step how to guide.

a. A good example is

https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/

4. Add a reference

a. You add a citation number in the contents and put the reference in the separate

reference section