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A toy Neural Network and an introduction to Tensorflow
I’m currently exploring ChatGPT and while I’m doing this, I am revisiting common ML concepts, and exploring libraries like the tensorflow library.
For the purposes of this blogpost, I gave ChatGPT the following prompt:
Please help me write a blog post on an introduction to ML methods in python, building increasingly complex Neural Networks to analyze a toy dataset. Please find an appropriate toy dataset (with preference towards gene expression methods) and begin the blog post by installing the appropriate packages in a new conda environment with Python 3.x
Chat’s response was more than adequate:
Welcome to our blog post on an introduction to Machine Learning (ML) methods in Python, specifically focused on building neural networks for analyzing gene expression data. First things first, let's get our environment set up. Setting up the Environment To start, we're going to use Conda, a powerful package manager and environment management system. If you don't have it installed, you can download it from...
The blog post follows a fairly standard format: We generate a python 3.9 environment, and populate it with standard python libraries like pandas, numpy, matplotlib. Then we
pip install tensorflow. ChatGPT suggested a publicly available dataset (the breast cancer wisconsin diagnostic dataset), and we fit a neural network to it.
Here’s the notebook, in the jupyter notebook.