https://github.com/huonglarne/self-study-deep-learning

Self studying Neural Network construction with Tensorflow
https://github.com/huonglarne/self-study-deep-learning

deep-learning neural-network neural-networks tensorflow

Last synced: 2 months ago
JSON representation

Self studying Neural Network construction with Tensorflow

• Host: GitHub
• URL: https://github.com/huonglarne/self-study-deep-learning
• Owner: huonglarne
• Created: 2020-10-16T01:45:07.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-01-04T11:35:08.000Z (over 5 years ago)
• Last Synced: 2025-04-14T04:56:50.123Z (about 1 year ago)
• Topics: deep-learning, neural-network, neural-networks, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 3.75 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Self study Deep Learning

Self studying neural network construction with Tensorflow. The textbook I am using is Deep Learning with Python by François Chollet.

## Chapter 2

Study the operations behind neural networks, including matrix operations, gradient descent and back propagation.

Code: multiclass classification.

Dataset: MNIST hand written digits.

## Chapter 3

Study the core components of a neural network including basic activation functions, optimizer, loss functions and validation metrics.

Learn about overfitting and how to fix it using k-fold cross validation and analyzing the metrics.

Code: binary classification and regression

Dataset: IMDB sentiment and Boston housing prices.