Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
Published: Jun 15, 2022
This course will give you an introduction to machine learning concepts and neural network implementation using Python and TensorFlow. Kylie Ying explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews.
This course was made possible by a grant from Google’s TensorFlow team.
- Datasets: https://drive.google.com/drive/folder…
- Feedforward NN colab notebook: https://colab.research.google.com/dri…
- Wine review colab notebook: https://colab.research.google.com/dri…
- (0:00:00) Introduction
- (0:00:34) Colab intro (importing wine dataset)
- (0:07:48) What is machine learning?
- (0:14:00) Features (inputs)
- (0:20:22) Outputs (predictions)
- (0:25:05) Anatomy of a dataset
- (0:30:22) Assessing performance
- (0:35:01) Neural nets
- (0:48:50) Tensorflow
- (0:50:45) Colab (feedforward network using diabetes dataset)
- (1:21:15) Recurrent neural networks
- (1:26:20) Colab (text classification networks using wine dataset)