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.
✏️ Course created by Kylie Ying.
🎥 YouTube: [ Ссылка ]
🐦 Twitter: [ Ссылка ]
📷 Instagram: [ Ссылка ]
This course was made possible by a grant from Google's TensorFlow team.
⭐️ Resources ⭐️
💻 Datasets: [ Ссылка ]
💻 Feedforward NN colab notebook: [ Ссылка ]
💻 Wine review colab notebook: [ Ссылка ]
⭐️ Course Contents ⭐️
⌨️ (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)
--
🎉 Thanks to our Champion and Sponsor supporters:
👾 Raymond Odero
👾 Agustín Kussrow
👾 aldo ferretti
👾 Otis Morgan
👾 DeezMaster
--
Learn to code for free and get a developer job: [ Ссылка ]
Read hundreds of articles on programming: [ Ссылка ]
