Guest
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: 🐦 Twitter: 📷 Instagram: This course was made possible by a grant from Google's TensorFlow team. ⭐️ Resources ⭐️ 💻 Datasets: 💻 Feedforward NN colab notebook: 💻 Wine review colab notebook: ⭐️ 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: Read hundreds of articles on programming: And subscribe for new videos on technology every day:
