Recurrent Neural Networks: Language Detection

This lecture covers the application of Recurrent Neural Networks (RNN) for language detection, where the final hidden activity depends on the whole phrase to predict the language. The instructor demonstrates how to preprocess sentences for language detection using one-hot encoding and joint training with gradient descent. The lecture also discusses the fitting of MNIST data with Convolutional Neural Networks and the use of Tree-Based Methods like Decision Trees and Ensembles. Additionally, it explores the concept of supervised learning through the Big Picture, emphasizing the importance of well-tuned models for optimal performance.