• 📚Chapter1:Recurrent Neural Networks for Language modeling

    This Chapter covers the use of neural networks for sentiment analysis, starting with dense layers and embeddings, and moving to traditional and recurrent models. It explains RNNs, their math, cost functions, and real-world applications, then introduces advanced variants like GRUs and bi-directional RNNs for better sequence understanding.

      • > Table of Content

        • 1- Neural Networks for Sentiment Analysis|Video 1|Note|Slide|
        • 2 -Dense Layers and ReLU|Video 1|Note|Slide|
        • 3-Embedding and Mean Layers|Video 1|Note|Slide
        • 6-Traditional Language models|Video 1|Note|Slide|
        • 7- Recurrent Neural network |Video 1|Note|Slide|
        • 8- Application of RNN|Video 1|Note|Slide|
        • 9- Math in Simple RNNs|video 1|Note|Slide|
        • 10- Cost Function for RNNs|Video 1|Note|Slide|
        • 11- Implementation Note|Video 1|Note|Slide|
        • 12-Gated Recurrent Units|Video 1|Note|Slide|
        • 13-Deep and Bi-directional RNNs|Video 1|Note|Slide|