Today, we look at Recurrent Neural Networks and their applications in Machine Learning, Artificial Intelligence, and Deep Learning. We have highlighted two videos uploaded in the past 24 hours that discuss the Backpropagation Through Time (BPTT) Algorithm and the Predicting Next Word in a Sequence. In these videos, they explore how to train a Recurrent Neural Network (RNN) and the design criteria for a RNN model to process sequential data. They also discuss the concept of text language conversion to numerical encoding, such as embedding, one-hot-encoding, and Neural network learned embedding.
Key Takeaways: • Backpropagation Through Time (BPTT) Algorithm for training a RNN model • Design criteria for a RNN model to process sequential data • Text language conversion to numerical encoding • Embedding, one-hot- encoding and Neural network learned embedding • Exploding gradient and vanishing gradient
Daily Recurrent Neural Networks Summary: Today, we have explored the applications of Recurrent Neural Networks (RNN) in Machine Learning, Artificial Intelligence, and Deep Learning. We have looked at the Backpropagation Through Time (BPTT) Algorithm and the Predicting Next Word in a Sequence. We have discussed the importance of text language conversion to numerical encoding, such as embedding, one-hot-encoding, and Neural network learned embedding. We invite you to scroll down and view the highlighted videos to learn more.
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