Tensorflow: Word Embeddings & Recurrent Neural Networks Overview/Description Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description Explore how to model language and text with word embeddings and how to use those embeddings in Recurrent Neural Networks. Leveraging TensorFlow to build custom RNN models is also covered.
Expected Duration (hours)
0.7
Lesson Objectives Tensorflow: Word Embeddings & Recurrent Neural Networks
Course Overview
perform text to numeric conversion using one-hot encoding
use frequency-based methods to generate word embeddings
use name prediction-based methods to generate word embeddings
identify pre-trained models for word vector embeddings
describe how to work with recurrent neurons
recognize the construction of a recurrent neural networks by unrolling recurrent memory cells
recognize the forward and backward passes while training a recurrent neural network
compare long memory cells with the normal recurrent neuron
"describe different kinds of encodings and why they are used "
describe the working of recurrent neural networks and how they differ from regular neural networks
Course Number: it_sdaidt_09_enus
Expertise Level
Intermediate