If you are working with text data using neural networks, RNNs are a natural choice for sequences. This course works through language modeling problems using RNNS - optical character recognition or OCR and generating text using character prediction.
Recurrent Neural Networks (RNN) performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell.
In this course, Language Modeling with Recurrent Neural Networks in Tensorflow, you will learn how RNNs are a natural fit for language modeling because of their inherent ability to store state. RNN performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell.
First, you will learn how to model OCR as a sequence labeling problem.
Next, you will explore how you can architect an RNN to predict the next character based on past sequences.
Finally, you will focus on understanding advanced functions that the TensorFlow library offers, such as bi-directional RNNs and the multi-RNN cell.
By the end of this course, you will know how to apply and architect RNNs for use-cases such as image recognition, character prediction, and text generation; and you will be comfortable with using TensorFlow libraries for advanced functionality, such as the bidirectional RNN and the multi-RNN cell.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview Hi, my name is Janani Ravi, and welcome to this course on Language Modeling using Recurrent Neural Networks in TensorFlow. A little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. RNNs are a natural fit for language modeling because they work very well with sequence data. An RNN's performance and its predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. The TensorFlow library has powerful built-in functions to allow us to build complex RNNs. This course applies RNNs to solve common problems in language modeling. We first build a neural network for optical character recognition, OCR. Instead of using image data in two dimensions to identify patterns, RNNs use the context in which a character occurs for OCR. That processing performance can be further improved by using bidirectional RNNs which use future data to predict current state. The second problem is one of character prediction. A trained model can be used to generate very natural-sounding sentences where every character input is used to predict the next character in the sequence. A fully trained model on technical papers can generate sentences which mimic the writing the style of those papers. This course explains the RNN architecture for these problems and implements a fully fledged neural network to find their solutions.