TensorFlow 2.0 Tutorial for Beginners 17 - IMDB Review Classification using RNN - LSTM

In this video we will learn about IMDB review classification using RNN-LSTM. Neural Networks are a set of algorithms which closely resembles the human brain and are designed to recognize patterns. Recurrent Neural Network is a generalization of a feedforward neural network that has an internal memory. RNN is recurrent in nature as it performs the same function for every input of data while the output of the current input depends on the past one computation. After producing the output, it is copied and sent back into the recurrent network. For making a decision, it considers the current input and the output that it has learned from the previous input. In other neural networks, all the inputs are independent of each other. But in RNN, all the inputs are related to each other.

The IMDB dataset contains 50,000 movie reviews for natural language processing or Text analytics. It has two columns-review and sentiment. The review contains the actual review and the sentiment tells us whether the review is positive or negative

🔊 Watch till last for a detailed description
03:13 Understanding the dataset
04:56 Dataset pre-processing
10:49 Building LSTM network
19:29 Plotting the learning curve

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