In this project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus & this could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news-related articles.
- Apply python libraries to import and visualize dataset
- Perform exploratory data analysis and plot word-cloud
- Perform text data cleaning such as removing punctuation and stop words
- Understand the concept of tokenizer.
- Perform tokenizing and padding on text corpus to feed the deep learning model.
- Understand the theory and intuition behind Recurrent Neural Networks and LSTM
- Build and train the deep learning model
- Access the performance of the trained model
Prerequisites: Python programming language, be familiar with deep learning for Natural Language Processing (NLP), and have trained models with TensorFlow or and its Keras API & TensorFlow.
The hands on project on Transfer Learning for NLP with TensorFlow Hub is divided into following tasks:
- Task 0️⃣1️⃣ Cloning the Repository
- Task 0️⃣2️⃣ Import the library and dataset
- Task 0️⃣3️⃣ Prtform the Exploratory Data Analysis
- Task 0️⃣4️⃣ Perform the Data Cleaning
- Task 0️⃣5️⃣ Visualize the CleanUp datatet
- Task 0️⃣6️⃣ Prepare the data by performing tokenization and Padding
- Task 0️⃣1️⃣ Build and Train Model
- Task 0️⃣7️⃣ Assess Trained Model Performance
