Sentiment Analysis Through Deep Learning with Keras & Python

Learn to apply sentiment analysis to your problems through a practical, real world use case.

Course curriculum

1. Birds Eye View of Deep Sentiment Analysis Preview
2. MNIST Dataset Description
3. Learning and Prediction Pipeline
1. Machine Learning Pipeline
2. Regression
3. Neural Networks - a Modular Approach
4. Recap and Supporting Talk
1. Windows installation and hurdles
2. Mac and Linux installation
3. Keras Data Preparation
4. Learning and Evaluation with Keras
1. Understanding the Sentiment Data
2. Structure of Data for Deep Learning
3. Model, Embedding and Applying to Real World
1. Basics of Convolutional Neural Networks
2. ConvNet with Keras
3. Pooling and Translation Invariance
4. Dropout and Regularization
5. Using the Functional API with CNN
1. CNN, LSTM and Other Models for Sentiment Analysis
1. Saving and Loading Model Weights
2. Parting words and future directions

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