5.0 / 5
We will cover the following tasks in 52 minutes:
- A look at what we will achieve by the end of the course.
- A look at the development environment.
Training the Model
- Training by fitting the training data to the model.
- Validating the training with the validation set.
- Model evaluation on the test set.
Accuracy and Loss Plots
- Using pyplot to plot loss on training and validation sets.
- Plotting training accuracy and validation accuracy values from during training.
- Interpreting the plots.
Importing Tensorflow and Dataset
- Importing Tensorflow and Keras.
- Importing the IMDB dataset.
- Splitting data in training and test sets.
- A look at training examples.
Preparing the Data
- Two approaches to making all examples uniform.
- Padding the data to have uniform shape for all examples.
Building the Model
- Creating a Sequential model.
- Adding layers to our neural network model.
- Compiling the model.
- Looking at the model summary.
- Splitting training set into a partial training set and a validation set.
About the Host
I have been writing code since 1993, when I was 11, and one of the first things I remember I wrote was a database management software for a local hospital. More recently, I wrote an award winning Chatbot for a multi-billion-revenue company! In the last one year, I've been learning Machine Learning, NLP and Deep Learning and I'm very excited to share my learnings with you with the help of Rhyme.com !