I took a course on DataCamp where I gained practical skills in Keras, focusing on creating various models like dense, LSTM, and GRU. The course covered data preprocessing techniques for deep learning, including tokenization and the use of embedding layers. I also learned about padding text data to ensure uniform lengths for input sequences.
Additionally, the course introduced me to different classification models and how to use callback plots in Keras. These plots are valuable for identifying overfitting and determining if a neural network would benefit from more training data. I also learned about early stopping, a technique where the model halts training if monitored metrics fail to improve over a specified number of epochs.
Deep learning models often require extensive training, especially with complex architectures and large datasets. By saving models at performance peaks and utilizing early stopping, I could streamline training processes and alleviate concerns about selecting the optimal number of epochs. The ability to save and restore models enabled me to pick up training from where I left off.
Furthermore, the course delved into optimizers, loss functions, and evaluating different activation functions for model performance. I gained insights into defining optimizers and exploring their impact on model training. This knowledge was instrumental in the project I documented on GitHub, where I applied these principles. You can find more details on this project by following the link provided in my GitHub repository.