Throughout the course of this book, we had the good fortune to explore together an intriguing idea that populates, and currently dominates, the realm of Artificial Intelligence (AI): Artificial Neural Networks (ANNs). On our journey, we had the opportunity to get detailed insight into the functioning of neural models, including the feed-forward, convolutional, and recurrent networks, and thereby Long Short-Term Memory (LSTM). We continued our journey by subsequently exploring self-supervised methods, including Reinforcement Learning (RL) with deep Q-networks, as well as autoencoders. We finalized our excursion by going over the intuition behind generative models.
In this chapter, we will cover the following topics:
- Sharing representations with transfer learning
- Transfer learning on Keras
- Concluding our experiments
- Learning representations...
