Deep Learning is a branch of Machine Learning specialized in Artificial Neural Networks with multiple intermediary layers.
Neural Networks with multiple layers can approximate very complex non-linear functions. That’s why Deep Learning has been very successful in CV (Computer Visions), NLP (Natural Language Processing), and Reinforcement Learning.
Deep Learning architectures include:
- Convolutional Neural Networks
- Recurrent Neural Networks
Due to its ability to learn high-order non-linear functions, Deep Learning algorithms can easily overfit small datasets. Also, Deep Learning algorithms may be computationally expensive, even for a small amount of data.
Thus, Deep Learning is not a silver bullet. Deep Learning can overfit easily with no gain to generalize the solution for simple problems over a small dataset, making it a poor choice for this domain.
However, Deep Learning has achieved stunning results for complex problems that benefit from large amounts of data, longer training time, and intense computational power.
Reinforcement Learning for Fun 