Tech Talk with Rowel Atienza: Is Keras the Perfect Deep Learning Library in Python?

Keras [Chollet, François. “Keras (2015).” (2017)] is a popular deep learning library with over 250,000 developers at the time of writing, and over 600 active contributors. This library is dedicated to accelerating the implementation of deep learning models. This makes Keras ideal when we want to be practical and hands-on.

Keras enables us to build and train models efficiently. In the library, layers are connected to one another like pieces of Lego, resulting in a model that is clean and easy to understand. Model training is straightforward requiring only data, a number of epochs of training, and metrics to monitor. The end result is that most deep learning models can be implemented with a significantly smaller number of lines of code.

Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. In this book, Professor Atienza strikes a balance between advanced concepts in deep learning and practical implementations with Keras.

Let’s find out why Rowel thinks it’s the perfect deep learning Library in Python:

What is Keras? Why should one prefer Keras over other libraries in Python? Why do you think Keras is the perfect deep learning library?
Rowel Atienza: Keras provides APIs for rapidly building, training, validating and deploying deep learning algorithms. It is suitable for someone who is starting in the field and also for advanced users. Keras is characterized by ease of use yet flexible enough to build complex networks especially with TensorFlow. Its tight integration with TensorFlow makes it a good choice for deep learning projects that could be deployed on production scale operations.
What are the future trends of Keras and how it is going to affect deep learning and AI in the long run? Where do you see the future of Keras with the release of TensorFlow 2.0?
RA: Beginning with TF2.0, Keras has become the primary front-end API of TensorFlow. This means that Keras will be actively developed and used in the immediate future. With the combined users base of Keras and TensorFlow, we can expect a good number of projects will be developed in Keras.
What are the key challenges in adoption of Keras in Deep Learning?
RA: Keras should attract more contributors in its library. It should act fast in adapting rapid advances in different subfields of deep learning. For example, the best implementations of graph neural networks are written in PyTorch. If PyTorch keeps on attracting serious contributors to implement new developments in deep learning, Keras and TensorFlow will lose their competitiveness.
What are your views on the statement “Keras is an essential tool in the toolbox of a data scientist working with neural networks”?
RA: In Kaggle, a good percentage of solutions are implemented in Keras. Data scientists find the appeal of a tool that helps them rapidly build, train and validate neural networks. Keras fits these requirements. Keras will remain in every data scientist’s toolbox.
How do you combine multiple models into a single Keras model?
RA: Keras is like pieces of lego. It could be modular at the layer or model level. As long as the input and output tensors of layer or model fit, they can be easily combined.
Which approach would you prefer, the design and implementation of a machine learning algorithm versus modifying an already-prepared one?
RA: I always prefer to build machine learning algorithms from what I think are suitable for the problem. Understanding the problem and finding the right solution is more important than just plugging in a start of the art solution to the problem without thinking how it works. The key is understanding the problem and the possible solution regardless whether it is built from the ground up or by modifying an existing one.