Introduction to Deep Learning & Neural Networks with Keras

What is Deep Learning?

Deep Learning is a field which comes under Machine Learning and is related to the use of algorithms in artificial neural networks. It is majorly used to create a predictive model to solve the problems with just a few lines of coding. A Deep Learning system is an extensive neural network which is inspired by the function and structure of the brain. Deep Learning is essential, especially when vast amounts of data are involved.

It creates an extensive neural network, and with the help of a large number of data, it becomes scalable and in return, improves the performance. It is beneficial especially in the case of unstructured data or the data which are unlabeled. Deep Learning can give excellent results through supervised learning or learning from labelled data.

As there are lots of data available on the internet which are generated every day and where the majority of them are unstructured, Deep Learning is becoming the next big thing in solving and dealing with these kinds of problems.

While in a situation where massive data becomes a problem to process and analyze, on the other hand, deep learning becomes better and better with more data given to it. It creates a bigger and better neural network when more data are connected in many ways creating bigger models and more computations processing. It also provides scope for better and improved algorithms, new insights, and enhanced techniques.

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What is Keras?

As of now, you already know how critical neural networks are in deep learning. There are many frameworks used to create neural networks. But at the same time, the complexity of many frameworks is becoming an obstacle to the developers. Many proposals have been made to simplify and improve the high-level APIs which are used to build neural network models, but nothing was very successful when carefully examined. To know more about Keras, Check out the article about Keras and Tenserflow.

This was when the entry of Keras framework made a big difference in the field of Deep Learning. Keras is written in the Python programming language and is one of the leading APIs for high-level neural networks. Keras supports the back-end computation engines of many neural networks.

It is also an improvement over low-level deep learning APIs. TensorFlow is an open-source for artificial intelligence library and allows developers to create large-scale neural networks with many layers. TensorFlow 2.0 has adopted Keras as their high-level API. This makes the Keras a clear winner among all other APIs of deep learning.

Principles of Keras

The primary purpose of the creation of Keras was to make it user-friendly and extendable easily at the same time. It worked with Python and was not designed for machines but human beings.

It reduces the cognitive load on developers by following the best practices. One can easily Keras for creation of new models by using standalone modules such as regularization schemes, activation functions, initialization schemes, optimizers, cost functions, and neural layers. New Functions, classes, and modules are straightforward to add. The models of Keras does not require separate model configuration files and are defined in Python code.

Models in Keras

The core data structure of Keras is the model, and there are mainly two types of models in Keras, which are Functional API Model Class and Sequential Model.

• Sequential Model: It is a model with a linear stack of a layer which is very simple to describe. In a sequential model, two dense layers are defined by the model. This makes the sequential model very less complicated in terms of coding. Only one line of coding is enough in definition of each layer such as trained model output prediction, Evaluation & Calculation of metrics and losses, training & fitting, learning process definition & compilation. The sequential Model of Keras is straightforward to use, but it is only limited to the model topology.
• Model Class with the functional API: Keras Model Class with useful API is mainly used for the creation of models which have high levels of complexity. These include models with shared layers, directed acyclic graphs (DAGs), multi-input and multi-output models, etc. Functional API provides more flexibility than a Sequential model in putting it together by first defining the layer, creating the model, compiling it and in the end, fitting or training it. Prediction and evaluation are similar as in the Sequential Model.

Keras Datasets and Applications

There are 7 Deep Learning sample datasets that one can generally find via the “keras.datasets” class. Those datasets include Boston Housing prices, MNIST fashion images, MNIST handwritten digits, Reuters newswire topics, IMDB movie reviews, and cifar100 & cifar10 small colour images.

 There are 10 Keras applications which are already pre-trained against MobileNetV2TK, NASNet, DenseNet, MobileNet, InceptionResNetV2, InceptionV3, ResNet50, VGG19, VGG16, Xception. These application models can be used by any beginner developer to fine-tune the models on a different set of classes, extract features and predict the classification of images.

Benefits of Keras

1. User-Friendly: One of the main reasons for Keras being the leader in High-level neural networks API is because of the user-friendliness.
2. Ease of Model Building and Learning: Other benefits of Keras are its ease of building models and ease of learning. It also provides strong support for distributed training & multiple GPUs.
3. Easy Integration with back-end engines: It can integrate with at least five back-end engines such as PlaidML, MXNet, Theano, CNTK, and TensorFlow.
4. Wide range of Broad adoption and production deployment options: It has support for an extensive range of production deployment options and offers the advantages of broad adoption.
5. Greater Flexibility: It also easily integrates with a lower-level of deep learning languages which enables a developer to implement anything he has built in the base language quickly. In this way, Keras offers great flexibility to the developer of Machine learning.
6. Adoption by Large Companies, Startups and Researchers: Keras is used by many large companies like Uber, Nvidia, Apple, Amazon, Microsoft, Square, Zocdoc, Instacart, Yelp, Netflix and Google among many others. Researchers at NASA and CERN have also adopted Keras as their frameworks for deep learning. It is also prevalent in startups which uses deep learning at the core of their products.
7. Easy to turn Models into Products: A developer can quickly convert his models into products because Keras supports a more excellent range of platforms than any other deep learning frameworks, including Google Cloud. It is achieved with the TensorFlow-Serving, in the browser via GPU-accelerated JavaScript runtimes such as WebDNN and Keras.js, on Android via TensorFlow Android runtime such as Not Hotdog app on iOS via Apple’s CoreML. Apple’s CoreML also provides official support for Keras.

Conclusion

This article is all about Keras and how it is being used for deep learning. We hope this article has shed some light on the principles of Keras, models in Keras and the benefits of using Keras. If you would like to know more about Machine Learning and Artificial Intelligence, check out IIT Madras and upGrad’s Advanced Certification in Machine Learning and Cloud.