Deep Network

Deep Network

• Deep learning is a new area of machine learning research, which has been introduced with the objective of moving machine learning closer to one of its original goals. Deep learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text.

• 'Deep learning' means using a neural network with several layers of nodes between input and output. It is generally better than other methods on image, speech and certain other types of data because the series of layers between input and output do feature identification and processing in a series of stages, just as our brains seem to.

• Deep Learning emphasizes the network architecture of today's most successful machine learning approaches. These methods are based on "deep" multi- neural networks with many hidden layers.

TensorFlow

• TensorFlow is one of the most popular frameworks used to build deep learning models. The framework is developed by Google Brain Team.

• Languages like C++, R and Python are supported by the framework to create the models as well as the libraries. This framework can be accessed from both - desktop and mobile.

• The translator used by Google is the best example of TensorFlow. In this, the model is created by adding the functionalities of text classification, natural language processing, speech or handwriting recognition, image recognition, etc.

• The framework has its own visualization toolkit, named TensorBoard which helps in powerful data visualization of the network along with its performance.

• One more tool added in TensorFlow, TensorFlow Serving, can be used for quick and easy deployment of the newly developed algorithms without introducing any change in the existing API or architecture.

• TensorFlow framework comes along with a detailed documentation for the users od or to adapt it quickly and easily, making it the most preferred deep learning to do framework to model deep learning algorithms.

• Some of the characteristics of TensorFlow is:

• Multiple GPU supported

• One can visualize graphs and queues easily using TensorBoard.

• Powerful documentation and larger support from community

Keras

• If you are comfortable in programming with Python, then learning Keras will not prove hard to you. This will be the most recommended framework to create deep aid learning models for ones having a sound of Python.

• Keras is built purely on Python and can run on the top of TensorFlow. Due to its complexity and use of low level libraries, TensorFlow can be comparatively harder to adapt for the new users as compared to Keras. Users those who are beginners in deep learning, and find its models difficult to understand in TensorFlow generally prefer Keras as it solves all complex models in no time.

• Keras has been developed keeping in mind the complexities in the deep learning models, and hence it can run quickly to get the results in minimum time. Convolutional as well as Recurrent Neural networks are supported in Keras. The framework can run easily on CPU and GPU.

• The models in Keras can be classified into 2 categories:

1. Sequential model:

The layers in the deep learning model are defined in a sequential manner. Hence the implementation of the layers in this model will also be done sequentially.

2. Keras functional API:

Deep learning models that has multiple outputs, or has shared layers, i.e. more complex models can be implemented in Keras functional API.

Difference between Deep Network and Shallow Network