Deep learning has brought in a revolution in our daily lives, and we mostly remain unaware of its scope. Since it is a subset of machine learning (ML), deep learning is based on the models of neural networks found in human brains and takes heavy references in terms of neurons and nodes. Therefore, it is called an artificial neural network. From voice assistants to self-driving cars, deep learning has contributed in numerous ways to shape the reality around us. Such growing popularity of deep learning has increased the demand for the Tensorflow course among engineers looking to make a career in machine learning.
Going by its definition, deep learning refers to the robust set of techniques that can be used to learn and leverage the most out of artificial neural networks. They can be widely helpful in providing the best solutions to problems in image/speech recognition, natural language processing, and more across several industries, from automobiles to finance, and healthcare to digital marketing.
Mathematically, many relevant libraries can ensure that necessary calculations take place properly, irrespective of their complexity. And, these libraries are:
- sci-kit learn
- Microsoft Cognitive Toolkit (CNTK)
What is Keras?
Keras is an open-source neural network library written in Python and comes with several functionalities from TensorFlow, Theano, and CNTK. Developed by a Google engineer, François Chollet, it is known for being fast and simple when it comes to building any deep learning algorithm.
Essential for quick prototyping of state-of-the-art research into production, Keras is known for its snappy debugging features and seamless working with Python tools. A network built-in Keras is simple and can be optimized according to user-defined scenarios for achieving actionable results. Its modular composition connects it to configurable building blocks with fewer limitations. Its overall flexibility and extendable features help design new custom blocks to put in new research in new layers, loss functions, and more.
Unlike any other machine learning library, Keras provides a distinct, singular API that can work across other machine learning frameworks for hassle-free operation. However, there is no hard and fast rule to using Keras in building a neural network. But, for systems with particular usage, Keras can help in better control and tracking.
Here are some of the additional features of Keras:
- It is driven by user experience.
- It can provide multi-backend and multi-platform.
- It enables the easy creation of data models.
- It comes with convolutional and recurrent network support
- It is expressive and perfect for innovative research in the field.
What is TensorFlow?
TensorFlow (TF) is also an open-source machine learning library that can be used to carry out calculations required to perform differentiable programming. Developed by Google in 2015, TF is a newbie in deep learning and has garnered massive popularity because of its user-friendly and simple APIs. TF can be observed widely in an application for face, handwriting recognition technology.
TF is comprehensively written in Python and offers several concepts that can be chosen according to your preference for the deployment of machine learning models. Generally preferred for conducting bulk tasks in machine learning, you can implement the Distribution Strategy API to perform distributed hardware configurations. There are no limit-nodes in a graph that can work across the distributed network and rely on the machine’s CPU and GPU.
Some of its features include:
- TF’s multiple abstraction levels help in the easy creation and training of models in the neural network.
- TF can also help achieve flexibility across different topologies and uses the Keras Functional API and Model Subclassing API.
Keras vs. TensorFlow
|Keras is a high-level API that wraps around the functionalities of other machine/deep learning libraries like TensorFlow, Theano, and CNTK. ||TF is a framework that offers both low-level and high-level APIs.|
|Keras can be easy to implement for someone adept in Python.||For TF, one needs to understand and learn the specific syntax of the TF function.|
|Keras is mainly used for seamless integrations.||TF is perfect for deep learning research in complex networks.|
|It can use other API debugging tools like TFDBG.||TF uses Tensor board visualization tools for debugging.|
|Keras is written in Python and can run on the top of Theano, TensorFlow, and CNTK.||TF is written entirely in C++, CUDA, and Python.|
|Keras is known for its comfortable architecture and user-friendly operation.||TF frameworks require complex debugging.|
|Keras is generally used for smaller datasets.||TF is widely used for massive datasets and high-performing models.|
|In Keras, community support is limited.||TF provides larger community support because of its extensive usage in the tech industry.|
Benefits of Using TensorFlow
Several advantages come with the usage of TensorFlow. And, they are:
- It can provide enhanced functionality that is advanced in conducting complex operations like threading and queues.
- It can help you have better control over networks, especially the ones involving weights or dynamic gradients.
- It can help in the execution of the subpart of a graph and acquire discrete data.
- It provides faster data compilation time than other frameworks and inherently differentiation capabilities.
Benefits of Using Keras
Keras is closely linked to TensorFlow because it wraps around several functionalities of TF, Theano, and other libraries. The advantages include:
- Keras helps in lowering the frequency of user actions needed for different use cases.
- It yields actionable feedback over user error with its consistent and simple user experience.
- It facilitates easy and quick prototyping.
- It also helps in building new layers, metrics, and high-yielding models.
It is not entirely accurate to compare Keras with TensorFlow since the former runs on the top and wraps around the latter’s functionalities. There is no one-size-fits-all solution to such scenarios as different tasks require different approaches and frameworks. Here is a brief insight into what we recommend:
For Development – Choose according to preference.
For advanced Ph.D. students – TensorFlow
For a beginner – Keras
For someone not familiar with Python – Keras
However, if you are an aspiring machine learning professional wanting to do more in the industry, then with the right Tensorflow course, you can learn more about the best use-case and libraries to choose from in real-time.