In this rapidly developing world, new technologies also become older within a couple of years. To cope up with this competitive world, keep chasing that flying feather.
TensorFlow can never be a new story to you as it was that word that gave you confidence when used in your code. But everything would have been fine until the moment you realized that it wasn’t just a word, but a board kept in front of the hole dug to the core of the earth because it doesn’t have any end when you get to know more about it. Everyday people wake with new ideas, and maybe you will become one of them after getting deep into our concepts.
Let’s get started with a quick synopsis of this article
- What is Tensor Flow?
- Why TensorFlow?
- Advantages of TensorFlow
- TensorFlow: “Am I the only one?”
- “I fit well here.”
- Conclusion
What is TensorFlow?
Google made an open-source library that is widely used by deep learning and neural networks. TensorFlow is an open-source library for numerical computation using data flow graphs to build models, making work faster and easier. The Google Brain team created this library as Google’s TensorFlow.
therefore, It provides excellent technology resources to facilitate the instantaneous computing phase across various platforms, ranging from desktops to network stacks, smart devices, and sensor nodes. it bundles machine learning and deep learning algorithms and models, which makes them useful.
The so-called TensorFlow framework has used Python to construct the front-end API and execute using high-level C++. That is, Python and C++ are significant contributors to it. Keras uses TensorFlow as its backend as it allows us to call functions quickly rather than using several code lines.
also, TensorFlow takes over many projects like image recognition, handwritten digit recognition, natural language processing, etc. therefore, In all these projects, it plays a significant role in training the deep neural networks and enables prediction for the trained model. (Sunith Shetty,2018)
Tensorflow cases like
- Image recognition
- Voice recognition
- Text-based application
- Time series
- Video detection.
Why is Tensorflow used?
Tensorflow was and is a framework that has been used by thousands of programmers in the early days. As it is an attractive word, people have always tempted towards its accessibility. As soon as it entered, various forums like Github and Quora enjoyed various repository contributions with real-time projects and applications.
Tensorflow kicked out all the frameworks and stands first among the best frameworks. Frequent updates made it a framework that is created once in a blue moon. Most evidently, its flexibility helps us a lot in defining our model in our style. That is why researchers use it as it is compatible with the user’s requirement. (Abadi et al., 2016)
Advantages of TensorFlow:
To begin with, it is a low-level library because of which it is flexible to use. It enables us with a readable and accessible syntax, which efficiently uses the programming requirement. Since it is a low-level library, it provides extraordinary performance, whereas other neural networks frameworks help in advanced purposes.
Tensorflow allows us to track or follow up on the neural network changes, which show how network conscious it is. It helps developers to study the operations across the network. it is user friendly and easily accessible as it has a high-level API. They are comfortable because it has provided us with various pre-built functions and advanced operations in building neural networks. (Shazeer et al., 2018)
TensorFlow: “Am I the only one?”
TensorFlow is a library with various other libraries to provide solutions for problems in particular areas. To say, generally, TensorFlow is use in deep neural networks. But when it comes to smart devices and embedded devices, it is Lite comes to play. thus, To be brief, Tensorflow is a library used to enhance Machine learning facilities in mobile devices.
TensorFlow.js is use to run existing machine learning models in the browser using JavaScript. Even new models can be built and trained in PythonPython can be convert and develop into JavaScript. however, TensorFlow hub: Here in this library, a new model uses a reusable model using machine learning models(De G Matthews et al., 2017)
This list will keep going until human needs are satisfied, which is an impossible scenario.
“I fit well here,”:
it is the framework use in deep neural networks. When it comes to external neural networks, they don’t provide proper model accuracy. But when deep neural networks are in the role, they use many neural networks and can find the hidden untrained structures in the unlabeled and unstructured datasets.
Backpropagation is applied, and the model will train its neural networks eventually. Even it goes well with Machine learning in building and training the dataset quickly.
Conclusion:
The conversation doesn’t end here. It keeps developing, and we will keep chasing that unstrung kite toward the sky. To summarize, TensorFlow is nothing but a framework that eases our deep neural networks and machine learning tasks.
Thousands of researchers and developers have experienced significant improvement in their models after using TensorFlow because of its efficient support in the front-end API. hence, It is a very flexible and easily accessible library for numerical computation. Keep learning, keep exploring, keep working.
References:
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. and Kudlur, M., 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).
De G. Matthews, A.G., Van Der Wilk, M., Nickson, T., Fujii, K., Boukouvalas, A., León-Villagrá, P., Ghahramani, Z. and Hensman, J., 2017. GPflow: A Gaussian process library using TensorFlow. The Journal of Machine Learning Research, 18(1), pp.1299-1304.
Shazeer, N., Cheng, Y., Parmar, N., Tran, D., Vaswani, A., Koanantakool, P., Hawkins, P., Lee, H., Hong, M., Young, C. and Sepassi, R., 2018. Mesh-TensorFlow: Deep learning for supercomputers. In Advances in Neural Information Processing Systems (pp. 10414-10423).
written by: Saarika R Nair
reviewed by: Rushikesh Lavate
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