• Laith Sharba

Deep learning in Python-Tensorflow and Keras


Deep learning in Python is also termed as structured or hierarchical learning. It happens to be part of the broader family of machine learning, which is based on the data representations learning. This is not the task-based algorithm and it can be semi-supervised, supervised or unsupervised. And Keras is a kind of open source neural-network collection, which is inscribed in Python. It is accomplished of running on top of the TensorFlow, and the Cognitive toolkit from Microsoft, Theano as well as PlaidML. It allows fast research with a deep neural network. It is modular, extensible and user-friendly, being designed as the portion of the research exertion of the mission ONEIROS, as well as its chief writer and the maintainer is Francois Cholet, who is from Google. He is also the writer of XCeption, which is a deep neural network model. The TensorFlow is an opensource framework, being developed by Google for running the machine learning, deep learning and various other statistical as well as predictive analytics workloads. You will find many such platforms, and like them, it's designed for streamlining the development process as well as the execution of advanced analytics apps for users like statisticians, predictive modelers, and the data scientists.

The TensorFlow can handle datasets that can be arrayed like nodes in the graphical form just like Computational nodes. Further, the edges that connect the nodes in the graph will represent the matrices or the multidimensional vectors, and these we know as Tensors. The TensorFlow programs make use of the data flow architecture, which works with the generalized intermediate results of the computations, which are ready for very large-scale parallel processing applications., and the most appropriate example is definitely the Neural Network.

The TensorFlow framework has the sets of both the low-level as well as high-level APIs. According to Google, however, you should use the high-level ones to maximum, for simplifying the pipeline development as well as application programming. However, having knowledge of the low-level APIs known as the TensorFlow Core can be very helpful for the experimentation as well as application debugging. The company has mentioned that it also gives the users the mental model of the inner workings of the machine language technology. Google has confirmed this.

TensorFlow applications can be run on the higher performance graphics processing units or the conventional CPUs as well as the TPUs, which are the Tensor processing units from Google, these are the custom device which is made to increase the speed of the TensorFlow jobs. The first TPU was being launched in the year 2016 and were used internally together with the TensorFlow for powering some of the applications as well as online amenities like RankBrain search algorithms as well as street viewing mapping technologies.

In early 2018, Google was up with the external TensorFlow task and made the second generation TPU which was made available to the Google Cloud platform members for the use in the training and running their own models of Machine learning. These workloads cost per second basis. And the Cloud TPU service too was launched as the beta version, and with only the limited quantities of the devices available for the use, and as per the Google.

TensorFlow Origin and Release

It’s just like the Google framework DistBelief, that the company used for carrying out the unsupervised type of feature learning, as well as the deep learning-based applications, which are based on very large neural networks as well as the Back-Propagation algorithm. Distbelief was first aired in the year 2012, and was a testbed for the deep learning that required advanced image as well as speech recognition, recommended engines, natural language processing as well as predictive analytics.

There is a difference between the two however, The TensorFlow was developed to work separately from the Google-owned Computing Infrastructure, and its code was quite easily portable for the outside uses. And it is now a general machine language framework for learning, and that isn't as tightly based on the Neural Network as the Distbelief used to be. Moreover, TensorFlow was designed to help faster configuration as well as to run the high-level APIs.

Google released TensorFlow in the year 2015 and as the open source technology, under the Apache 2.0 licensee. And since that time, the framework is up with a variety of adherent that goes beyond Google. As an example, the TensorFlow tooling can support the add-on modules to the AI Development and Machine learning suites from the Microsoft, IBM and various others.

At the beginning of 2017, TensorFlow reached the release 1.0.0 status. And in that version was included the specialized debugger, a Docker container images for the version 3 of the Python Programming language as well as experimental Java API. There were four more releases that were followed during the course of 2017. There was a TensorFlow lite version for the mobile, as well as embedded devices, which was introduced as the developer preview. And by February 2018, TensorFlow was up with the release of the version 1.6.0.

TensorFlow Application

The TensorFlow applications are high level, as well as highly advanced technology use, and large-scale AI undertakings in the realms of the machine learning as well as the deep learning. It empowered the Googles Rank Brain machine learning system and has been employed to make the information retrieval capabilities of the company's flagship search engine better.

Google has also made use of the framework for the apps that includes the automatic email response generation, optical character recognition, image classification, as well as the drug discovery applications that the Google worked on which the top research scholars from the Stanford University.

And TensorFlow and Keras are in Python. Hence install Python, and Pytorch now. Make also the use of the TPUs and others mentioned above for TensorFlow and Keras, and start exploring Deep Learning now. This is a must if you want to be a Data Scientist! The GPU comes for free on the Google Cloud, and hence you will not have to invest any money as well!

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