TensorFlow Tutorial: From Introduction to Applications

  • Neelam Tyagi
  • Mar 23, 2020
  • Deep Learning
  • Machine Learning
TensorFlow Tutorial: From Introduction to Applications title banner

The Google brain team initiated a project in 2011 to discover the services of very-giant-scale deep neural networks for both research and deployment in Google’s product to increase performance. 

 

TensorFlow is the successor of DistBelief, as part of early work, Google built DistBelief that was a first generation scalable distributed training and inference system. It can utilize computing clusters with thousands of machines to train large models. 

 

Google performed many projects on that including unsupervised learning, language representation, image and object detection models, face and speech recognition, reinforcement learning, etc. 

 

But, based on the experiments with DistBelief and a complete insight of the expedient system properties and needs for training and using neural networks, Google built TensorFlow

 

Tensorflow is a second generation system for the performance and advancement of large scale machine learning models. Amidst the topmost machine learning systems, TensorFlow is an open-source library software developed by Google that is used to enforce machine learning and deep learning practices.

 

In this tutorial, you will discover the

 

  1. What is TensorFlow?

  2. Its features and architecture 

  3. Benefits and its difference with other machine learning software

  4. Its applications 

 

What is TensorFlow?

 

TensorFlow is an open source platform for machine learning, it has an all-inclusive, flexible ecosystem of tools and techniques, libraries and community resources. 

 

  • It sketches the nodes of dataflow graphs over numerous devices in a group and within a device over many computational devices, covering CPUs, GPUs and Tensor Processing Units (TPUs).

 

  • It allows developers to experiment with modern training and optimization algorithms as it holds an array of applications that entirely concentrates on training and interface on deep learning models. 

 

  • It was developed to make the computing stack obvious and speedier for a broad bandwidth of machine learning and deep learning applications. It is an open end-to-end library for symbolic math that governs different programming. 

 

  • It is regularly used when building neural networks that satisfy the process of collecting the data needed to train models. It is the foundation library that makes deep learning models directly or through wrapper libraries to simplify the process designed on TensorFlow.

 

  • It has high scalability to facilitate different types of high-level numerical computation that is required to train deep learning models for high scale machine learning applications. It functions tasks in the convenient language “Python” and executes them in “C++”.

 

You may understand TensorFlow more in depth from the official video from the official TensorFlow youtube channel.



Key features of TensorFlow;

 

  1. Functions precisely with mathematically expressions incorporating multi-spatial arrays.

  2. High calibration of complex computation over machines and large datasets.

  3. Avail assistance to deep neural network and machine learning theories.

  4. It executes computation using data flow graphs;

    1. Nodes present in the graph depicts mathematical operations.

    2. Graph edges show the multi-dimensional arrays or tensors that move between them.

 

Let TensorFlow divulge its own memoir itself !!!! 

 

TensorFlow: Data-Flow Graph and Ecosystem

 

Tensorflow numerical computation is explained in the nitty-gritty of data-flow and operations in the structure of a conducted graph, let us gain how nodes and edges work in data flow;

 

  • Node: It functions computation and contains zero or more inputs and outputs. Data flows medially avowed as tensors, i.e multi-dimensional arrays of real values.

 

  • Edges: It exemplifies the flow of data, branching, looping, and upgrading to current condition. Specific edges (like normal and special) can be used to adjust the variations within the graph. Edges are the carriers of data structure aka tensors.

 

  • Operations: An admitted abstract computation that seizes input traits and bears output traits. Basically, an operation manages the tensors present in data flow.

 

You can read here more about data flow graphs here.

 

Understand elements of TensorFlow;

 

  • Tensor: It is termed TensorFlow as it accounts input as multi-dimensional arrays, also called Tensors. All the computations involve tensors. A tensor is a vector or matrix of n-dimensions that depicts all types of data.

 

  • Graphs: A sort of flowchart or set of computation of various operations is built that execute on input. The input enters from one end, moves through various operations and exits from the other end as output. All the operations take place inside a graph, each operation is termed as “op node” and linked with each other.

 

 

Ecosystem of TensorFlow

 

It incorporates multiple tools and libraries to help and work more effectively;

 

  1. TensorBoard: is an array of web applications for examining, visualizing and knowing the TensorFlow graphs and execution. It can be used to view TensorFlow graphs and zoom in for the details of graph subparts. Its utilization involves plotting of loss and accuracy during the training run, visualizing histogram of how tensor varies across time, depicting extra data like images, comparing training runs, and runtime stats, total memory usage, tensor shapes, etc.  

 

  1. Datalab: Jupyter notebooks are convenient to explore data, explain TensorFlow models and force training runs. Google Cloud Datalab gives a Jupyter-based workspace with all tools like BigQuery for datasets, Apache Beam for data processing, and others like NumPy, Pandas, Scikit-learn, Matplotlib, etc.

 

  1. Facets: is a recently released open source tool for data visualization that assists in discerning ML datasets and earns the idea of the shape and characteristics of each attribute and observes the pattern and interactions with each other. (Find more here)

 

 

TensorFlow: Benefits

 

Being a Google develop app, TensorFlow possesses multiple benefits, some of which are discussed below;

 

  1. The biggest benefit TensorFlow furnishes for ML is “Abstraction”, instead of negotiating with the details of implementing algorithms, or identifying meaningful ways to attach the output of one function into the input of another, the user can concentrate on the complete logic of application. 

 

  1. TensorFlow proffers extra conveniences for users who attempt to debug and achieve scrutiny into TensorFlow applications. It has the mode “Eager Execution” that examines and modifies each graph operation isolatedly and clearly rather than building the entire graph as a single object and evaluating it all at once. Similarly “TensorBoard” visualization helps in inspecting and arranging the graphs execution in the web-based interactive dashboard. 

 

  1. TensorFlow attains many advantages from the backing of Google. Google has sustained pace of development back the project and makes many imperative contributions around TensorFlow that makes it straightforward to deploy and use. These are an “online hub” for sharing models developed with the framework and in-browser and mobile-friendly incorporation of the framework.

 

 

TensorFlow: Competitors

 

TensorFlow contends with a ream of many machine learning frameworks such as PyTorch, CNTK, MXNet, these are the dominant abundance that fuel the same need. Below, it has mentioned the conditions where they are inconceivable against TensorFlow;

 

  1. PyTorch, with the benefit of being developed with Python, has many similarities to TensorFlow; hardware-stimulated elements under the cowl, an efficiently engaged development model that permits for design as required per terms, and many beneficial elements inclusive. It is apparently a better preference for quick development of projects that need to be run in less time, instead TensorFlow prevails for huge projects and more convoluted assignments.

 

  1. CNTK, being the Microsoft Cognitive Toolkit, operates many neural networks tasks faster and incorporates deep chunks of API( C, C++, Java, Python). But CNTK is not as convenient to learn and deploy as TensorFlow. Instead, TensorFlow uses graph format to explain dataflow and targets on modeling deep learning neural networks. 


The image displays the various machine learning frameworks that are used for large scale computation, and have originated from different platforms.

Various Machine Learning Frameworks


  1. Apache MXNet, endorsed by Amazon as the chief deep learning framework on AWS, can compute numerous GPUs and machines and reinforce a wide reach of languages API like Python, R, Scala, C++, Pearl, Go, JavaScript, Julia. However, its original APIs are not comfortable to work with as TensorFlow. 

 

 

Tensorflow: Applications

 

  1. It is used in various Google products and apps- Gmail, Translate, Maps, Search, Photos, Speech, Android, YouTube and Play included. 

 

  1. It is practiced by leading enterprises around the world counting Airbnb, Ebay, Dropbox, Snapchat, Twitter, IBM, Uber and ofcourse Google.

 

 

Conclusion

 

In this tutorial you discovered the TensorFlow open-source library for deep learning and machine learning. You understood that TensorFlow is the library for fast numerical computation, specially developed for various types of operations necessary for the evolution and validation of huge deep learning models developed for machine learning applications.

 

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Neelam Tyagi

A versatile and creative technical writer in Analytics Steps. She has cross-functional experience in qualitative and quantitative analysis. She is focused and enthusiastic to achieve targets on time.

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