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
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.
You may understand TensorFlow more in depth from the official video from the official TensorFlow youtube channel.
Functions precisely with mathematically expressions incorporating multi-spatial arrays.
High calibration of complex computation over machines and large datasets.
Avail assistance to deep neural network and machine learning theories.
It executes computation using data flow graphs;
Nodes present in the graph depicts mathematical operations.
Graph edges show the multi-dimensional arrays or tensors that move between them.
Let TensorFlow divulge its own memoir itself !!!!
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;
You can read here more about data flow graphs here.
It incorporates multiple tools and libraries to help and work more effectively;
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.
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.
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)
Being a Google develop app, TensorFlow possesses multiple benefits, some of which are discussed below;
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.
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.
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 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;
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.
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.
Various Machine Learning Frameworks
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.
It is used in various Google products and apps- Gmail, Translate, Maps, Search, Photos, Speech, Android, YouTube and Play included.
It is practiced by leading enterprises around the world counting Airbnb, Ebay, Dropbox, Snapchat, Twitter, IBM, Uber and ofcourse Google.
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.
Never miss a single analytical update from Analytics Steps, share this blog on Facebook, Twitter, and LinkedIn.
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working Ecosystem
READ MORE6 Major Branches of Artificial Intelligence (AI)
READ MORETop 10 Big Data Technologies
READ MORE8 Most Popular Business Analysis Techniques used by Business Analyst
READ MORE7 types of regression techniques you should know in Machine Learning
READ MOREDeep Learning - Overview, Practical Examples, Popular Algorithms
READ MOREIntroduction to Time Series Analysis in Machine learning
READ MOREWhat is the OpenAI GPT-3?
READ MOREHow Does Linear And Logistic Regression Work In Machine Learning?
READ MORE7 Types of Activation Functions in Neural Network
READ MORE
Comments