TensorFlow Machine Learning

The age of AI (Artificial Intelligence) and tensor deep learning technologies has pushed TensorFlow to the centre stage of the tech world. TensorFlow itself is an open-source AI library that lets developers create models from data flow graphs. To be honest, developers often face the question of TensorFlow and pytorch which type of machine learning they should use. TensorFlow is a critical part in the modern AI and ML (Machine Learning) technologies we have available for us today.

TensorFlow Overview

Google developed TensorFlow mainly for facilitating the development of deep learning applications It is an open-source library it comprises with support for traditional machine models. The technology was initially meant to abet massive numerical computations without addressing the specific needs of deep learning methodologies.

Naturally, it came as a surprise to TensorFlow developers when it emerged that the technology helped deep learning development immensely. Such great usefulness led Google to switch to the open-source licensing terms for TensorFlow.

The tensor deep learning technology accepts multi-dimensional data arrays referred to as tensors. The importance of such multiple dimensional data arrays while handling massive data amounts cannot be overstressed.

TensorFlow works through data flow graphs that come with edges and nodes. The execution mechanism of graphs makes the execution of such TensorFlow code distributed across computer clusters using GPUs easy.

Top Use Cases of TensorFlow

  • Text-based apps
  • Audio recognition
  • Time series
  • Image recognition
  • Video detection

Top reasons to choose TensorFlow

  • It is the leading AI platform that developers prefer
  • It is a part of the managed mainstream public cloud ML PaaS
  • It opens immense possibilities and great power for developers when used in conjunction with Keras
  • It enjoys comprehensive support for both the tooling and integration components of the technology
  • It is backed by Google’s R&D (Research & Development) team

Deep Learning with TensorFlow

Industry experts expect the deep learning market to be valued at $1,772.9 million by the end of this year according to earlier estimates. The ease with which you can create a neural network adds to its advantage. Deep learning neural network models can be created without having to worry about the algorithm that powers it as there are a large number of deep learning frameworks present to help you. Deep learning frameworks usually consist of an interface and library other than the framework itself. Some of the frameworks iSummation deals with include:

  • Keras
  • Caffee
  • Microsoft Cognitive Toolkit
  • DeepLearning4j
  • MXNET
  • Chainer
  • And of course, TensorFlow

Many of the different frameworks use tensor deep learning and are meant for different application purposes. TensorFlow stands out among them due to the great flexibility of its system architecture. Many top tech giants like Meta, Google, Airbnb, Nvidia, Deepmind and Lenovo amongst others are using TensorFlow for their next-generation products and services. Developers can use TensorFlow both on mobile and desktop platforms and leading cloud service providers offer TensorFlow support and hosting like Azure TensorFlow. You can also use common programming languages like Python, C++ and R to use and modify your TensorFlow code.

Top TensorFlow Projects Examples

Deep Speech

The Deep Speech TensorFlow project wanted to create a text-to-speech converter using the TensorFlow library. The tensor deep learning application bears out how well TensorFlow works with language processing activities. The functionality of the Deep Speech project can be further extended using hardware devices like Arduino, Raspberry Pi and also high-powered GPU servers.

Sudoku

The TensorFlow Sudoku project wanted to create a sudoku solver bot that can analyse sudoku grids and can automatically fill them up appropriately by using simple maths rules. A Raspberry pi3 and a camera consisted of the bot’s hardware. The camera was used to take snapshots of the sudoku grid. The image processing functionalities of TensorFlow let it pre-process the images and segment them into a specific box.

Such individual boxes were then analysed by using neural network-based image recognition. The image recognition process outputs a numeric representation of the sudoku grid.

Detecting, Tracking and Counting Vehicles

The aim of this project was to use the functions of the TensorFlow Object Counting API for the detection, tracking and calculating the number of vehicles. For added utility, the project can be made more complex by classifying vehicle types based on speed, color and size. The project used OpenCV which is a computer vision related software library in addition to TensorFlow.

The speed predictions were based upon OpenCV’s calculation and pixel manipulation functions. The detection and classification of the vehicles on the other hand used TensorFlow’s Object Detection API.

Our TensorFlow expertise

CoLab

It lets developers create and run code on the free Jupyter notebook browser environment.

TensorBoard

Our ability in TensorBoard gives us the necessary tools for machine learning visualisation and experimentation.

ML Perf

This is a broad suite of machine learning tools that finds use in assessing ML software, hardware and other services.

TensorFlow Research Cloud

The TensorFlow Research Cloud, often abbreviated as TFRC, is a program used by researchers to apply for and eventually get access to over 1000 Cloud Tensor Processing Units (TPUs).

MLIR

MLIR integrates and unifies high-performance machine learning model infrastructure in TensorFlow.

Accelerated Linear Algebra

We also use a linear algebra domain-specific compiler that speeds up TensorFlow models.

Our TensorFlow Service Offering

Machine Learning

iSummation is your partner in the development of high-performance numerical computations across a wide number of platforms.

Natural Language Processing

iSummation has extensive experience in leveraging the module functions of TensorFlow to handle responses acting as state machine classifiers.

Chatbot Development

The sequence-to-sequence functionality of TensorFlow modules allows us to deal with user response in the chatbot development process.

Image Processing

iSummation can effectively use the TensorFlow AI module to act as an agent of image classification.

Complex Numerical Computations

iSummation has created a number of single-page apps that can carry out heavy numerical computations continuously using data-flow graphs.

Outcome Predictions

We use exhaustive linear models to crunch complex data sets with the goal of finding pricing and multiclass categorization for eCommerce ventures.

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