With data taking center stage in most organizational setups, artificial intelligence and machine learning have the potential to run rampant. How do you control them in a way that optimizes the value of data for your business? Deep learning is a necessity. However, organizations also need ways to simplify the management of processes in their deep learning framework.
TensorFlow is one Google framework that works best with all deep learning models. In all actuality, TensorFlow is nothing but a deep neural network that performs based on its surrounding environment. TensorFlow utilizes the concept of positive reinforcement, whereby the machine diverts toward certain (favorable) tasks.
The framework for TensorFlow takes into account multiple layers of data known as nodes. These nodes come out with the most accurate outcome for every particular action taken by the system.
To simplify the whole process, TensorFlow can take the effort out of machine learning and harness its potential. The framework helps in the creation of high-end applications. Since deep learning models are a type of machine learning, TensorFlow fits perfectly for the task.
The characteristics of an optimized deep learning framework include:
- Exceptional performance by the model that meets the expectations of those on the upper-level hierarchy
- Easily comprehensible by your staff
- Processes run parallel to each other, reducing effort and computations
- The ability to compute gradients automatically
- Exceptional portability
TensorFlow has all these characteristics and is currently being used by some major government entities and private companies. These organizations harness the power in the framework to derive the best results for their own intelligent processes.
NASA, Dropbox, Airbnb, Uber, Airbus, and Snapchat all follow one form of TensorFlow or another for deep learning. TensorFlow’s ability lies in the fact that it can be an amazing tool for businesses looking to get the most out of AI and ML.
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Reasons for TensorFlow’s Popularity
TensorFlow has only increased in the property during the last couple of years. This popularity is justified as there are sufficient reasons to support this change.
To begin with, TensorFlow has one of the most popular and commonly used software libraries. The library hosts multiple software processes. Additionally, the framework for TensorFlow is exceptionally easy for developers to understand and deploy models on. The complexity of the framework can significantly define the kind of results you get from your ML or DL model. To get the perfect results from the system, your framework shouldn’t be too complex and should be easy to understand by all involved. Once developers know how to maneuver around the processes, they will be able to build and then deploy models quickly through the setup.
The TensorFlow was created with the power limitations that most developers have in mind. The creators had an eye on the presence of legacy systems within organizations, and hence, they wanted a system that could be deployed easily within systems with limited power. The software library for TensorFlow can be run on all kinds of systems, with their power limitations in mind. The good thing about the software library is that you don’t need specific computing power, and you can run it on all kinds of systems. The library can also be run on smartphones. Both Android and Apple operating software are compatible with the framework. Even people who have worked with TensorFlow on an Intel I3 (with 8 GB RAM) sing the praises of the framework and its minimal performance issues.
The TensorFlow model is extremely simple to train on both GPU and CPU for distributed computing. This can enhance efficiency and lead to better overall results. The system is also extremely responsive, and it reacts almost immediately to whatever commands you put in.
TensorFlow has been made with an eye on all kinds of audiences, which is why it works brilliantly on all sorts of languages. The systems can work on multiple languages, based on whatever you are more comfortable with. Users also get literal walkthroughs and tutorials to make their understanding of the system more accessible. The framework is simple to use, with the walkthrough tutorials making comprehension significantly easier.
All of these attributes make TensorFlow a hot property within the market and make it a deep learning framework that you can mark in your book as one to watch.
Common Deep Learning Challenges
Here’s a quick look at some of the challenges that hinder deep learning analysis within organizations.
Need for Lots of Data
Organizations need lots of data for a deep learning model to function properly. The data provided also includes training data that can be used for preparing the model for data it will encounter in the future. This training data should replicate the original data as much as possible so that the system is prepared for what is to come.
Costs Are Higher
The costs associated with deep learning can be high. This is primarily because deep learning models happen to run only on high-grade computers. These high-grade computers come with a big price tag, which is often a bit too much for management to pay.
Additionally, the labor required for managing deep learning models can be a bit hard to achieve. You need the best talent who will want more money for better work.
As an expert, you should make sure that the cases you work on do not create any irresponsible bias. The biggest challenge of deep learning is to make sure that the results you create or the insights you generate do not unfairly favor one group of individuals over another. Many organizations have seen their data campaigns go south because of an unethical approach to their usage of deep learning.
Lack of Expertise
Organizations often lack the human resources that are required for handling the complications of deep learning.
Legacy systems aren’t well-versed with the computing power needed for DL. Updating your older legacy systems to new ones can be time-consuming and expensive.
How Can TensorFlow Help Your Business?
Deep learning is a transformational solution that helps organizations in their data transformation. The neural networks associated with DL can not only solve business problems, but they can also create value for the organization. DL, under the framework of TensorFlow, can be helpful for businesses in many cases.
Some general use cases of the framework include:
- Image Recognition
- Video Analysis
- Sound Recognition
- Text-Based Applications
- Object Tagging
- Flaw Detection
- Sentiment Analysis
- Computer Vision
- Anomaly Detection
More specific use cases that we can see rolling out in the coming times include:
- Self-driving cars
- Sea and air drones
- Smart personal assistants with improved user experience
TensorFlow can prove to help simplify and take the complications out of the algorithms used in these cases.
Check out the video below to master the concept of Deep Learning with TensorFlow -
The success and popularity of TensorFlow are justified. This framework may prove to be essential for businesses that want to extract value from their AI, ML, and DL campaigns. Deep learning is transforming the business world around us with added intelligence, and TensorFlow may be the asset leading this change forward.
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