TensorFlow, which Google first made publicly available in November 2015, is now the most widely used framework for building deep learning models.
Deep Learning began to perform better than any other machine learning model a few years ago when presented with enormous amounts of data. Google published the platform to enable developers and academics to collaborate on AI models after realizing that deep neural networks might be used to improve all of its services.
Businesses have seen the technology being more widely used over four years. According to the Upwork Q1 2018 Skills Index, the second-fastest growing talent is:
What is TensorFlow?
Deep Learning is a branch of machine learning that uses multi-layer neural networks and is loosely based on the biological brain. What are neural networks and machine learning? We’ll discuss it shortly.
First and foremost, you should be aware that TensorFlow is one of the machine learning frameworks that developers and companies may use. Some more well-known frameworks are PyTorch, which is an open-source framework created by Facebook, and Caffe, which Berkeley made available.
Back to the basics now
What is machine learning?
Machine learning is the science (and art) of teaching computers how to “learn” (i.e., grow progressively better at a certain activity) from data, without needing to be explicitly programmed. We’re going to assume you have some familiarity with this topic already, but here’s a fast reminder.
What are neural networks?
As we previously discussed, biological brain networks serve as a loose inspiration for neural networks. These systems pick up skills by taking into account examples, or training data.
Deep learning differs from regular machine learning in that it often doesn’t require any task-specific rules to be set into it.
This simply implies that we don’t have to explain how the world works to our model; instead, it picks up “representations” by sifting through vast volumes of data, which might lead to the discovery of novel insights and novel patterns.
As stated in this article on Machine Learning vs. Deep Learning:
To analyze data, learn from that data, and make wise judgments based on what it has learned, we utilize a machine algorithm. In essence, Deep Learning is used in layers to build an artificial “Neural Network” that is capable of self-learning and reasoning. Deep Learning is a branch of machine learning, so to speak.
Now, back to TensorFlow.
TensorFlow Business Applications
The subjects we’ll be discussing are listed below in brief:
- using convolutional neural networks to recognize images (CNN)
- Models for Sequence-to-Sequence (Seq2Seq) natural language processing
- engines that recommend products using TensorFlow
Case Study: Coca-Cola, TensorFlow, and Digital Marketing
As described in this editorial, Coca-Cola supported one of its biggest and most successful digital marketing efforts with picture recognition powered by TensorFlow.
Coca-Cola has operated a loyalty program since 2006 where users may enter a code found on the bottle cap to win prizes.
In today’s mobile-first era, thumb-entering a 14-character code on your phone isn’t a very pleasant user experience. It made sense to ask customers to submit photographs, but how could they scan the codes on millions of different bottle cap images?
As you may guess, each image would have a somewhat distinct lighting setup, perspective, and string of low-resolution letters. The editorial says:
Convolutional Neural Networks, according to their study team, is the solution.
CNNs, a subset of deep learning, is at the core of contemporary artificial intelligence in business.
CNNs excel in image identification tasks, from deciphering handwritten numbers to reading street address numbers on Google Maps.
Convolutional Neural Networks was the ideal choice since Coca-Cola wanted to extract strings from photos that had tiny character sets with a lot of variation in the characters’ looks.
Because the models were so complicated in the past, creating deep neural networks like CNNs was fairly challenging. Fortunately, the TensorFlow framework makes it very easy to create and use deep neural networks.
Natural Language Processing, Sequence Prediction, and TensorFlow
Understanding sequential data, such as text that has a series of characters, is another use of TensorFlow and deep learning. Recurrent neural networks are neural networks designed for sequential data.
A chatbot for an eCommerce business that uses natural language processing is one example of this. A neural network may be trained using data from previous customer conversations, and the model might discover patterns in the sequences. The bots can then produce future sequence predictions based on past data after learning the patterns.
By 2020, 85% of all customer contacts, according to Gartner, will be managed without the need for human agents.
Many more intriguing business applications can use this kind of language modeling, including:
- Speech recognition for real-time subtitling
- AI-powered chatbots
- Virtual assistants like Siri, Alexa, and Google Assistant
- Machine translation
- Document summarization
- Image captioning
- Product description writing
Sequence prediction has a variety of successful commercial applications, so it’s worth looking into how it may work for you.
Summary: What is Tensorflow?
The enormous toolkit provided by the open-source TensorFlow framework gives programmers and business owners a complete pipeline for creating successful AI models.
This cutting-edge technology is being used in real-world settings for picture identification, natural language processing, and potent product recommendation engines.
It is already being utilized by businesses like Coca-Cola to assist their biggest digital marketing initiatives.
