TensorFlow 2.0: A Powerful New Deep Learning Tool

TensorFlow 2.0 is a deep learning library developed by Google built to solve large machine learning projects. This open-source library, based on artificial neural networks, can use massive datasets to derive insights and predictions. TensorFlow 2.0 is now so much more than its original incarnation. It has a whole ecosystem of tools and availability in programming languages such as JavaScript and Swift and is a more user-friendly tool. These improvements make TensorFlow 2.0 a powerful way to get started in deep learning, which I’m excited to teach in my new course, Complete TensorFlow 2.0 and Keras Deep Learning Bootcamp. 

TensorFlow was named the number one trending tech skill for 2020 and beyond in the Learning Platform for Business report 2020 Workplace Learning Trends Report: The Skills of the Future. Let’s explore more about TensorFlow, why you and your company might use it, common applications of the library, and how TensorFlow 2.0 differs from its predecessor.

TensorFlow: Deep learning vs. machine learning

It’s useful to understand TensorFlow’s place in the world of artificial intelligence, specifically why it’s considered a deep learning tool within the greater machine learning discipline.

Machine learning is a general approach that uses a variety of algorithms to extract insights out of data. As datasets grow, so do the complexities of the algorithms used to interpret that data. Algorithms built for small datasets may be able to tell the algorithm what to do in every possible scenario. But in large datasets with millions of data points, that simply isn’t realistic. Instead, machine learning techniques teach algorithms how to learn from the data they ingest and find insights from those learnings and trends. 

A common machine learning example is found on real estate websites like Zillow to predict the listing price of a house. An algorithm ingests historic data on sales of houses in the area, including the number of bedrooms, bathrooms, cosmetic features, and more to then determine the likely value of the house. 

Deep learning methods of artificial intelligence use artificial neural networks to accomplish tasks similar to those considered in machine learning. Due to their unique capabilities, deep learning methods can perform complex tasks that are impossible for typical machine learning algorithms. For example, deep learning neural networks can identify objects in images such as identifying a cat versus a dog in an uploaded image (a project we do in my course). 

Neural networks accomplish these tasks because they are designed to mimic how the human brain’s biological neurons operate. Neurons work together to think, learn, process information, make decisions, etc. A neural network simulates a brain’s functionality by arranging artificial neurons into layers and connecting those layers. Artificial neurons (perceptrons) accept inputs and provide usable outputs through the use of an activation function.

Complete TensorFlow 2.0 and Keras Deep Learning Bootcamp

Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Jose Portilla, Pierian Training