Jose Portilla

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 Udemy 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

Last Updated June 2020

  • 116 lectures
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Learn to use Python for Deep Learning with Google’s latest Tensorflow 2 library and Keras! | By Jose Portilla

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4 important tools in the TensorFlow ecosystem

As TensorFlow adoption has grown, its user community has created an ecosystem of tools that solve a wide array of problems like the use of a specific programming language, easier data ingestion, or serving models to a customer. Some of the most useful tools include:

TensorFlow is the #1 trending tech skill on Udemy.com this year! Discover the full list of trending tech skills in the 2020 Workplace Learning Trends Report.

Why TensorFlow over other deep learning libraries?

While quite a few deep learning libraries are used in industry and academia, TensorFlow is a solid choice for engineers of various expertise levels for several reasons: 

  1. Large community — TensorFlow is hugely popular in the deep learning community. You’ll find active online groups, frequent in-person meetup opportunities, and plenty of educational resources for you and your team. 
  2. Backed by Google — Google invested heavily in AI research in the last decade, which earned it AI and data science credibility across many of its products, including Google Cloud. It continues to resource TensorFlow’s development and open-source accessibility while also using it across many of its own commercial and enterprise products. 
  3. Tools ecosystem — Other deep learning libraries don’t have a robust ecosystem of tools like those we explored above. Many libraries began as a research method and weren’t fully intended for production use. Google’s prioritization of a production-ready product helped establish a strong community of enthusiasts who built related tools. Google also provides Google Colaboratory, a free tool for developers to write, run, and share code within Google Drive.

What’s new in TensorFlow 2.0?

TensorFlow was initially designed for advanced practitioners and was not easy for beginners. Its early version had a complicated static graph system requiring developers to define the entire graph in code before running models. TensorFlow 2.0 is now designed with usability in mind and is a great choice for students new to deep learning and offers a few fresh features including:

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TensorFlow applications and examples 

One of the most popular examples of TensorFlow is called image classification, which trains a neural network to reliably identify which animal is in a photo, for example. But, TensorFlow has so many more applications than that relatively simple classification. We can understand more of TensorFlow’s real-world applications through the lens of their specific neural networks subcategories.


Example of ANN regression model to predict housing prices.

Classification models predict the category of something. In the course, we examine real data from LendingClub to predict whether or not a borrower will pay back their loan given historical feature information about the person.


Example of a classification model that predicts loan remittance likelihood.

Example from the TensorFlow 2.0 course of a CNN algorithm that identifies photos of animals.

A sales prediction visualization created from an RNN algorithm.

With TensorFlow 2.0’s updates making it easier to use for technical teams and engineers of all experience levels, this is a great time to start building neural networks and make your deep learning projects come to life. Start learning in my course Complete TensorFlow 2.0 and Keras Deep Learning Bootcamp.

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Page Last Updated: February 2020