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TensorFlow 2.0: Dynamic, Readable, and Highly Extended

TensorFlow 2.0 Introduction

Considering learning a new Python framework for deep learning? If you already know some TensorFlow and are looking for something with a little more dynamism, you no longer have to switch all the way to PyTorch thanks to some substantial changes coming as part of TensorFlow 2.0. In fact, many of the changes in 2.0 specifically address the alleged shortcomings of TensorFlow.

With eager execution by default, you no longer have to pre-define a static graph, initialize sessions, or worry about tensors falling outside of the proper scope when you get over-zealous in your object-oriented principles. TensorFlow still has about 3 times the user base of PyTorch (judging from the repositories on GitHub referencing each framework), and that means more extensions, more tutorials, and more developers collaboratively exploring the space of all possible code errors on Stack Overflow. You’ll also find that despite the major changes starting with TensorFlow 2.0, the project developers have taken many steps to ensure that backwards compatibility can be maintained.

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Object Detection Tutorial in TensorFlow: Real-Time Object Detection

Creating accurate machine learning models that are capable of identifying and localizing multiple objects in a single image has remained a core challenge in computer vision. But with recent advancements in deep learning, object detection applications are easier to develop than ever before. TensorFlow’s object detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. 

You can go through this real-time object detection video lecture where our deep learning training expert discusses how to detect an object in real time using TensorFlow.

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The Mandelbrot Set In TensorFlow

What Is the Mandelbrot Set?

According to Wikipedia, “The Mandelbrot set is a famous example of a fractal in mathematics. The Mandelbrot set is important for chaos theory. The edging of the set shows a self-similarity, which is not perfect because it has deformations. The Mandelbrot set can be explained with the equation zn+1 = zn2 + c. In that equation, c and z are complex numbers, and n is zero or a positive integer (natural number). Starting with z0=0, c is in the Mandelbrot set if the absolute value of zn never becomes larger than a certain number (that number depends on c) no matter how large n gets.”

Visualizing the set has nothing to do with machine learning. It can be thought of as another TensorFlow example for mathematics. So, let’s learn how can we compute the Mandelbrot set in TensorFlow.