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Category: TensorFlow

#005 TF 2.0 An implementation of a Shallow Neural Network with tf.keras – Circles dataset

Highlights: In previous post we have talked about TensorFlow Wrappers and there we concluded that tf.keras is the most convenient way to build neural networks. Now we are going to implement one very simple network using this high-level API. Tutorial Overview: Imports and Dataset preparation Building a Neural Network Visualization 1. Imports and Dataset preparation Let’s start with basic imports. Don’t worry if some things are not familiar with all of these libraries, we will…
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#011 TF YOLO V3 Object Detection in TensorFlow 2.0

YOU ONLY LOOK ONCE Highlights: Prior to Yolo majority of approaches for object detection tried to adapt the classifiers for the purpose of detection. In YOLO, an object detection has been framed as a regression problem to spatially separated bounding boxes and associated class probabilities. In this post we will learn about the YOLO Object Detection system, and how to implement such a system in TensorFlow 2.0. About Yolo:Our unified architecture is extremely fast. Our…
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#010 TF TensorBoard: Visualizing Learning

Highlights: In this post we will learn what is TensorBoard and how to use it. For most people, neural networks can sometimes be a bit of black box. Debugging problems is also a lot easier when we can see what the problem is. Thankfully, TensorBoard is a tool that will help us to analyze neural networks and to visualize learning. Tutorial Overview: Sequential API Model Subclassing Intro The idea of TensorBoard is to help to…
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#004 TF 2.0 TensorFlow Wrappers

Highlights: In this post we are going to talk more about TensorFlow Wrappers. We are going to compare things before and after TensorFlow 2.0. This post will be the introductory one to the series of posts where we are going to build a wide variety of neural networks. To use TensorFlow in our projects, we need to learn how to program using the TensorFlow API. TensorFlow has multiple APIs that can be used to interact with…
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#003 TF 2.0 Eager Execution- A Pythonic way of using TensorFlow

TensorFlow uses Eager execution, which is a more convenient way to execute the code, and also more “Pythonic”. It is a default choice in the latest version TensorFlow 2.0. In TensorFlow 1.x, we first need to write a Python program that constructs a graph for our computation, the program then invokes Session.run(), which hands the graph off for execution to the C++ runtime. This type of programming is called declarative programming (specification of the computation…
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