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Category: Deep Learning

#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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#OD1 YOLO Object Detection

YOU ONLY LOOK ONCE Highlights: In this post we will learn about the YOLO Object Detection system, and how to implement such a system with Keras. About Yolo: Our unified architecture is extremely fast. Our base YOLO model processes imagesin real-time at 45 frames per second. A smaller version of the network, Fast YOLO,processes an astounding 155 frames per second … — You Only Look Once: Unified, Real-Time Object Detection, 2015 Tutorial Overview: This post…
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#FA 003 Face Detection in Videos using OpenCV

In real case scenarios, there is often a need for detection and recognition of faces not just in images, but in videos. For example, this is a necessary prerequisite for security cameras, filters on social networks, identification at work and many other cases where cameras are used. The pictures are static, but videos can be seen as a series of pictures, or frames. All video clips are constructed of a constant number of frames in…
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#008 TF An implementation of a Convolutional Neural Network in tf.keras – MNIST dataset

In this post we will see how we can classify handwritten digits using convolutional neural network implemented in Keras. Required packages: Numpy Matplotlib Keras Tensorflow Sklearn Seaborn __future__ Keras-vis Table of Contents: Load the digit dataset Implementing a Neural Network Visualization Test our model Save a model as picture Activation Maps Saliency maps Activations Now we can plot some predictions, to see how our model works. Images for testing can be downloaded from the internet…
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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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