Implementation with TensorFlow2 Keras — Summary of Generative Adversarial Network Essentially, in the case of vanilla GANs, the entire architecture is composed of a generator and a discriminator. The goal of the generator is to create “Fake Data” that resembles the “Real Data” using the “Noise Vectors”. Whereas, the goal of the discriminator is to distinguish generated “Fake Data” from…

#OpenVINO #Pre-trained Model #AI Application In the previous “Introduction to OpenVINO”, I talked about what OpenVINO is, what it is capable of, how to get started, and performed integration with TensorFlow2. In today’s article, I would like to introduce some of the more practical applications that I built with the…

#Intel hardware #Intel Deep Learning #Optimization Toolkit From the previous article, I briefly introduced a Deep Learning Model Optimization with TensorFlow models. In that article, I demonstrated some results I found while trying out myself. Overall, with proper configuration and model quantization, I was able to produce models with smaller…

TLDR: After completing a few Medical Imaging Computer Vision projects with TensorFlow Keras, I decide to investigate a bit more on optimizing models. Within this article, I will briefly share my thoughts and study notes regarding the TensorFlow Optimization Toolkit. This article elaborates on the importance of LOW LATENCY and REDUCED…

# Deep Learning # Medical Imaging # Image Classification # Image Segmentation # Object Detection Project Motivation: Since I have been studying a lot in deep learning (still a lot to catch up), I will like to apply all the techniques to my projects. My goal is to implement the models…

Background: So I was asked to study the topic of “Web Scraping/Crawling” at work. Specifically, my task was to collect the entire text data based on a website domain which I have no idea how to start. Nonetheless, I had come across a python tool called SCRAPY!! …

Summary Anomaly detection, aka outlier detection, has always been an interesting area of study. A data set contains few outliers that will mislead your ML models because coefficients are likely to fit towards outliers, ie rare cases. For instance:

Data Source Mobile Price Classification: https://www.kaggle.com/iabhishekofficial/mobile-price-classification Author: Abhishek Sharma Project Summary With over 20 features related to different specifications on mobile phones, my goal is to figure out the best algorithm that will help make price decisions for the “Imaginary Manager Bob”. FYI, Bob is a made-up person HA.

Before Starting: For the next two weeks, I will be presenting you with two ML practical projects regarding classification. In specific, a binary classification project and a multiclass classification project. I am trying to be as active as possible in sharing practical projects here at Medium. Just a heads up…