Face Detection and Recognition with Python.
The process involved using MTCNN for face detection, InceptionResnetV1 to extract face embeddings, and an SVM classifier to identify the person based on those embedding
In Part I for Azure MLOps for Begineers, we learnt how to train, register and deploy our model in Azure ML. In this part, we will learn how to use that endpoint to make forecast using a Flask Based web application. For basic Flask setup check : Streamlined Flask Deployment on Azure. We will build our forecasting application on the […]
Azure MLOps for Begineers: Train, Deploy and Serve a GRU forecasting model.
With Azure and GitHub CI/CD, you can automate your Flask deployment process for a seamless, instant launch. This powerful combination streamlines workflows, reduces manual effort, and ensures your web application is up and running in no time. In this guide, we’ll explore how to leverage GitHub CI/CD for rapid Flask deployment on Azure, empowering developers to deliver scalable, high-performance web apps with ease.
Low-Light Image Enhancer with GLADNet and Pytorch
Breast Cancer Classification with Azure ML & MLflow: End-to-End ML Tutorial
Build an Image Classifier on Azure
Learn to train, register and deploy model in Azure ML
This introduction focuses on using MLflow Tracking to experiment with a Support Vector Machine (SVM) classifier on the Digits dataset, a classic dataset in machine learning for multi-class classification.
Learning Image Segmentation with U-Net in Pytorch
we will learn to solve Bipedal Walker Hardcore Challenge with Soft Actor-Critic Algorithm
In this tutorial we will learn how to master a Bipedal Walker with PPO (Proximal Policy Optimization).
Second Part we will learn about the major components PPO for ai agent.
This is first of two part tutorial. Here we learn to build snake game. In part two, we will learn to build a PPO agent to play with it.
In this blog post, we will explore the Proximal Policy Optimization (PPO) algorithm. We’ll compare it to other deep reinforcement learning algorithms like Double Deep Q-learning and TRPO. Additionally, we’ll learn how to implement PPO using PyTorch.
Introduction of Prioritized Experience Replay and its implementation with PyTorch.
This is an implementation of Policy Gradient algorithm using PyTorch.
Implementation of Gaussian Double Deep Q network with PyTorch
This is implementation of MoG-DQN using PyTorch.
IQN is a state-of-the-art RL algorithm that focuses on predicting the full distribution of returns rather than just the mean. This approach provides a more comprehensive understanding of the value of actions, allowing for better decision-making in uncertain environments