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
Azure MLOps for Begineers: Train, Deploy and Serve a GRU forecasting model.
Low-Light Image Enhancer with GLADNet and Pytorch
Build an Image Classifier on Azure
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
In this blog post, we will implement Double DQN using PyTorch to solve the Lunar Lander environment from OpenAI Gym.
Solving the Acrobot problem with the help of Actor-Critic algorithm.
The blog about the CNN functions in PyTorch and other assisting functions generally used with Convolution Neural Networks.
In double DQNs, we use a separate network to estimate the target rather than the prediction network. The separate network has the same structure as the prediction network. And its weights are fixed for every T episode (T is a hyperparameter we can tune), which means they are only updated after every T episode. The update is simply done by […]