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DQN Implementation for CartPole Deep Q-Network (DQN) Implementation for CartPole-v1 Introduction This document provides a detailed explanation of a Deep Q-Network (DQN) implementation for solving the CartPole-v1 environment from OpenAI Gym. The implementation uses PyTorch for the neural network components. CartPole Problem: The agent must balance a pole on a moving cart by applying forces to the cart. The state space consists of cart position, cart velocity, pole angle, and pole angular velocity. The action space is discrete (left or right force). 1. Environment Setup and Imports The first section imports necessary libraries and sets up the environment. import gymnasium as gym import math import random import matplotlib import matplotlib.pyplot as p...

Residual Networks Classification with PyTorch - Complete Guide Residual Networks Classification with PyTorch This guide provides a comprehensive explanation of implementing Residual Networks (ResNet) for image classification using PyTorch, covering both custom ResNet implementation and transfer learning approaches. 1. Introduction to Residual Networks Residual Networks (ResNets) were introduced by Microsoft Research in 2015 to address the degradation problem in deep neural networks. As networks get deeper, accuracy gets saturated and then degrades rapidly. ResNets solve this by introducing "skip connections" or "shortcuts" that allow gradients to flow through the network more effectively. Key Features of ResNets: Skip Connections: Allow the network to learn identity functions, making deeper networks easier to train R...

Flower Classification with MobileNetV2 | Complete Guide Flower Classification with Transfer Learning using MobileNetV2 Project Overview This project demonstrates transfer learning using MobileNetV2 to classify flowers from the Oxford Flowers102 dataset, which contains 102 different flower categories. Why Transfer Learning? Efficiency: Leverages pre-trained weights from ImageNet (1.4M images) Performance: Achieves good accuracy with limited training data Resource-friendly: MobileNetV2 is optimized for mobile/edge devices System Architecture [MobileNetV2 Backbone] → [Feature Extractor] → [Custom Classifier Head (102 units)] Input: 224×224 RGB images → Output: 102-class probabilities Implementation Details Copy flower_classifica...

Autoencoder for Fashion MNIST Compression Data Compression Using Autoencoders on Fashion MNIST Project Overview This project implements an autoencoder neural network in PyTorch to compress and reconstruct images from the Fashion MNIST dataset. Autoencoders are unsupervised learning models that learn efficient data representations (encodings) by compressing the input into a latent space and then reconstructing it. What is an Autoencoder? An autoencoder consists of two main components: Encoder: Compresses the input into a lower-dimensional representation (latent space) Decoder: Reconstructs the input from the compressed representation The model is trained to minimize the difference between the original input and its reconstruction, forcing it to learn the most important features of the data. ...

Neural Network Architectures Overview Neural Network Architectures Overview 1. Artificial Neural Network (ANN) The fundamental building block of deep learning, consisting of interconnected nodes organized in layers. Architecture Input Layer: Receives the raw input data Hidden Layers: 1 or more layers that transform inputs through weights and activation functions Output Layer: Produces the final prediction or classification Key Equations output = activation(Wx + b) where: W = weight matrix x = input vector b = bias vector activation = nonlinear function (ReLU, sigmoid, tanh) Advantages Universal function approximator Simple to implement Good for structured data Limitations Poor performance with unstructured data (images, text) No spatial or temporal awareness...