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LSTM Music Generator Documentation | AI Music Composition Guide LSTM Music Generator Documentation This document explains a Python program that generates music using an LSTM neural network trained on MIDI files. Table of Contents Overview Requirements Getting Started Code Explanation Configuration Model Architecture Data Preparation Training Process Music Generation How It Works Limitations Potential Improvements Overview This...

Spatial Transformer Networks (STN) on MNIST - Complete Implementation Guide Spatial Transformer Networks (STN) on MNIST Introduction This document explains a complete implementation of Spatial Transformer Networks (STN) applied to the MNIST dataset using PyTorch. The STN is a learnable module that automatically applies spatial transformations to input data to enhance geometric invariance in neural networks. STN architecture overview (Source: PyTorch tutorials) Setup and Initialization 1. Importing Required Libraries import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision from torchvision import datasets, transforms import matplotlib.pyplot as plt import numpy as np import urllib.request Key libraries: ...

Transformer Sentiment Analysis | PyTorch Implementation Guide Transformer-Based Sentiment Analysis Implementation Introduction This guide provides a complete implementation of a binary sentiment classifier using a Transformer architecture in PyTorch. The model classifies sentences as either positive (1) or negative (0) sentiment. Key Features of This Implementation: Custom Transformer encoder architecture with 4 layers and 4 attention heads Dropout regularization (rate=0.2) to prevent overfitting Early stopping during training with patience of 10 epochs L2 regularization (weight decay=1e-5) Vocabulary-based text preprocessing with padding 80/20 train/test split with random state for reproducibility ...

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...