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RNN Time Series Prediction - Complete Guide RNN Time Series Prediction A Comprehensive Guide to Sequence Forecasting with Recurrent Neural Networks Introduction Recurrent Neural Networks (RNNs) are a class of neural networks designed to work with sequential data. Unlike traditional feedforward networks, RNNs maintain a "memory" of previous inputs through hidden states, making them particularly effective for time series forecasting. Key Concepts Sequential Processing : RNNs process data points in sequence, maintaining information about previous steps Hidden State : Internal memory that captures information about previous inputs Time Unrolling : The process of visualizi...

CIFAR-10 Image Classification with CNN (PyTorch) - Complete Guide CIFAR-10 Image Classification Using CNN with PyTorch This comprehensive guide demonstrates how to classify images from the CIFAR-10 dataset using Convolutional Neural Networks (CNNs) built with PyTorch. The project covers the entire machine learning pipeline from data loading to model deployment, including advanced techniques for improving performance. 🚀 Key Features Complete PyTorch implementation Data augmentation techniques Multiple CNN architectures Training visualization Model evaluation metrics Hyperparameter tuning Model saving/loading Deployment options 📊 Performance Baseline model: ~70% accuracy Improved mod...

Advanced MNIST Digit Classification with PyTorch Advanced MNIST Digit Classification with PyTorch This comprehensive tutorial covers multiple approaches to handwritten digit recognition using the MNIST dataset with PyTorch, from basic neural networks to convolutional architectures and advanced techniques. 📌 Key Features Basic ANN implementation CNN architectures Data augmentation Model interpretation Hyperparameter tuning Performance optimization 🎯 Performance Basic ANN: ~97% accuracy Simple CNN: ~99% accuracy Training time: 1-10 minutes Hardware: CPU/GPU compatible 1. Environme...

Health Tracker Program Health Tracker Program This program tracks the time spent on web browsers and saves the data to Google Sheets. Below are the steps to set it up and the complete code. Prerequisites Install Required Libraries: pip install psutil gspread oauth2client Set Up Google Sheets API: Go to the Google Cloud Console . Create a new project. Enable the Google Sheets API for your project. Create credentials (Service Account) and download the JSON file. Share your Google Sheet with the email address of the service account (found in the JSON file). Create a Google Sheet: Create a new Google Sheet where you want to save the data. Note the name of the sheet, as you will need it in the code. Health Tracker Program import psutil import threading import time import gspread from oauth2client.service_account import Service...

OCR Extraction with Python Python OCR Image Reader with Tesseract 💾 1. Install Required Libraries Use pip to install all required packages: pip install opencv-python pytesseract pillow numpy 🔍 2. Overview This Python script reads an image file (e.g., denek.png ) containing Turkish text, preprocesses it for better recognition, extracts text using pytesseract , and prints specific fields like Gönderen (Sender) and Kaynak (Source). 📦 3. Check Library Dependencies def check_libraries(): necessary_libraries = ["cv2", "pytesseract", "PIL", "pathlib", "os", "numpy"] for library in necessary_libraries: try: __import__(library) print(f"{library} is installed.") except ImportError: print(f"{library} is not installed. Please install it using pip: pip install {library}") ...