#convolutional neural network

The DeepLearningFlappyBird project employs the Deep Q-Network algorithm to train AI for Flappy Bird, using convolutional neural networks to analyze pixel data for gameplay optimization. Featuring key reinforcement learning techniques, the project integrates TensorFlow, pygame, and OpenCV-Python. Step-by-step guidance is provided to build a learning architecture that masters the game through state-action value functions and adaptive training, like ε-greedy policy and mini-batch sampling. Discover advancements in AI game strategy through detailed implementation methods.

CREPE is a cutting-edge tool for monophonic pitch tracking, leveraging a deep convolutional neural network that processes waveform inputs directly. It exceeds the performance of trackers such as pYIN and SWIPE, offering adaptable model capacities for efficient computation. CREPE supports batch processing, is easily accessible via command line and Python, and user friendly with installation through PyPI. It allows flexible time-step adjustments and Viterbi smoothing. Optimized for processing vocal and instrumental audio in WAV format, it provides resampling capabilities and benefits from GPU acceleration for faster processing.

This software uses U-Net for precise image segmentation and cv2 for detecting and correcting edges of Chinese license plates. It employs a convolutional neural network (CNN) for comprehensive, end-to-end recognition. Built with Python 3.6 and TensorFlow's Keras, it performs well even under challenging conditions like skewed angles or poor lighting. Ensure any input images without specific location needs are 240x80 or smaller to achieve optimal recognition results.