philtabor/Youtube-Code-Repository 排名第 12,549，此处有 8 个同类条目；shervinea/cheatsheet-translation 排名第 12,552。

Youtube-Code-Repository Repository for all the code from my youtube channel You can find me at Kaggle/Venus-Volcanoes My crude implementation of a convolutional neural network to perform image classification on data gathered by the Magellan spacecraft. The data is horribly skewed, as most images do not contain a volcano. This means we'll have to do some creative data engineering for our model training. 1% of the data is "no volcano", and our model returns an accuracy of

around 88%, which is better than a model that outputs straight 0s for predictions. You can check out the video for this at You can find the data for this project at ReinforcementLearning/DeepQLearning My implementation of the Deep Q learning algorithm in PyTorch. Here we teach the algorithm to play the game of space invaders. I haven't had enough time to train this model yet, as it takes quite some time even on my 1080Ti / i7 7820k @ 4. I'll

train longer and provide a video on how well it does, at a later time. The blog post talking about how Deep Q learning works can be found at Video for this is at CNN. py Simple implementation of a convolutional neural network in TensorFlow, version 1. Video tutorial on this code can be found here Achieves accuracy of 98% after 10 epochs of training Requires data from ReinforcementLearning/blackJack-no-es. py Implementation of Monte Carlo control without exploring

starts in the blackjack environment from the OpenAI gym. Video tutorial on this code can be found at Algorithm trains for 1,000,000 games and produces a win rate of around 42%, loss rate of 52% and draw rate of 6% ReinforcementLearning/blackJack-off-policy. py Implementation of off policy Monte Carlo control in the blackjack environment from the OpenAI gym. Video tutorial on this code can be found at Algorithm trains for 1,000,000 games and produces

a win rate of around 29%, loss rate of 66% and draw rate of 5% ReinforcementLearning/cartpole_qlearning. py Implementation of the Q learning algorithm for the cart pole problem. Code is based on the course by lazy programmer, which you can find here here Video tutorial on this code can be found at ReinforcementLearning/doubleQLearning. py Implementation of the double Q learning algorithm in the cart pole environment. This is based on my course on reinforcement learning, which you

can find at this repo Video tutorial on this code can be found ReinforcementLearning/sarsa. py Implementation of the SARSA algorithm in the cart pole environment. This is based on my course on reinforcement learning, which can be found here Video tutorial on this code can be found at