Nolearn_utils
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Data Science Ipython Notebooks | 25,668 | 7 months ago | 34 | other | Python | |||||
| Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. | ||||||||||
| Pytorch Handbook | 18,594 | 9 months ago | 52 | Jupyter Notebook | ||||||
| pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行 | ||||||||||
| Handwritingrecognition Cnn | 89 | 7 years ago | 2 | Python | ||||||
| This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle. | ||||||||||
| Kannada_mnist | 69 | 4 years ago | Jupyter Notebook | |||||||
| The main repository for the Kannada MNIST dataset | ||||||||||
| Mnist | 50 | 7 years ago | mit | Jupyter Notebook | ||||||
| Machine Learning analyses of the MNIST database of handwritten digits | ||||||||||
| Kaggle For Fun | 38 | 7 years ago | 4 | Python | ||||||
| All my submissions for Kaggle contests that I have been, and going to be participating. | ||||||||||
| Digit_recognizer | 27 | 3 years ago | mit | Jupyter Notebook | ||||||
| CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995). | ||||||||||
| Nolearn_utils | 21 | 7 years ago | 6 | Jupyter Notebook | ||||||
| Utilities for nolearn.lasagne | ||||||||||
| Sign_language_mnist | 15 | a year ago | apache-2.0 | Python | ||||||
| Tutorial for Vitis AI using the Sign Language MNIST | ||||||||||
| Mnist Digit Recognizer Cnn Keras 99.66 | 11 | 6 years ago | mit | Jupyter Notebook | ||||||
| Used the Dataset "MNIST Digit Recognizer" on Kaggle. Trained Convolutional Neural Networks on 42000 Training Images and predicted labels on 28000 Test Images with an Validation Accuracy of 99.52% and 99.66% on Kaggle Leaderboard. | ||||||||||
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