Deep Learning With Python_中文版+英文版+代码

[{"title":"（ 23 个子文件 29.91MB ） Deep Learning With Python_中文版+英文版+代码","children":[{"title":"Deep Learning With Python_中文版+英文版+代码","children":[{"title":"Deep_Learning_With_Python.pdf <span style='color:#111;'> 6.67MB </span>","children":null,"spread":false},{"title":"Deep Learning With Python中文版.pdf <span style='color:#111;'> 19.06MB </span>","children":null,"spread":false},{"title":"deep-learning-with-python-notebooks--master","children":[{"title":"deep-learning-with-python-notebooks-master","children":[{"title":"5.2-using-convnets-with-small-datasets.ipynb <span style='color:#111;'> 421.04KB </span>","children":null,"spread":false},{"title":"8.3-neural-style-transfer.ipynb <span style='color:#111;'> 405.32KB </span>","children":null,"spread":false},{"title":"8.2-deep-dream.ipynb <span style='color:#111;'> 196.41KB </span>","children":null,"spread":false},{"title":"3.5-classifying-movie-reviews.ipynb <span style='color:#111;'> 67.89KB </span>","children":null,"spread":false},{"title":"6.1-using-word-embeddings.ipynb <span style='color:#111;'> 92.11KB </span>","children":null,"spread":false},{"title":"6.2-understanding-recurrent-neural-networks.ipynb <span style='color:#111;'> 82.64KB </span>","children":null,"spread":false},{"title":"6.4-sequence-processing-with-convnets.ipynb <span style='color:#111;'> 92.21KB </span>","children":null,"spread":false},{"title":"8.1-text-generation-with-lstm.ipynb <span style='color:#111;'> 156.83KB </span>","children":null,"spread":false},{"title":"6.3-advanced-usage-of-recurrent-neural-networks.ipynb <span style='color:#111;'> 199.45KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"8.5-introduction-to-gans.ipynb <span style='color:#111;'> 144.19KB </span>","children":null,"spread":false},{"title":"4.4-overfitting-and-underfitting.ipynb <span style='color:#111;'> 103.62KB </span>","children":null,"spread":false},{"title":"6.1-one-hot-encoding-of-words-or-characters.ipynb <span style='color:#111;'> 8.59KB </span>","children":null,"spread":false},{"title":"5.3-using-a-pretrained-convnet.ipynb <span style='color:#111;'> 227.68KB </span>","children":null,"spread":false},{"title":"8.4-generating-images-with-vaes.ipynb <span style='color:#111;'> 277.18KB </span>","children":null,"spread":false},{"title":"5.4-visualizing-what-convnets-learn.ipynb <span style='color:#111;'> 6.68MB </span>","children":null,"spread":false},{"title":"3.7-predicting-house-prices.ipynb <span style='color:#111;'> 68.59KB </span>","children":null,"spread":false},{"title":"3.6-classifying-newswires.ipynb <span style='color:#111;'> 62.21KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 3.90KB </span>","children":null,"spread":false},{"title":"5.1-introduction-to-convnets.ipynb <span style='color:#111;'> 10.84KB </span>","children":null,"spread":false},{"title":"2.1-a-first-look-at-a-neural-network.ipynb <span style='color:#111;'> 13.61KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}],"spread":true}],"spread":true}]