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Title: GitHub - diltont/deep-learning-with-python-notebooks: Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Open Graph Title: GitHub - diltont/deep-learning-with-python-notebooks: Jupyter notebooks for the code samples of the book "Deep Learning with Python"

X Title: GitHub - diltont/deep-learning-with-python-notebooks: Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Description: Jupyter notebooks for the code samples of the book "Deep Learning with Python" - diltont/deep-learning-with-python-notebooks

Open Graph Description: Jupyter notebooks for the code samples of the book "Deep Learning with Python" - diltont/deep-learning-with-python-notebooks

X Description: Jupyter notebooks for the code samples of the book "Deep Learning with Python" - diltont/deep-learning-with-python-notebooks

Opengraph URL: https://github.com/diltont/deep-learning-with-python-notebooks

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chapter09_part01_image-segmentation.ipynb	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks/blob/master/chapter09_part01_image-segmentation.ipynb
chapter09_part02_modern-convnet-architecture-patterns.ipynb	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks/blob/master/chapter09_part02_modern-convnet-architecture-patterns.ipynb
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chapter14_conclusions.ipynb	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks/blob/master/chapter14_conclusions.ipynb
README	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks
MIT license	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks
	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks#companion-jupyter-notebooks-for-the-book-deep-learning-with-python
Deep Learning with Python, 2nd Edition (Manning Publications)	https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff
	https://patch-diff.githubusercontent.com/diltont/deep-learning-with-python-notebooks#table-of-contents
Chapter 2: The mathematical building blocks of neural networks	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter02_mathematical-building-blocks.ipynb
Chapter 3: Introduction to Keras and TensorFlow	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter03_introduction-to-keras-and-tf.ipynb
Chapter 4: Getting started with neural networks: classification and regression	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter04_getting-started-with-neural-networks.ipynb
Chapter 5: Fundamentals of machine learning	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter05_fundamentals-of-ml.ipynb
Chapter 7: Working with Keras: a deep dive	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter07_working-with-keras.ipynb
Chapter 8: Introduction to deep learning for computer vision	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter08_intro-to-dl-for-computer-vision.ipynb
Part 1: Image segmentation	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part01_image-segmentation.ipynb
Part 2: Modern convnet architecture patterns	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part02_modern-convnet-architecture-patterns.ipynb
Part 3: Interpreting what convnets learn	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_part03_interpreting-what-convnets-learn.ipynb
Chapter 10: Deep learning for timeseries	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter10_dl-for-timeseries.ipynb
Part 1: Introduction	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part01_introduction.ipynb
Part 2: Sequence models	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part02_sequence-models.ipynb
Part 3: Transformer	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part03_transformer.ipynb
Part 4: Sequence-to-sequence learning	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part04_sequence-to-sequence-learning.ipynb
Part 1: Text generation	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part01_text-generation.ipynb
Part 2: Deep Dream	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part02_deep-dream.ipynb
Part 3: Neural style transfer	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part03_neural-style-transfer.ipynb
Part 4: Variational autoencoders	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part04_variational-autoencoders.ipynb
Part 5: Generative adversarial networks	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part05_gans.ipynb
Chapter 13: Best practices for the real world	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter13_best-practices-for-the-real-world.ipynb
Chapter 14: Conclusions	https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_conclusions.ipynb
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