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deep-learning-with-python-notebooks-tf2.x

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deep learning with python codes for tf2.x

deep-learning-with-python-notebooks-tf2.x 최신버젼 다운로드

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# Companion Jupyter notebooks for the book "Deep Learning with Python adapted for Tensorflow 2.x"

This repository contains Jupyter notebooks implementing the code samples found in the book [Deep Learning with Python (Manning Publications)](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments.

This repository contains Jupyter notebooks implementing the code samples found in the book [Python ile Derin Öğrenme (http://buzdagiyayinevi.com/python-ile-derin-ogrenme/). Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments.

These notebooks use Python 3.x and Kerass within Tensorflow 2.0

## Table of contents

* Chapter 2:
    * [2.1: A first look at a neural network](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/2.1-a-first-look-at-a-neural-network.ipynb)
* Chapter 3:
    * [3.5: Classifying movie reviews](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/3.5-classifying-movie-reviews.ipynb)
    * [3.6: Classifying newswires](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/3.6-classifying-newswires.ipynb)
    * [3.7: Predicting house prices](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/3.7-predicting-house-prices.ipynb)

* Chapter 4:
    * [4.4: Underfitting and overfitting](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/4.4-overfitting-and-underfitting.ipynb)

* Chapter 5:
    * [5.1: Introduction to convnets](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/5.1-introduction-to-convnets.ipynb)

* [5.2: Using convnets with small datasets](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/5.2-using-convnets-with-small-datasets.ipynb)
    * [5.3: Using a pre-trained convnet](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/5.3-using-a-pretrained-convnet.ipynb)
    * [5.4: Visualizing what convnets learn](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/5.4-visualizing-what-convnets-learn.ipynb)
* Chapter 6:
    * [6.1: One-hot encoding of words or characters](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/6.1-one-hot-encoding-of-words-or-characters.ipynb)
    * [6.1: Using word embeddings](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/6.1-using-word-embeddings.ipynb)
    * [6.2: Understanding RNNs](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/6.2-understanding-recurrent-neural-networks.ipynb)
    * [6.3: Advanced usage of RNNs]()
    * [6.4: Sequence processing with convnets]()
* Chapter 8:
    * [8.1: Text generation with LSTM](https://nbviewer.jupyter.org/github/birolkuyumcu/deep-learning-with-python-notebooks-tf2.x/blob/master/8.1-text-generation-with-lstm.ipynb)
    * [8.2: Deep dream]()
    * [8.3: Neural style transfer]()
    * [8.4: Generating images with VAEs]()
    * [8.5: Introduction to GANs]()

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