Deep-Learning-Bootcamp

Introduction to deep learning with Python

We start with an overview of the basicspandas and scikit-learn to pre-process our data and perform simple machine learning tasks. We then dive into Keras with tensorflow backend and focus on some of the most common applications of deep learning.

This repository can be accessed via this short link:

bit.ly/DL-Bootcamp

Textbooks

Deep Learning with Python (2nd edition) by François Chollet
Python Data Science Handbook by Jake VanderPlas

Acknowledgment

Some content of a handful of notebooks come from Python Data Science Handbook or A Whirlwind Tour of Python by Jake VanderPlas (under the MIT license. Read more at the Open Source Initiative). Modifications and updates have been made.
The content of notebook 02-Exploratory-Data-Analysis.ipynb comes primarily from a Kaggle notebook by Aguiar.
Notebooks on deep learning are corresponding chapters of Deep Learning with Python (2nd edition)

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
.gitignore		.gitignore
01-Tidy-Data.ipynb		01-Tidy-Data.ipynb
02-Exploratory-Data-Analysis.ipynb		02-Exploratory-Data-Analysis.ipynb
03-Machine-Learning-Overview.ipynb		03-Machine-Learning-Overview.ipynb
04-Introducing-Scikit-Learn.ipynb		04-Introducing-Scikit-Learn.ipynb
05-Classification.ipynb		05-Classification.ipynb
06-Regression.ipynb		06-Regression.ipynb
07-Intro-to-Unsupervised.ipynb		07-Intro-to-Unsupervised.ipynb
08-Exploring-Hand-written-Digits.ipynb		08-Exploring-Hand-written-Digits.ipynb
09-ch02-mathematical-building-blocks.ipynb		09-ch02-mathematical-building-blocks.ipynb
10-ch04-getting-started-with-neural-networks.ipynb		10-ch04-getting-started-with-neural-networks.ipynb
11-ch05-fundamentals-of-ml.ipynb		11-ch05-fundamentals-of-ml.ipynb
12-ch07-working-with-keras.ipynb		12-ch07-working-with-keras.ipynb
13-ch08-intro-to-dl-for-computer-vision.ipynb		13-ch08-intro-to-dl-for-computer-vision.ipynb
14-ch09-part01-image-segmentation.ipynb		14-ch09-part01-image-segmentation.ipynb
15-ch09-part03-interpreting-what-convnets-learn.ipynb		15-ch09-part03-interpreting-what-convnets-learn.ipynb
16-Working-with-Time-Series.ipynb		16-Working-with-Time-Series.ipynb
17-ch10-dl-for-timeseries.ipynb		17-ch10-dl-for-timeseries.ipynb
18-ch11-part01-introduction.ipynb		18-ch11-part01-introduction.ipynb
19-ch11-part02-sequence-models.ipynb		19-ch11-part02-sequence-models.ipynb
20-ch11-part03-transformer.ipynb		20-ch11-part03-transformer.ipynb
21-ch11-part04-sequence-to-sequence-learning.ipynb		21-ch11-part04-sequence-to-sequence-learning.ipynb
22-ch12-part01-text-generation.ipynb		22-ch12-part01-text-generation.ipynb
23-ChatGPT-Completion-API.ipynb		23-ChatGPT-Completion-API.ipynb
24-ch12-part02-deep-dream.ipynb		24-ch12-part02-deep-dream.ipynb
25-ch12-part03-neural-style-transfer.ipynb		25-ch12-part03-neural-style-transfer.ipynb
26-ch12-part04-variational-autoencoders.ipynb		26-ch12-part04-variational-autoencoders.ipynb
27-ch12-part05-gans.ipynb		27-ch12-part05-gans.ipynb
LICENSE		LICENSE
README.md		README.md

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