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All basic ML notebooks in TensorFlow 2.0 + Keras are complete

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GokuMohandas committed Dec 3, 2019
1 parent 6f09bea commit 9f627f56c9bb1b3a5f9433eee7432b74508b1a64
Showing with 22 additions and 13 deletions.
  1. +10 βˆ’0 CHANGES.md
  2. +1 βˆ’1 LICENSE
  3. +11 βˆ’12 README.md
@@ -6,6 +6,16 @@ Releases are displayed in reverse chronological order and each release will cont
- Minor version Y (x.Y.z): When a notebook lesson is complete.
- Patch version X (x.y.Z): Changes made in between notebook lessons.

## [v2.0](https://github.com/practicalAI/practicalAI/releases/tag/v2.0)
#### New:
- all basic ML notebooks in TensorFlow 2.0 + Keras
#### Changed:
- explanations for all concepts
- visualizations
- interpretability
- "From scratch" implementations
#### Fixed:

## [v1.2.0](https://github.com/practicalAI/practicalAI/releases/tag/v1.2.0)
#### New:
- πŸ”’ NumPy lesson is complete.
@@ -1,6 +1,6 @@
MIT License

Copyright (c) 2019 practicalAI
Copyright (c) 2020 practicalAI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
@@ -9,8 +9,8 @@ A <i><b>practical</b></i> approach to machine learning.
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<sub>Created by
@@ -59,21 +59,21 @@ contributors
</ul>
</td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/00_Notebooks.ipynb">πŸ““ Notebooks</a></td>
<td><a>πŸ“ˆ Linear Regression</a></td>
<td><a>πŸ”Ž Data & Models</a></td>
<td><a>οΈπŸ–Ό Convolutional Neural Networks</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/04_Linear_Regression.ipynb">πŸ“ˆ Linear Regression</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/07_Data_and_Models.ipynb">πŸ”Ž Data & Models</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/10_Convolutional_Neural_Networks.ipynb">οΈπŸ–Ό Convolutional Neural Networks</a></td>
</tr>
<tr>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/01_Python.ipynb">🐍 Python</a></td>
<td><a>πŸ“Š Logistic Regression</a></td>
<td><a>πŸ›  Utilities</a></td>
<td><a>πŸ‘‘ Embeddings</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/05_Logistic_Regression.ipynb">πŸ“Š Logistic Regression</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/08_Utilities.ipynb">πŸ›  Utilities</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/11_Embeddings.ipynb">πŸ‘‘ Embeddings</a></td>
</tr>
<tr>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/02_NumPy.ipynb">πŸ”’ NumPy</a></td>
<td><a>οΈπŸŽ› Multilayer Perceptrons</a></td>
<td><a>οΈβœ‚οΈ Preprocessing</a></td>
<td><a>πŸ“— Recurrent Neural Networks</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/06_Multilayer_Perceptrons.ipynb">οΈπŸŽ› Multilayer Perceptrons</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/09_Preprocessing.ipynb">οΈβœ‚οΈ Preprocessing</a></td>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/12_Recurrent_Neural_Networks.ipynb">πŸ“— Recurrent Neural Networks</a></td>
</tr>
<tr>
<td><a href="https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/03_Pandas.ipynb">🐼 Pandas</a></td>
@@ -220,7 +220,6 @@ contributors
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