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Added Bayesian lessons to DeepSchool

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sachinruk committed Mar 24, 2018
1 parent bf1dd02 commit 5faea7f80d635944a846bf48e8ec95e5a1c7443c
Showing with 2,360 additions and 116 deletions.
  1. +24 −0 Bayesian_stats/Dockerfile
  2. +247 −0 Bayesian_stats/Lesson 0 - Coin Toss.ipynb
  3. +730 −0 Bayesian_stats/Lesson 1 - Gaussian Random Walks.ipynb
  4. +760 −0 Bayesian_stats/Lesson 2 - Multiple Stocks Example.ipynb
  5. +10 −0 Bayesian_stats/Readme.md
  6. +4 −0 Bayesian_stats/run.sh
  7. +2 −2 DL-Keras_Tensorflow/Lesson 0 - LinRegression.ipynb
  8. +66 −1 DL-Keras_Tensorflow/Lesson 01 - PenalisedRegression - Solutions.ipynb
  9. +34 −1 DL-Keras_Tensorflow/Lesson 02 - GradientDescent.ipynb
  10. +26 −1 DL-Keras_Tensorflow/Lesson 03 - TF_intro.ipynb
  11. +34 −1 DL-Keras_Tensorflow/Lesson 04 - NeuralNets.ipynb
  12. +27 −2 DL-Keras_Tensorflow/Lesson 05 - Keras Sentiment Analysis - Solutions.ipynb
  13. +26 −1 DL-Keras_Tensorflow/Lesson 06 - contraception.ipynb
  14. +26 −1 DL-Keras_Tensorflow/Lesson 09 - Hyperparameters - Solutions.ipynb
  15. +55 −86 DL-Keras_Tensorflow/Lesson 11 - Convolutional Neural Nets - Solutions.ipynb
  16. +26 −1 DL-Keras_Tensorflow/Lesson 12 - CIFAR10 CNN - Solutions.ipynb
  17. +27 −2 DL-Keras_Tensorflow/Lesson 13 - Transfer Learning - Solutions.ipynb
  18. +27 −3 DL-Keras_Tensorflow/Lesson 14 - Sentiment Analysis - RNNs - Solutions.ipynb
  19. +27 −2 DL-Keras_Tensorflow/Lesson 15 - LSTM Shakespeare generator - Solutions.ipynb
  20. +28 −3 DL-Keras_Tensorflow/Lesson 16 - LSTM Trump Tweets - Solutions.ipynb
  21. +28 −3 DL-Keras_Tensorflow/Lesson 17 - LSTM part 2 Trump Tweets - Solutions.ipynb
  22. +25 −1 DL-Keras_Tensorflow/Lesson 18 - Fake News Classification - Solutions.ipynb
  23. +26 −2 DL-Keras_Tensorflow/Lesson 19 - Seq2Seq - Date translator - Solutions.ipynb
  24. +49 −2 DL-Keras_Tensorflow/Lesson 20 - Deep Q Learning - Solutions.ipynb
  25. +26 −1 DL-Keras_Tensorflow/Lesson 21 - Generative Adversarial Networks.ipynb
  26. BIN images/gan_demo.png
@@ -0,0 +1,24 @@
FROM sachinruk/miniconda3

RUN conda install -y \
h5py \
pandas \
jupyter \
matplotlib \
seaborn \
scikit-learn \
pandas

RUN conda config --append channels conda-forge

RUN conda install -c conda-forge -y pymc3

RUN conda install -y mkl-service

RUN conda clean --yes --tarballs --packages --source-cache

VOLUME /notebook
WORKDIR /notebook
EXPOSE 8888
CMD jupyter notebook --allow-root --no-browser --ip=0.0.0.0 --NotebookApp.token=

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@@ -0,0 +1,10 @@
# Bayesian Workshops
A set of introductory tutorials for Bayesian analysis with pymc3.

## Installation
Run the following commands:
```
git clone https://github.com/sachinruk/bayes_school.git
cd bayes_school
bash run.sh
```
@@ -0,0 +1,4 @@
docker pull sachinruk/bayesian_docker
mkdir -p ../data
docker run -d -p 8888:8888 -v $${PWD}:/notebook \
-v $${PWD}/../data:/notebook/data sachinruk/bayesian_docker
@@ -37,7 +37,7 @@
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7fba2c561320>"
"<IPython.lib.display.YouTubeVideo at 0x103f77470>"
]
},
"execution_count": 1,
@@ -952,7 +952,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.3"
},
"latex_envs": {
"bibliofile": "biblio.bib",

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@@ -10,6 +10,39 @@
"https://www.youtube.com/watch?v=IxBYhjS295w"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/IxBYhjS295w\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x10d296438>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo(\"IxBYhjS295w\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
@@ -659,7 +692,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.3"
},
"latex_envs": {
"bibliofile": "biblio.bib",
@@ -24,6 +24,31 @@
"\\end{align}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/q5iL3XYFv2M?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/q5iL3XYFv2M?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
@@ -709,7 +734,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.3"
},
"latex_envs": {
"bibliofile": "biblio.bib",

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@@ -6,12 +6,37 @@
"source": [
"# Sentiment Analysis\n",
"We will use the IMDB sentiment analysis database in this tutorial. The main idea that is used in this tutorial is that certain words are enough to establish the sentiment of a given sentence. The word order is discarded in this particular tutorial.\n",
"<img src=\"./images/angry_happy_dogo.png\" alt=\"dogo\" style=\"width: 500px;\"/>\n",
"<img src=\"../images/angry_happy_dogo.png\" alt=\"dogo\" style=\"width: 500px;\"/>\n",
"\n",
"## References\n",
"1. http://ai.stanford.edu/~amaas/data/sentiment/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/TIRsS6ktXqA?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/TIRsS6ktXqA?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
@@ -975,7 +1000,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.3"
},
"latex_envs": {
"bibliofile": "biblio.bib",
@@ -9,6 +9,31 @@
"https://www.youtube.com/watch?v=wSXGlvTR9UM"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/wSXGlvTR9UM?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/wSXGlvTR9UM?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
@@ -1725,7 +1750,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.3"
},
"latex_envs": {
"bibliofile": "biblio.bib",
@@ -1240,6 +1240,31 @@
"## Best Hyper parameters"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/eEGbTd7AG9s?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/eEGbTd7AG9s?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
]
},
{
"cell_type": "code",
"execution_count": 26,
@@ -1435,7 +1460,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.3"
},
"latex_envs": {
"bibliofile": "biblio.bib",
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