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Fixes in names #36

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61d9191
Create devcontainer.json
sudo-rsingh Jan 31, 2024
9c47fdf
fixes
sudo-rsingh Jan 31, 2024
7905f70
build
sudo-rsingh Jan 31, 2024
acbf2a3
Revert "build"
sudo-rsingh Jan 31, 2024
8457b3a
Revert "fixes"
sudo-rsingh Jan 31, 2024
2736ebe
Reapply "fixes"
sudo-rsingh Jan 31, 2024
9a61736
Reapply "build"
sudo-rsingh Jan 31, 2024
7a2e7ff
ignore revert
sudo-rsingh Jan 31, 2024
550f22f
citation
sudo-rsingh Jan 31, 2024
08027a2
acknowledgement
sudo-rsingh Feb 2, 2024
8ef3b12
markdown revert
sudo-rsingh Feb 12, 2024
60c7457
Update contents.md
sudo-rsingh Feb 12, 2024
40e4a97
ci
sudo-rsingh Feb 13, 2024
9dc31e6
revert ci
sudo-rsingh Feb 13, 2024
5b1fa80
location change
sudo-rsingh Feb 13, 2024
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5 changes: 5 additions & 0 deletions .devcontainer/devcontainer.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,5 @@
{
"image": "mcr.microsoft.com/devcontainers/universal:2",
"features": {
}
}
27 changes: 27 additions & 0 deletions new-website/utils/tutorials/acknowledgement.html
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Expand Up @@ -6,16 +6,43 @@
padding-right: 80px;
font-size: 50px;
}
h2 {
margin-top: 250px;
padding-right: 80px;
font-size: 20px;
}
.general {
padding-left: 80px;
padding-right: 80px;
font-size: 20px;
}
.citation {
padding-left: 80px;
padding-right: 80px;
font-size: 15px;
}
</style>
<body>
<h1>Acknowledgement</h1>
<p class='general'>
We acknowledge the DeepChem community for their contributions and support.
</p>
<p class='citation'>
<h2>Citing This Book:</h2>
<br>
@manual{
<br>
title={The DeepChem Book},
<br>
organization={DeepChem},
<br>
author={Ramsundar, Bharath and DeepChem Community},
<br>
howpublished = {\url{https://deepchem.io/tutorials}},
<br>
year={2024},
<br>
}
</p>
</body>
</html>
2 changes: 0 additions & 2 deletions new-website/utils/tutorials/acknowledgement.md
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13 changes: 8 additions & 5 deletions new-website/utils/tutorials/build_pdf_book.py
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Expand Up @@ -48,7 +48,10 @@ def html_to_pdf():
chapter = pd.read_csv(INFO_PATH + "-".join(i))
for j in chapter["File Name"]:
print(i, j)
pdfkit.from_file(DATA_PATH + j[:-5] + "html", PDF_PATH + j[:-5] + "pdf")
try:
pdfkit.from_file(DATA_PATH + j[:-5] + "html", PDF_PATH + j[:-5] + "pdf")
except:
pass

def merge_pdf():
"""Merges the compiled PDFs."""
Expand Down Expand Up @@ -86,8 +89,8 @@ def compile_information_pages():
pdfkit.from_file('acknowledgement.html', 'storage/acknowledgement.pdf')

if __name__ == "__main__":
os.system("mkdir " + PDF_PATH)
html_to_pdf()
#os.system("mkdir " + PDF_PATH)
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Remove cruft

#html_to_pdf()
merge_pdf()
compile_information_pages()
merge_pdf_pages(['storage/title.pdf', 'storage/acknowledgement.pdf', 'storage/contents.pdf', 'storage/full_pdf.pdf'])
#compile_information_pages()
#merge_pdf_pages(['storage/title.pdf', 'storage/acknowledgement.pdf', 'storage/contents.pdf', 'storage/full_pdf.pdf'])
66 changes: 31 additions & 35 deletions new-website/utils/tutorials/contents.html
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Expand Up @@ -19,7 +19,7 @@ <h2>1. Introduction To Deepchem</h2>
<li>The Basic Tools of the Deep Life Sciences</li>
<li>Working With Datasets</li>
<li>An Introduction To MoleculeNet</li>
<li>Molecular Fingerprints</li>
<li>Molecular Fingerprints: Representing Molecules for Deep-Learning</li>
<li>Creating Models with TensorFlow and PyTorch</li>
<li>Introduction to Graph Convolutions</li>
<li>Going Deeper on Molecular Featurizations</li>
Expand All @@ -33,89 +33,85 @@ <h2>1. Introduction To Deepchem</h2>
<li>Training a Generative Adversarial Network on MNIST</li>
<li>Advanced model training using hyperopt</li>
<li>Introduction to Gaussian Processes</li>
<li>PytorchLightning Integration</li>
<li>Pytorch Lightning Integration for DeepChem Models</li>
</ol>
</li>
<li>
<h2>2. Molecular Machine Learning</h2>
<h2>2. More Molecular Machine Learning</h2>
<ol>
<li>Molecular Fingerprints
<li>Going Deeper on Molecular Featurizations
<li>Learning Unsupervised Embeddings for Molecules
<li>Atomic Contributions for Molecules
<li>Interactive Model Evaluation with Trident Chemwidgets
<li>Transfer Learning With ChemBERTa Transformers
<li>Training a Normalizing Flow on QM9
<li>Large Scale Chemical Screens
<li>Introduction to Molecular Attention Transformer
<li>Generating molecules with MolGAN
<li>Introduction to GROVER
<li>Going Deeper on Molecular Featurizations</li>
<li>Learning Unsupervised Embeddings for Molecules</li>
<li>Atomic Contributions for Molecules</li>
<li>Interactive Model Evaluation with Trident Chemwidgets</li>
<li>Transfer Learning With ChemBERTa Transformers</li>
<li>Training a Normalizing Flow on QM9</li>
<li>Large Scale Chemical Screens</li>
<li>Introduction to Molecular Attention Transformer</li>
<li>Generating molecules with MolGAN</li>
<li>Introduction to GROVER</li>
</ol>
</li>
<li>
<h2>3. Modeling Proteins</h2>
<ol>
<li>Protein Deep Learning
<li>Protein Deep Learning</li>
</ol>
</li>
<li>
<h2>4. Protein Ligand Modeling</h2>
<ol>
<li>Modeling Protein Ligand Interactions
<li>Modeling Protein Ligand Interactions With Atomic Convolutions
<li>DeepChemXAlphafold
<li>Modeling Protein Ligand Interactions</li>
<li>Applications of DeepChem with Alphafold: Docking and protein-ligand interaction from protein sequence</li>
</ol>
</li>
<li>
<h2>5. Quantum Chemistry</h2>
<ol>
<li>Exploring Quantum Chemistry with GDB1k
<li>DeepQMC tutorial
<li>Training an Exchange Correlation Functional using Deepchem
<li>Exploring Quantum Chemistry with GDB1k</li>
<li>DeepQMC integration with DeepChem tutorial</li>
<li>Training an Exchange Correlation Functional using Deepchem</li>
</ol>
</li>
<li>
<h2>6. Bioinformatics</h2>
<ol>
<li>Introduction to Bioinformatics
<li>Multisequence Alignments
<li>Deep probabilistic analysis of single-cell omics data
<li>Introduction to Bioinformatics</li>
<li>Multisequence Alignments</li>
<li>Deep probabilistic analysis of single-cell omics data</li>
</ol>
</li>
<li>
<h2>7. Material Sciences</h2>
<ol>
<li>Introduction To Material Science
<li>Introduction To Material Science</li>
</ol>
</li>
<li>
<h2>8. Machine Learning Methods</h2>
<ol>
<li>Using Reinforcement Learning to Play Pong
<li>Introduction to Model Interpretability
<li>Uncertainty In Deep Learning
<li>Using Reinforcement Learning to Play Pong</li>
<li>Introduction to Model Interpretability</li>
<li>Uncertainty In Deep Learning</li>
</ol>
</li>
<li>
<h2>9. Deep Differential Equations</h2>
<ol>
<li>Physics Informed Neural Networks
<li>Introducing JaxModel and PINNModel
<li>About Neural ODE : Using Torchdiffeq with Deepchem
<li>Physics Informed Neural Networks</li>
<li>Introducing JaxModel and PINNModel</li>
<li>About Neural ODE : Using Torchdiffeq with Deepchem</li>
</ol>
</li>
<li>
<h2>10. Equivariance</h2>
<ol>
<li>Introduction to Equivariance
<li>Modeling Protein Ligand Interactions With Atomic Convolutions
<li>DeepChemXAlphafold
<li>Introduction to Equivariance</li>
</ol>
</li>
<li>
<h2>11. Olfaction</h2>
<ol>
<li>Predict Multi Label Odor Descriptors using OpenPOM
<li>Predict Multi Label Odor Descriptors using OpenPOM</li>
</ol>
</li>
</ul>
Expand Down
71 changes: 0 additions & 71 deletions new-website/utils/tutorials/contents.md
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2 changes: 1 addition & 1 deletion new-website/utils/tutorials/title.html
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Expand Up @@ -21,7 +21,7 @@
<body>
<h1>The DeepChem Book</h1>
<p class='general'>
Democratizing Deep-Learning for Drug Discovery Quantum Chemistry, Materials Science and Biology
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
</p>
<p class='author'>
Bharath Ramsundar
Expand Down
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