From c08f997de81f0b8d7df387274ed4bb8599062817 Mon Sep 17 00:00:00 2001 From: Rakshit Kumar Singh Date: Mon, 22 Jan 2024 15:58:41 +0000 Subject: [PATCH] content update --- new-website/utils/tutorials/contents.md | 86 ++++++++++++------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/new-website/utils/tutorials/contents.md b/new-website/utils/tutorials/contents.md index 0f919105..34cbd54a 100644 --- a/new-website/utils/tutorials/contents.md +++ b/new-website/utils/tutorials/contents.md @@ -1,45 +1,45 @@ # Conents -- About_nODE_Using_Torchdiffeq_in_Deepchem -- Advanced_Model_Training -- Advanced_model_training_using_hyperopt -- An_Introduction_To_MoleculeNet -- Atomic_Contributions_for_Molecules -- Conditional_Generative_Adversarial_Networks -- Creating_Models_with_TensorFlow_and_PyTorch -- Creating_a_high_fidelity_model_from_experimental_data -- DeepQMC_tutorial -- Deep_probabilistic_analysis_of_single-cell_omics_data -- Exploring_Quantum_Chemistry_with_GDB1k -- Generating_molecules_with_MolGAN -- Going_Deeper_on_Molecular_Featurizations -- Hierarchial_Moelcular_Generation_with_HierVAE -- Interactive_Model_Evaluation_with_Trident_Chemwidgets -- Introducing_JaxModel_and_PINNModel -- Introduction_To_Material_Science -- Introduction_to_Bioinformatics -- Introduction_to_Equivariance -- Introduction_to_GROVER -- Introduction_to_Gaussian_Processes -- Introduction_to_Graph_Convolutions -- Introduction_to_Model_Interpretability -- Introduction_to_Molecular_Attention_Transformer -- Large_Scale_Chemical_Screens -- Learning_Unsupervised_Embeddings_for_Molecules -- Modeling_Protein_Ligand_Interactions -- Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions -- Molecular_Fingerprints -- Multisequence_Alignments -- Physics_Informed_Neural_Networks -- Protein_Deep_Learning -- Putting_Multitask_Learning_to_Work -- PytorchLightning_Integration +- About Neural ODE : Using Torchdiffeq with Deepchem +- Advanced Model Training +- Advanced model training using hyperopt +- An Introduction To MoleculeNet +- Atomic Contributions for Molecules +- Conditional Generative Adversarial Networks +- Creating Models with TensorFlow and PyTorch +- Creating a high fidelity model from experimental data +- DeepQMC tutorial +- Deep probabilistic analysis of single-cell omics data +- Exploring Quantum Chemistry with GDB1k +- Generating molecules with MolGAN +- Going Deeper on Molecular Featurizations +- Hierarchial Moelcular Generation with HierVAE +- Interactive Model Evaluation with Trident Chemwidgets +- Introducing JaxModel and PINNModel +- Introduction To Material Science +- Introduction to Bioinformatics +- Introduction to Equivariance +- Introduction to GROVER +- Introduction to Gaussian Processes +- Introduction to Graph Convolutions +- Introduction to Model Interpretability +- Introduction to Molecular Attention Transformer +- Large Scale Chemical Screens +- Learning Unsupervised Embeddings for Molecules +- Modeling Protein Ligand Interactions +- Modeling Protein Ligand Interactions With Atomic Convolutions +- Molecular Fingerprints +- Multisequence Alignments +- Physics Informed Neural Networks +- Protein Deep Learning +- Putting Multitask Learning to Work +- PytorchLightning Integration - Scanpy -- The_Basic_Tools_of_the_Deep_Life_Sciences -- Training_a_Generative_Adversarial_Network_on_MNIST -- Training_a_Normalizing_Flow_on_QM9 -- Training_an_Exchange_Correlation_Functional_using_Deepchem -- Transfer_Learning_With_ChemBERTa_Transformers -- Uncertainty_In_Deep_Learning -- Using_Reinforcement_Learning_to_Play_Pong -- Working_With_Datasets -- Working_With_Splitters +- The Basic Tools of the Deep Life Sciences +- Training a Generative Adversarial Network on MNIST +- Training a Normalizing Flow on QM9 +- Training an Exchange Correlation Functional using Deepchem +- Transfer Learning With ChemBERTa Transformers +- Uncertainty In Deep Learning +- Using Reinforcement Learning to Play Pong +- Working With Datasets +- Working With Splitters