Machine Learning Applied

We apply machine learning to bioinformatics, cheminformatics, baseball data, baseketball data, and more.

[Machine Learning Applied @MachineLearnApp]

Visualizing Correlations Among #Dow 30 #Stocks Via #NetworkX https://t.co/PDIn2jwLGq

[Machine Learning Applied @MachineLearnApp]

Paper: XPySom: High-Performance Self-Organizing Maps – Mancini et al 2020 https://t.co/UCZmQ6nv90 #MachineLearning #Som

[Machine Learning Applied @MachineLearnApp]

Finding Similar Stocks Via Fast #GPU Based #NearestNeighbors with #Faiss https://t.co/dwy0KrmXrA #machinelearning

[Machine Learning Applied @MachineLearnApp]

Fast #GPU Based #NearestNeighbors with #Faiss https://t.co/XO1vugbgL4 #MachineLearning

[pen(Taka) @iwatobipen]

Compound Generator with Graph Networks, GraphINVENT #chemoinformatics #RDKit #PyTorch https://t.co/etMw1uflon

[DeepGraphLibrary @DGLGraph]

DGL v0.5 has been released! A major update with a focus on better documentation, GNN kernels, distributed training and so on. Read the summary of highlights: https://t.co/RYnuSkMI16

[Machine Learning Applied @MachineLearnApp]

Hyperparameter Search With #GPyOpt Part 3 – #Keras (CNN) Classification and Ensembling https://t.co/NIqobLaaHO #machinelearning #bayesianoptimization #deeplearning

[Numba @numba_jit]

Numba 0.49.1 patch release is live! This fixes a number of reported issues. See details here: https://t.co/U3prAkoqxw

[Machine Learning Applied @MachineLearnApp]

#Keras #DeepLearning

[François Chollet @fchollet]

Keras has a new website, which includes a 100% refreshed list of developer guides and code examples.

https://t.co/m6mT8SrKDD

[Machine Learning Applied @MachineLearnApp]

Ebook: #DeepLearning for Toxicity and Disease Prediction – Gong, Zhang, Chen (eds) 2020 https://t.co/3xhOgPMsfk #machinelearning #bioinformatics #cheminformatics

[Machine Learning Applied @MachineLearnApp]

Hyperparameter Search With #GPyOpt Part 2 – #XGBoost Classification and Ensembling https://t.co/ZHSOjvdK16 #machinelearning #bayesianoptimization

[Koji Tsuda @kojitsuda]

Antimicrobial peptide generated by GAN. Passed in vitro validation. Twice as strong as ampicillin. Check out our preprint in @ChemRxiv https://t.co/9sxhEjiYWo

[Machine Learning Applied @MachineLearnApp]

Paper: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery – Xu et al 2017 https://t.co/srKObU6pQP #cheminformatics #chemoinformatics #machinelearning #deeplearning

[Machine Learning Applied @MachineLearnApp]

Paper: CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations – Paul et al 2018 https://t.co/rW7f8TsTfx #cheminformatics #chemoinformatics #machinelearning #deeplearning

[Rowel Atienza 🇵🇭 @jacobe]

2nd ed of my book, Advanced Deep Learning w/ TF2 & Keras, was released days before the global pandemic. Was excited to announce it but weeks of sad news sapped my energy. Announcing it now as a historical footnote. Hopefully, WFH & SFH still find useful.

https://t.co/AMpRCSYXDV

[Machine Learning Applied @MachineLearnApp]

Paper: A Tutorial on Bayesian Optimization – Frazier 2018 https://t.co/2UN4QtjnDV #Bayesianoptimization #machinelearning

[Machine Learning Applied @MachineLearnApp]

Hyperparameter Search With #GPyOpt Part 1 – #Scikit-learn Classification and Ensembling https://t.co/PKMvj3OP18 #machinelearning #bayesianoptimization

[Machine Learning Applied @MachineLearnApp]

#MachineLearning #DeepLearning

[DeepGraphLibrary @DGLGraph]

A new release v0.4.3 is out! This includes TensorFlow support; DGL-KE and DGL-LifeSci, two packages for knowledge graph embedding and chemi- and bio-informatics respectively; Graph sampling on heterograph, with multi-GPU acceleration. Blogpost is here: https://t.co/TAZ05Ze7V3

[OptunaAutoML @OptunaAutoML]

Released #Optuna v1.3.0! New experimental features including a faster CMA-ES sampler and hyperparameter importance assessment. The per-trial log is also improved. https://t.co/av2QFDGrtK

[Machine Learning Applied @MachineLearnApp]

#PyTorch

[Sebastian Raschka @rasbt]

torchlayers: an interesting new abstraction layer API on top of PyTorch, which is basically an "Sequential" object w/o the need to call it in the forward method. And, more interestingly, it performs automatic dimensionality and shape deduction: https://t.co/0jIV40V2be