Machine Learning Applied
@MachineLearnApp
We apply machine learning to bioinformatics, cheminformatics, baseball data, baseketball data, and more.
قد يعجبك
Visualizing Correlations Among #Dow 30 #Stocks Via #NetworkX machinelearningapplied.com/visualizing-co…
Paper: XPySom: High-Performance Self-Organizing Maps – Mancini et al 2020 machinelearningapplied.com/paper-xpysom-h… #MachineLearning #Som
Finding Similar Stocks Via Fast #GPU Based #NearestNeighbors with #Faiss machinelearningapplied.com/finding-simila… #machinelearning
Fast #GPU Based #NearestNeighbors with #Faiss machinelearningapplied.com/fast-gpu-based… #MachineLearning
Compound Generator with Graph Networks, GraphINVENT #chemoinformatics #RDKit #PyTorch iwatobipen.wordpress.com/2020/08/29/com…
iwatobipen.wordpress.com
Compound Generator with Graph Networks, GraphINVENT #chemoinformatics #RDKit #PyTorch
Here is a new article from Esben et. al. about de novo compound generator with graph network which is named GraphINVENT. PDF Graph based approach has advantage for compound generation compared to S…
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: dgl.ai/release/2020/0…
Hyperparameter Search With #GPyOpt Part 3 – #Keras (CNN) Classification and Ensembling machinelearningapplied.com/hyperparameter… #machinelearning #bayesianoptimization #deeplearning
Numba 0.49.1 patch release is live! This fixes a number of reported issues. See details here: groups.google.com/a/continuum.io…
Keras has a new website, which includes a 100% refreshed list of developer guides and code examples. keras.io
Ebook: #DeepLearning for Toxicity and Disease Prediction – Gong, Zhang, Chen (eds) 2020 machinelearningapplied.com/ebook-deep-lea… #machinelearning #bioinformatics #cheminformatics
Hyperparameter Search With #GPyOpt Part 2 – #XGBoost Classification and Ensembling machinelearningapplied.com/hyperparameter… #machinelearning #bayesianoptimization
Antimicrobial peptide generated by GAN. Passed in vitro validation. Twice as strong as ampicillin. Check out our preprint in @ChemRxiv doi.org/10.26434/chemr…
Paper: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery – Xu et al 2017 machinelearningapplied.com/paper-seq2seq-… #cheminformatics #chemoinformatics #machinelearning #deeplearning
Paper: CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations – Paul et al 2018 machinelearningapplied.com/paper-chemixne… #cheminformatics #chemoinformatics #machinelearning #deeplearning
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. amzn.to/2wotTnN
Paper: A Tutorial on Bayesian Optimization – Frazier 2018 machinelearningapplied.com/paper-a-tutori… #Bayesianoptimization #machinelearning
Hyperparameter Search With #GPyOpt Part 1 – #Scikit-learn Classification and Ensembling machinelearningapplied.com/hyperparameter… #machinelearning #bayesianoptimization
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: dgl.ai/release/2020/0…
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. github.com/optuna/optuna/…
github.com
Release v1.3.0 · optuna/optuna
This is the release note of v1.3.0. Highlights Experimental CMA-ES A new built-in CMA−ES sampler is available. It is still an experimental feature, but we recommend trying it because it is much fas...
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: github.com/szymonmaszke/t…
github.com
GitHub - szymonmaszke/torchlayers: Shape and dimension inference (Keras-like) for PyTorch layers...
Shape and dimension inference (Keras-like) for PyTorch layers and neural networks - szymonmaszke/torchlayers
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قد يعجبك
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AMLD Intelligence Summit
@appliedmldays -
Towards AI
@towards_AI -
Machine Learning Journal (@[email protected])
@MLJ_Social -
Sasha Rush
@srush_nlp -
Kyle Wiggers
@Kyle_L_Wiggers -
Machine Learning Trends
@MLTrendss -
Machine Learning Dept. at Carnegie Mellon
@mldcmu -
Jason Wei
@_jasonwei -
UMD Center for Machine Learning
@ml_umd -
Nordic AI Institute
@nordicinst -
The Machine Learning Times
@ML_Times -
Pinecone
@pinecone -
Physics & Astronomy Zone
@zone_astronomy -
Christian Szegedy
@ChrSzegedy -
🤖🇨🇭AI & Machine Learning @ HSLU
@hslu_aiml
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