| Probabilistic Modelling and Bayesian Inference, Zubin Gharamani |
http://mlss.tuebingen.mpg.de/2015/slides/ghahramani/lect1bayes.pdf |
Probabilstic modelling, Machine Learning |
Slides |
| High level explanation of Variational Inference |
https://www.cs.jhu.edu/~jason/tutorials/variational.html |
Bayesian, Variational Inference |
Webpage |
| Variational Autoencoder using an RNN in Keras |
http://alexadam.ca/ml/2017/05/05/keras-vae.html |
Variational Autoencoder, RNN, Keras, Python, Code |
Webpage |
| Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries |
https://arxiv.org/pdf/1805.00352.pdf |
Word2Vec, Doc2Vec, Sentiment Analysis, NLP |
Paper |
| Gaussian Processes and Kernels |
https://www.inf.ed.ac.uk/teaching/courses/mlpr/2016/notes/w7c_gaussian_process_kernels.pdf |
Gaussian Processes |
Notes |
| Overview of Edward: A probabilitic programming system |
http://dustintran.com/talks/Tran_Edward.pdf |
Edward, Probabilstic programming |
Slides |
| Edward: Linear Regression |
http://edwardlib.org/tutorials/supervised-classification |
Edward, Probabilistic programming |
Webpage |
| Gaussian Processes for Machine Learning in Python |
https://www.inf.ed.ac.uk/teaching/courses/mlpr/2016/notes/w7c_gaussian_process_kernels.pdf |
Gaussian Processes, Python |
Webpage |
| Object Detection with Tensorflow |
https://3sidedcube.com/guide-retraining-object-detection-models-tensorflow/ |
Object Detection, Tensorflow |
Webpage |
| Configuring the Tensorflow Object Detection Training Pipeline |
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/configuring_jobs.md |
Object Detection, Tensorflow |
Webpage |
| Word2Vec to Doc2Vec: An eaxmple with Gensim |
https://ireneli.eu/2016/07/27/nlp-05-from-word2vec-to-doc2vec-a-simple-example-with-gensim/ |
Word2Vec, Doc2Vec, NLP, Gensim |
Webpage |
| Why Deep Learning Methods use KL divergence |
https://digitalcommons.utep.edu/cgi/viewcontent.cgi?article=2188&context=cs_techrep |
Deep Learning, KL divergence |
Paper |
| Functions supported in KateX |
https://katex.org/docs/supported.html |
KateX |
Webpage |
| Introduction to Graphical Models and Bayesian Networks |
https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html |
Graphical models, Plate notation, Bayesian network |
Webpage |
| Implementing a variational autoencoder in Pytorch |
https://medium.com/@sikdar_sandip/implementing-a-variational-autoencoder-vae-in-pytorch-4029a819ccb6 |
Variational autoencoder, PyTorch |
Webpage |
| Ali Ghodsi Lecture on Variational autoencoder |
https://www.youtube.com/watch?v=uaaqyVS9-rM |
Variational autoencoder |
Youtube video |
| Jaan.io Variational autoencoder |
https://jaan.io/publications/ |
Variational autoencoder |
Webpage |
| Jeremy Jordan autoencoder |
https://www.jeremyjordan.me/autoencoders/ |
Autoencoder, architecture |
Webpage |
| Jeremy Jordan Variational autoencoder |
https://www.jeremyjordan.me/variational-autoencoders/ |
Variational autoencoder |
Webpage |
| Bernoulli and Binomial distributions |
https://www.youtube.com/watch?v=7mZksQ24MlI |
Bernoulli, Binomial, Distributions |
Youtube video |
| Dimensionality reduction overview from LLNL |
https://e-reports-ext.llnl.gov/pdf/240921.pdf |
Dimensionality reduction, PCA, MDS, ICA, Non linear PCA |
Report |
| Overview of Variational inference and comparison to MCMC by Jordan Blei |
https://arxiv.org/pdf/1601.00670.pdf |
Variational inference, MCMC, Jordan Blei, Very Readableiew of Variational Inference! |
Paper |
| In depth variational autoencoder |
http://ruishu.io/2018/03/14/vae/ |
Variational autoencoder |
Paper |
| Autoencoder using Tensorflow |
https://danijar.com/building-variational-auto-encoders-in-tensorflow/ |
Variational autoencoder, Tensorflow |
Paper |
| Machine Learning Conferences |
https://jackietseng.github.io/conference_call_for_paper/2018-2019-conferences.html |
Machine Learning, Conference |
Conference |
| Conference on Data Mining |
http://www.guide2research.com/conference/icdm-2019 |
Data Mining, Conference |
Conference |
| Conference on Visual Computing |
http://www.isvc.net |
Visual Computing, Conference |
Conference |
| Introduction to distributions |
https://www.datacamp.com/community/tutorials/probability-distributions-python |
Probability distributions, Python |
Webpage |
| Overview of distributions |
https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119197096.app03 |
Probability distributions/td>
| Book |
| Neural Architecture Search |
https://arxiv.org/pdf/1808.05377.pdf |
NAS, survey/td>
| paper |
| Setting up a Jupyter Notebook server |
https://jupyter-notebook.readthedocs.io/en/stable/public_server.html |
Jupyter Notebook, server/td>
| website |
| Modeling with uncertainty: Machine Learning |
https://blog.sigopt.com/posts/modeling-with-uncertainty |
Modeling, uncertainty/td>
| website |
| Bayesian optimization |
https://blog.sigopt.com/posts/bayesian-optimization-with-uncertainty |
Bayesian optimization/td>
| website |
| Hyperparameter optimization |
https://blog.sigopt.com/posts/evaluating-hyperparameter-optimization-strategies |
Hyperparameter optimization/td>
| website |
| SMAC |
https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf |
Hyperparameter optimization, Gaussian Process, Bayesian |
Paper |
| Overview of Automated Hyperparameter tuning |
https://indico.cern.ch/event/433556/contributions/1930567/attachments/1231738/1806005/fonarev_hyperparams.pdf |
Hyperparameter optimization, Overview |
Paper |
| Auto-Keras |
https://arxiv.org/pdf/1806.10282.pdf |
AutoML, Neural architecture search |
Paper |
| Algorithms for hyperparameter optimization |
http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf |
Hyperparameter optimization, TPE |
Paper |
| Bayesian optimization primer |
https://app.sigopt.com/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf |
Hyperparameter optimization, Overview |
Paper |
| Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures |
http://proceedings.mlr.press/v28/bergstra13.pdf |
Hyperparameter optimization, Computer Vision |
Paper |
| Tree structured Parzen Estimators |
https://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f |
Hyperparameter optimization, TPE |
website |
| Overview of Bayesian Optimization |
https://www.doc.ic.ac.uk/~mpd37/teaching/ml_tutorials/2017-11-08-Archambeau-Bayesian-optimization.pdf |
Hyperparameter optimization, Overview, Amazon, Reference |
Slides |
| Auto-Weka |
https://arxiv.org/pdf/1208.3719.pdf |
Hyperparameter optimization, SMAC, TPE |
Paper |
| Auto-sklearn |
https://ml.informatik.uni-freiburg.de/papers/15-NIPS-auto-sklearn-preprint.pdf |
Hyperparameter optimization, SMAC, Meta-learning |
Paper |
| Feature selection |
https://machinelearningmastery.com/an-introduction-to-feature-selection/ |
Feature selection |
website |
| Tensorflow slides |
https://www.math.purdue.edu/~nwinovic/slides/Getting_Started_with_TensorFlow_I.pdf_original |
Tensorflow |
slides |
| Greenplum with python pandas and nltk example |
https://dwhsys.com/2018/05/06/data-mining-in-mpp-database/ |
Green plum, machine learning |
blog |
| Gatsby slides repo |
https://github.com/fabe/gatsby-starter-deck |
Gatsby, HTML slides |
Repository |
| Deep Gaussian Process for Financial predictions |
https://medium.com/neuri/being-bayesian-and-thinking-deep-time-series-prediction-with-uncertainty-25ff581b056c |
Financial time series, Gaussian Process |
Blog |
| NLP comparison of ELMO, Bert and ULMFIT |
https://jalammar.github.io/illustrated-bert/ |
NLP, BERT, LSTM, ELMO, ULMFIT |
Blog |
| Data Science Workflows |
https://towardsdatascience.com/data-science-project-flow-for-startups-282a93d4508d/ |
Data Science, Workflow |
Blog |
| Evaluation of NLP frameworks |
https://towardsdatascience.com/paper-summary-evaluation-of-sentence-embeddings-in-downstream-and-linguistic-probing-tasks-5e6a8c63aab1/ |
NLP, Comparison of frameworks |
NLP comparison of ELMO, Bert and ULMFIT |
https://jalammar.github.io/illustrated-bert/ |
NLP, BERT, LSTM, ELMO, ULMFIT |
Blog |
| 4 Sequence Encoding Blocks You Must Know Besides RNN/LSTM in Tensorflow |
https://hanxiao.github.io/2018/06/24/4-Encoding-Blocks-You-Need-to-Know-Besides-LSTM-RNN-in-Tensorflow/ |
RNN, LSTM |
Blog |
| Transfer Learning in NLP for Tweet Stance Classification |
https://towardsdatascience.com/transfer-learning-in-nlp-for-tweet-stance-classification-8ab014da8dde |
NLP, ULMFIT |
Blog |
| Keras Attention Mechanism |
https://github.com/philipperemy/keras-attention-mechanism |
LSTM, GRU |
Website |
| How to code The Transformer in Pytorch |
https://towardsdatascience.com/how-to-code-the-transformer-in-pytorch-24db27c8f9ec |
RNN, NLP, PyTorch |
Blog |
| Complete Guide to spaCy |
https://nlpforhackers.io/complete-guide-to-spacy/ |
spaCy,NLP |
Blog |
| Use torchtext to Load NLP Datasets |
https://towardsdatascience.com/use-torchtext-to-load-nlp-datasets-part-i-5da6f1c89d84 |
PyTorch,NLP |
Blog |
| PyTorch Sentiment Analysis |
https://github.com/bentrevett/pytorch-sentiment-analysis |
PyTorch,torchtext |
Blog |
| How Does Attention Work in Encoder-Decoder Recurrent Neural Networks |
https://machinelearningmastery.com/how-does-attention-work-in-encoder-decoder-recurrent-neural-networks/ |
LSTM,RNN |
Blog |
| 2019 Bloomberg Data Science Research Grant Program |
https://www.techatbloomberg.com/data-science-research-grant-program-1/ |
data-science-research-grant-program-1 |
Website |
| Call for Reproducibility Workflows |
https://bids.berkeley.edu/news/call-reproducibility-workflows |
bids |
Blog |
| ODSC Grant Award |
https://odsc.com/odsc-grant-award |
odsc-grant-award |
website |
| $25 Million in Artificial Intelligence Grants from Google |
https://www.ictworks.org/artificial-intelligence-grants-google/#.XKzQ2OtKgWp |
artificial-intelligence-grants-google |
website |
| AI GRANT |
https://aigrant.org/ |
aigrant |
website |
| How to Use t-SNE Effectively |
https://distill.pub/2016/misread-tsne/ |
t-SNE |
website |
| Bayesian Reasoning and Machine Learning |
http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf |
Bayesian |
Paper |
| Advanced NLP with spaCy |
https://course.spacy.io/chapter1 |
NLP, spaCy |
Book |
| Getting started with PyMC3 |
https://docs.pymc.io/notebooks/getting_started.html |
PyMC3 |
Paper |
| Getting started with PyMC3 |
https://docs.pymc.io/notebooks/getting_started.html |
PyMC3 |
Paper |
| Predicting Movie Review Sentiment with BERT on TF Hub |
https://colab.research.google.com/github/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb#scrollTo=dCpvgG0vwXAZ |
BERT |
Notebook |
| Variational inference |
https://ermongroup.github.io/cs228-notes/inference/variational/Z |
MCMC |
Book |
| The variational auto-encoder |
https://ermongroup.github.io/cs228-notes/extras/vae/ |
vae |
Book |
| Sampling Methods |
https://ermongroup.github.io/cs228-notes/inference/sampling/ |
sampling |
Book |
| DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN |
https://www.groundai.com/project/doping-generative-data-augmentation-for-unsupervised-anomaly-detection-with-gan/ |
GAN, DOPING |
Paper |
| Text Variational Autoencoder in Keras |
http://alexadam.ca/ml/2017/05/05/keras-vae.html/ |
vae, keras-vae |
blog |
| VAE-Text-Generation |
https://github.com/Toni-Antonova/VAE-Text-Generation/blob/master/vae_nlp.ipynb |
vae, |
blog |
| Productionizing NLP Models |
https://medium.com/modern-nlp/productionizing-nlp-models-9a2b8a0c7d14 |
NLP |
blog |
| Intuitively Understanding Variational Autoencoders |
https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf |
vae,variational-autoencoders |
blog |
| Text Variational Autoencoder in Keras |
http://alexadam.ca/ml/2017/05/05/keras-vae.html |
vae,Keras |
blog |
| VAE-Text-Generation |
https://github.com/Toni-Antonova/VAE-Text-Generation/blob/master/vae_nlp.ipynb |
vae |
blog |
| Understanding the Deployment Cost of Cloud Computing Services for the Higher Education Institutions |
https://ieeexplore-ieee-org.ezproxy.lib.vt.edu/document/8939863/keywords |
cloud computing |
article |
| Adversarial Text Generation Without Reinforcement Learning |
https://arxiv.org/pdf/1810.06640.pdf |
Generative Adversarial Networks |
article |
| Introduction to Kurobako: A Benchmark Tool for Hyperparameter Optimization Algorithms |
https://tech.preferred.jp/en/blog/kurobako/ |
Optuna, Algorithms |
Blog |
| Nelder-Mead Optimization |
https://codesachin.wordpress.com/2016/01/16/nelder-mead-optimization/ |
Dimensions, Optimization |
Blog |
| Hyper-parameter optimization algorithms: a short review |
https://medium.com/criteo-labs/hyper-parameter-optimization-algorithms-2fe447525903 |
Algorithms, Hyper-parameter |
Article |
| Overview on Automatic Tuning of Hyperparameters |
https://indico.cern.ch/event/433556/contributions/1930567/attachments/1231738/1806005/fonarev_hyperparams.pdf |
Hyperparameter, Automatic tuning |
Article |
| Interpreting Generalized Linear Models |
https://www.datascienceblog.net/post/machine-learning/interpreting_generalized_linear_models/ |
Data, GML |
Article |
| Generalized Linear Models |
http://www.stat.columbia.edu/~madigan/W2025/notes/GLM.pdf/ |
Generalized linear models |
Article |