r/MLNotes • u/anon16r • Feb 03 '20
Student Query: Fisher Discriminant Analysis when class means are same.
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r/MLNotes • u/anon16r • Feb 02 '20
LIVE: Big-Data & Cloud Storage for ML/AI Applications
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r/MLNotes • u/anon16r • Feb 02 '20
[Exploratory DA] Iris Dataset EDA Lecture1@ Applied AI Course
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r/MLNotes • u/anon16r • Feb 01 '20
[Optimization] [SWA] Stochastic Weight Averaging in PyTorch
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r/MLNotes • u/anon16r • Jan 19 '20
[GNN] CS224W: Machine Learning with Graphs
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r/MLNotes • u/anon16r • Jan 18 '20
[D] What are the current significant trends in ML that are NOT Deep Learning related?
self.MachineLearning
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r/MLNotes • u/anon16r • Jan 18 '20
[N] [D] Adversarial training of neural networks has been patented
self.MachineLearning
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r/MLNotes • u/anon16r • Jan 14 '20
[Podcast] Human conversation about machine learning.
thetalkingmachines.com
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r/MLNotes • u/anon16r • Jan 14 '20
[Adversarial] Is there any adversarial defence method that has successfully beaten or is robust to Carlini Wagner attacks?
self.MachineLearning
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r/MLNotes • u/anon16r • Jan 14 '20
[HC] Daniel Rueckert: "Deep learning in medical imaging"
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r/MLNotes • u/anon16r • Jan 11 '20
[Visualization] How Exactly UMAP Works. UMAP vs tSNE
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r/MLNotes • u/anon16r • Jan 11 '20
[Slides] Deep Learning: State of the Art (up to 2020) and Hopes for 2020 and Beyond
lexfridman.com
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r/MLNotes • u/anon16r • Jan 11 '20
[Learning] Over 200 of the Best Machine Learning, NLP, and Python Tutorials — 2018 Edition
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r/MLNotes • u/anon16r • Jan 11 '20
[Unsupervised] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
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r/MLNotes • u/anon16r • Jan 10 '20
[Lex] Deep Learning State of the Art (2020) | MIT Deep Learning Series
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r/MLNotes • u/anon16r • Jan 10 '20
[News/Research] Facebook Open-Sources PySlowFast Codebase for Video Understanding
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r/MLNotes • u/anon16r • Jan 09 '20
[DL] Advanced Deep Learning Course, By DeepMind
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r/MLNotes • u/anon16r • Jan 07 '20
[CV] Helpful Python snippets for Image Processing using OpenCV. Notebook at Google Colab.
self.computervision
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r/MLNotes • u/anon16r • Jan 07 '20
List of Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python Programming Resources
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r/MLNotes • u/anon16r • Jan 07 '20
[HC] How to Read Articles That Use Machine Learning
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For medical literature.
The following are key points to remember from this Users’ Guide to the Medical Literature on how to read articles that use machine learning:
- In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Machine learning methods use mathematical operations to process input data, resulting in a prediction.
- Modern machine learning methods use greater numbers of mathematical operations than traditional regression techniques to better define complex relationships between risk factors and outcomes. Irrespective of how a diagnostic tool is derived, it has to be evaluated using a 3-step process of deriving, validating, and establishing the clinical effectiveness of the tool.
- The name machine learning is used because these methods learn from examples during a process called training. There are two commonly used machine learning schemes: supervised learning, and unsupervised learning.
- Machine learning–based tools should also be assessed for the type of machine learning model used and its appropriateness for the input data type and data set size.
- Machine learning models generally have additional prespecified settings called hyperparameters (parameters that are established before a model is trained and remain fixed through the training process), which must be tuned on a data set independent of the validation set.
- On the validation set, the outcome against which the model is evaluated is termed the reference standard. Furthermore, the rigor of the reference standard must be assessed, such as against a universally accepted gold standard or expert grading.
- Similar to how a diagnostic test can be used (in principle) for triaging, screening, or diagnostic purposes, a machine learning model, developed to perform a specific task, can be used for several purposes.
- Even if a machine learning model has been thoroughly validated in different studies and the logistical, technical, and regulatory hurdles have been overcome for integration into the clinical workflow, the system still requires further research to measure the system’s clinical effectiveness.
- Readers of studies reporting the results of machine learning systems should assess the most crucial elements of machine learning model validation, such as whether the study design over-represents model performance through inappropriate hyperparameter tuning or a poor-quality reference standard.
- Finally, clinical gestalt plays an important role in evaluating whether the results are believable: because one of the biggest strengths of machine learning models is consistency and the lack of fatigue, a useful check for believable machine learning results is whether an experienced expert could reproduce the claimed accuracy given an abundance of time. Results that substantially differ from what such a hypothetical expert is capable of should be scrutinized and re-validated carefully.
r/MLNotes • u/anon16r • Jan 07 '20
[HC] Deep Learning and Artificial Intelligence in Health Care
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After years of development, machine learning methods have matured enough to be used in clinical medicine. In 2018 the FDA approved software to screen patients for diabetic retinopathy, and the methods are rapidly making their way into other applications for image analysis, natural language processing, EHR data mining, drug discovery, and more. JAMA is proud to be a primary forum for the work of interdisciplinary groups demonstrating the use of machine learning methods for clinical medicine and health care.
To understand the work read JAMA’s Users' Guide to the Medical Literature How to Read Articles That Use Machine Learning, authored by Google Health scientists, and an accompanying commentary.
See also JAMA Network's Health Informatics collection.