Yes. I make mistakes ... rarely. http://www.flippingphysics.com
From playlist Miscellaneous
An general explanation of the underactive thyroid.
From playlist For Patients
Which Is Worse: Underpopulation Or Overpopulation?
This video was made in partnership with Gates Ventures. The human population of the world will soon peak – and then decrease – thanks to a combination of two quickly changing economic and educational trends. LEARN MORE ************** To learn more about this topic, start your googling wi
From playlist Policy
IDEspinner Buffer Overflows pt1
This movie tries to show how you can create a bufferoverflow Credits go out to IDEspinner
From playlist Buffer overflow
In this video, I define what it means to rearrange (or reshuffle) a series and show that if a series converges absolutely, then any rearrangement of the series converges to the same limit. Interesting Consequence: https://youtu.be/Mw7ocynGVmw Series Playlist: https://www.youtube.com/play
From playlist Series
Machine Learning with Imbalanced Data - Part 3 (Over-sampling, SMOTE, and Imbalanced-learn)
In this video, we discuss the class imbalance problem and how to use over-sampling methods to address this problem. We use the thyroid data set and the logistic regression classifier to train binary classifiers on the original data set and the preprocessed data. We discuss uniform sampling
From playlist Machine Learning with Imbalanced Data - Dr. Data Science Series
This is why you should care about unbalanced data .. as a data scientist
What do you do when your data has lots more negative examples than positive ones? Link to Code : https://github.com/ritvikmath/YouTubeVideoCode/blob/main/Unbalanced%20Data.ipynb My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Concepts
Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes
How can data leakage happen during data preparation? Find out here, where Ms. Coffee Bean is visualizing the answer to this question -- as it was answered by the paper “Using AntiPatterns to avoid MLOps Mistakes”. Paper explained 📄: Muralidhar, Nikhil, Sathappah Muthiah, Patrick Butler, M
From playlist Explained AI/ML in your Coffee Break
Randomized SVD: Power Iterations and Oversampling
This video discusses the randomized SVD and how to make it more accurate with power iterations (multiple passes through the data matrix) and oversampling. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 1 from: "Data-Drive
From playlist Data-Driven Science and Engineering
John Miao - Coherent Diffractive Imaging: A Unification of Microscopy, Diffraction and Computation
Recorded 13 September 2022. Jianwei Miao of the University of California, Los Angeles, presents "Coherent Diffractive Imaging: A Unification of Microscopy, Diffraction and Computation" at IPAM's Computational Microscopy Tutorials. Learn more online at: http://www.ipam.ucla.edu/programs/wor
From playlist Tutorials: Computational Microscopy 2022
What is an enlargement dilation
👉 Learn about dilations. Dilation is the transformation of a shape by a scale factor to produce an image that is similar to the original shape but is different in size from the original shape. A dilation that creates a larger image is called an enlargement or a stretch while a dilation tha
From playlist Transformations
How to deal with Imbalanced Datasets in PyTorch - Weighted Random Sampler Tutorial
In this video we take a look at how to solve the super common problem of having an imbalanced or skewed dataset, specifically we look at two methods namely oversampling and class weighting and how to do them both in PyTorch. Toy dataset used in video: https://www.kaggle.com/dataset/77934f
From playlist PyTorch Tutorials
Is the Curse of Dimensionality the same as overfitting?
#machinelearning #shorts #datascience
From playlist Quick Machine Learning Concepts
👉 Learn about dilations. Dilation is the transformation of a shape by a scale factor to produce an image that is similar to the original shape but is different in size from the original shape. A dilation that creates a larger image is called an enlargement or a stretch while a dilation tha
From playlist Transformations
Machine Learning with Imbalanced Data - Part 5 (Ensemble learning, Bagging classifier)
In this video, we discuss the use of ensemble learning strategies to address the class imbalance problem. Therefore, one can use a combination of data-level preprocessing methods and cost-sensitive learning to improve the performance of classifiers on class-imbalanced data sets. #Imbalan
From playlist Machine Learning with Imbalanced Data - Dr. Data Science Series
Reasonable samples | Statistical studies | Probability and Statistics | Khan Academy
To make a valid conclusion, you'll need a representative, not skewed, sample. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/statistical-studies/statistical-questions/e/valid-claims?utm_source=YT&utm_medium=Desc&utm_campaign=Proba
From playlist High school statistics | High School Math | Khan Academy
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Illustration of the benefits of oversampling signals when using practical analog anti-aliasing and anti-imaging filters.
From playlist Sampling and Reconstruction of Signals
Machine Learning for Everybody – Full Course
Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts. ✏️ Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed ⭐️ Code and
From playlist Data Science
Overfitting 1: over-fitting and under-fitting
[http://bit.ly/overfit] When building a learning algorithm, we want it to work well on the future data, not on the training data. Many algorithms will make perfect predictions on the training data, but perform poorly on the future data. This is known as overfitting. In this video we provid
From playlist Overfitting