Collection of Jupyter notebooks for quantitative finance
Collection of notebooks about quantitative finance, with interactive python code. - cantaro86/Financial-Models-Numerical-Methods
my_library
Python Tuples and Tuple Methods
Brush up on your Python basics with this post on creating, using, and manipulating tuples.
Neural Networks 201: All About Autoencoders
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.
Research Guide: Model Distillation Techniques for Deep Learning
Knowledge distillation is a model compression technique whereby a small network (student) is taught by a larger trained neural network (teacher). The smaller network is trained to behave like the large neural network. This enables the deployment of such models… Continue reading…
Markov Chain Analysis and Simulation using Python - Towards Data Science
Solving real-world problems with probabilities
A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores
How to Label 1M Data Points per Week
Practical Data Science with R, 2nd Edition, IS OUT!!!!!!!
Practical Data Science with R, 2nd Edition author Dr. Nina Zumel, with a fresh author’s copy of her book!
Workflow Tools for Model Pipelines
Chapter 5 excerpt of “Data Science in Production”
How to Speed up Pandas by 4x with one line of code
While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.
Correlation Coefficients in One Picture - DataScienceCentral.com
Correlation coefficients enable to you find relationships between a wide variety of data. However, the sheer number of options can be overwhelming. This picture sums up the differences between five of the most popular correlation coefficients. Part two covers several less…
When your data doesn’t fit in memory: the basic techniques
You can process data that doesn’t fit in memory by using four basic techniques: spending money, compression, chunking, and indexing.
P-value Explained Simply for Data Scientists
This post is about various evaluation metrics and how and when to use them.
Recommender Engine - Under The Hood - DataScienceCentral.com
Many of us are bombarded with various recommendations in our day to day life, be it on e-commerce sites or social media sites. Some of the recommendations look relevant but some create range of emotions in people, varying from confusion to anger. There are basically two types of…
Time Series Prediction - A short introduction for pragmatists · Blog · Liip
Optimizers Explained - Adam, Momentum and Stochastic Gradient Descent
The 5 Classification Evaluation metrics every Data Scientist must know
This post is about various evaluation metrics and how and when to use them.
What does the Laplace Transform tell us? A visual explanation [video]
Sign up with brilliant and get 20% off your annual subscription: https://brilliant.org/MajorPrep/ STEMerch Store: https://stemerch.com/ Support the Channel: https://www.patreon.com/zachstar PayPal(one time donation): https://www.paypal.me/ZachStarYT This video goes through a…
https://towardsdatascience.com/facebook-has-been-quietly-open-sourcing-some-amazing-deep-learning-capabilities-for-pytorch-a7ed5bc71f26
Computing Receptive Fields of Convolutional Neural Networks
Detailed derivations and open-source code to analyze the receptive fields of convnets.
[R] Announcing Confident Learning: Finding and Learning with Label Errors i
157 votes, 15 comments. Hi, Reddit. I'm excited to share confident learning for characterizing, finding, and learning with label errors in datasets…
Deep Learning in the Real World: Dealing with Non-Differentiable Loss Funct
Over the past few years, deep learning has been taking by storm many industries. From voice recognition to image analysis and synthesis, neural networks have turned out to be very efficient at solv…
Remember QR Codes? They’re More Powerful Than You Think | Andreessen Horowitz
China’s mobile payment ecosystem, the largest in the world, is built upon QR codes. But that technology extends far beyond shopping to ease friction throughout daily life. On a recent trip to China, I personally interacted with QR codes 42 times in a single day—to ride the…
How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - KDnuggets
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.
Open-sourcing Polynote: an IDE-inspired polyglot notebook
Jeremy Smith, Jonathan Indig, Faisal Siddiqi
5 Advanced Features of Pandas and How to Use Them
The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner.
[D] Tools/Techniques for Efficiently Sorting Image Data
I'm planning on sorting ~100,000 images to use as data for a computer vision application. With this much data, shaving a little time off of each…
How to Easily Deploy Machine Learning Models Using Flask
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
Welcome to PyTorch Tutorials — PyTorch Tutorials 1.3.0 documentation
How to Install PySpark and Integrate It In Jupyter Notebooks: A Tutorial
Here's how to install PySpark on your computer and get started working with large data sets using Python and PySpark in a Jupyter Notebook.
A Brief Introduction to PySpark - Towards Data Science
PySpark is a great language for performing exploratory data analysis at scale, building machine learning pipelines, and creating ETLs for…
Density Estimation: MLE, MAP, MOM, KDE, ECDF, Q-Q Plot, GAN
Density estimation is estimating the probability density function of the population from the sample
The 5 Algorithms for Efficient Deep Learning Inference on Small Devices - Fritz ai
With recent developments in deep learning, neural networks are getting larger and larger. For example, in the ImageNet recognition challenge, the winning model, from 2012 to 2015, increased in size by 16 times. And in just one year, for Baidu’s… Continue reading The 5 Algorithms…
[R] How UMAP works -- a detailed comparison with t-SNE
23 votes, 13 comments. A recent blog post How Exactly UMAP Works provides a different perspective on explaining the UMAP dimensionality reduction…
Building our Centralized Experimental Platform | Stitch Fix Technology – Multithreaded
Running an A/B test is easy. Screwing up an A/B test is even easier.
Introducing Gradient Community Notebooks: Easily Run ML Notebooks on Free G
Gradient Community Notebooks provide free GPUs with pre-configured environments for machine learning and deep learning projects.
Is Rectified Adam actually *better* than Adam? - PyImageSearch
Is the Rectified Adam (RAdam) optimizer actually better than the standard Adam optimizer? According to my 24 experiments, the answer is no, typically not (but there are cases where you do want to use it instead of Adam).
Basic Linear Algebra for Deep Learning | Built In
Basic linear algebra can have a surprising influence on deep learning and machine learning. Learn what linear algebra is and how to use it.
Bayesian Inference with Generative Adversarial Network Priors
A blog about Compressive Sensing, Computational Imaging, Machine Learning. Using priors to avoid the curse of dimensionality arising in Big Data.
Demystifying Object Detection and Instance Segmentation for Data Scientists
Easy Explanation!!! I tried
MrSyee/pg-is-all-you-need: Policy Gradient is all you need! A step-by-step
Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods. - MrSyee/pg-is-all-you-need
Hugging Face – The AI community building the future.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
(32) PyTorch Lightning - William Falcon - YouTube
In recent years, techniques such as 16-bit precision, accumulated gradients and distributed training have allowed models to train in record times. In this talk William Falcon goes through the implementation details of the 10 most useful of these techniques, including…
How to find Feature importances for BlackBox Models?
Permutation Importance as a feature selection method
Clustering Metrics Better Than the Elbow Method
We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.
101 Machine Learning Algorithms for Data Science | Data Science Blog
Data Science Dojo blog features the most recent, and relevant articles about data science, analytics, generative AI, large language models, machine learning, and data visualization.
Generative Deep Learning [Book]
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With … - Selection from Generative Deep Learning [Book]
On Sharding
Video streaming and processing in the Jupyter Notebook
1.3M subscribers in the Python community. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta…
Khruangbin, 'Maria También' (Live)
Watch the Texas-based Thai funk band perform the lead single from their latest release, Con Todo El Mundo.