my_library
Python For Finance: Algorithmic Trading
Originally published at https://www.datacamp.com/community/tutorials/finance-python-trading
Frequency Distribution Analysis using Python Data Stack – Part 1
During my years as a Consultant Data Scientist I have received many requests from my clients to provide frequency distribution
Elliptic Curves as Python Objects | Math ∩ Programming
Last time we saw a geometric version of the algorithm to add points on elliptic curves. We went quite deep into the formal setting for it (projective space $ \mathbb{P}^2$), and we spent a lot of time talking about the right way to define the “zero” object in our elliptic curve…
Modern Machine Learning Algorithms: Strengths and Weaknesses
Get to know the ML landscape through this practical, concise overview of modern machine learning algorithms. Plus, we'll discuss the tradeoffs of each.
Berkeley CS189 Machine Learning: Complete Lecture Notes [pdf]
An Intro to Integer Programming for Engineers: Simplified Bus Scheduling
Explore what’s new, what’s next, and what should be on your radar in the world of transportation. Our insights are supported by real-world data from our partners across the globe.
timekit: Time Series Forecast Applications Using Data Mining
The timekit package contains a collection of tools for working with time series in R. There’s a number of benefits. One of the biggest is the ability to use a time series signature to predict future values (forecast) through data mining techniques. W...
Data Science for Operational Excellence (Part-5)
Operations need to have demand forecasts in order to establish optimal resource allocation policies. But, when we make predictions the only thing that we assure is the occurrence of prediction errors. Fortunately, there is no need to be 100% accurate to succeed, we just need to…
Linux Load Averages: Solving the Mystery
Linux load averages explained, including why they include the uninterruptible I/O sleep state.
Enabling signal processing over data streams
40 Techniques Used by Data Scientists - Data Science Central
These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of…
Basic Signal Operations in DSP: Constant-Valued and Alternating Signals
In this article, we’ll discuss a few elementary signal operations by considering one of the signals to be constant-valued.
https://blog.statsbot.co/introduction-to-imitation-learning-32334c3b1e7a
Python decorators, the right way: the 4 audiences of programming languages
Python decorators are a useful but flawed language feature. Intended to make source code easier to write, and a little more readable, they neglect to address another use case: that of the programmer who will be calling the decorated code. If you’re a Python programmer, the…
Creating a Blogging App Using React, Part 5: Profile Page
In the previous part of this tutorial series, you saw how to implement the update and delete post feature for our React blog application. In this tutorial, you'll implement the profile page for...
Builder design pattern in Ruby – kkempin’s dev blog – Medium
Builder pattern in Ruby is useful when the algorithm how to build an object is something independent of the parts that makes the object.
scikit-surprise 1.0.5 : Python Package Index
An easy-to-use library for recommender systems.
(1) Cohort Analysis with Python | LinkedIn
Discover 100 collaborative articles on domains such as Marketing, Public Administration, and Healthcare. Our expertly curated collection combines AI-generated content with insights and advice from industry experts, providing you with unique perspectives and up-to-date…
Beej's Guide to C Programming (2007)
Learning to Optimize with Reinforcement Learning
The BAIR Blog
10 Common Software Architectural Patterns in a nutshell
Ever wondered how large enterprise scale systems are designed? Before major software development starts, we have to choose a suitable…
Understanding the secret_key_base in Ruby on Rails
Have you ever wondered what the secret_key_base value is and how it’s used in a Rails application? This configuration value was introduced…
React Crash Course for Beginners, Part 3
So far in this React series, we've created a working sample app as a starting point for our 'Movie Mojo' gallery app, and we've seen how using props allows us to customize the appearance of...
A Gentle Introduction to RNN Unrolling - MachineLearningMastery.com
Recurrent neural networks are a type of neural network where the outputs from previous time steps are fed as input to the current time step. This creates a network graph or circuit diagram with cycles, which can make it difficult to understand how information moves through the…
Deep Meaning Beyond Thought Vectors
I ended my last post by saying that I might write a follow-up post on current work that seems to exhibit progress toward natural language understanding. I am going to discuss a couple sampled pap…
Sorting Visualizations - Album on Imgur
Discover topics like sorting, visualization, rainbow, algorithms, satisfying, and the magic of the internet at Imgur, a community powered entertainment destination. Lift your spirits with funny jokes, trending memes, entertaining gifs, inspiring stories, viral videos, and so…
How to set up world-class continuous deployment using free hosted tools
I’m going to describe a way to put together a world-class continuous deployment infrastructure for your side-project without spending any money. With continuous deployment every code commit is tested against …
Interactive Workflows for C with Jupyter – Jupyter Blog
Scientists, educators and engineers not only use programming languages to build software systems, but also in interactive workflows, using…
How to solve 90% of NLP problems: a step-by-step guide
Using Machine Learning to understand and leverage text.
The Matrix Calculus You Need For Deep Learning
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the…
Deep Learning Research Review Week 2: Reinforcement Learning – Adit Deshpan
Engineering at Forward | UCLA CS '19
Deep Learning Research Review Week 3: Natural Language Processing – Adit De
Engineering at Forward | UCLA CS '19
Firebase console
NLP Concepts with spaCy. Code examples released under CC0 https://creativec
NLP Concepts with spaCy. Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ · GitHub
agnusmaximus/Word2Bits: Quantized word vectors that take 8x-16x less space than regular word vectors
Quantized word vectors that take 8x-16x less space than regular word vectors - agnusmaximus/Word2Bits
LouieYang/deep-photo-styletransfer-tf: Tensorflow (Python API) implementati
Tensorflow (Python API) implementation of Deep Photo Style Transfer - LouieYang/deep-photo-styletransfer-tf
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while...
5 Fantastic Practical Natural Language Processing Resources
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
How to build a deep learning model in 15 minutes – tech-at-instacart
An open source framework for configuring, building, deploying and maintaining deep learning models in Python.
From neural nets to autonomous machine learning algorithms: The many differ
We ask marketers to define AI, one of the industry's most prevalent buzzwords.
Generative Adversarial Networks (GANs): Engine and Applications - Data Scie
Generative adversarial networks (GANs) are a class of neural networks that are used in unsupervised machine learning. They help to solve such tasks as image generation from descriptions, getting high resolution images from low resolution ones, predicting which drug could treat a…
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists: 9781491953242: Computer Science Books @ Amazon.com
Building an image search with Algolia & Google’s Vision API
Build an 'auto-tagging' image search service using Algolia and Google Cloud's Vision API
Learning to write programs that generate images
Through a human’s eyes, the world is much more than just the images reflected in our corneas. For example, when we look at a building and admire the intricacies of its design, we can appreciate...
Using React, Firebase, and Ant Design to Quickly Prototype Web Applications
In this guide I will show you how to use Firebase, React, and Ant Design as building blocks to build functional, high-fidelity web applications. To illustrate this, we’ll go through an example of building a todo list app. These days, there are so many tools available for web…
Expero | Graph Data & Visualizations
Expero's cross-functional teams offer deep expertise in custom software development, product design and technology to solve complex issues and drive innovation.
Eecs227at
Real-time object detection with deep learning and OpenCV - PyImageSearch
In this tutorial I demonstrate how to apply object detection with deep learning and OpenCV + Python to real-time video streams and video files.
Prettier · Opinionated Code Formatter
Opinionated Code Formatter