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Machine Learning

A Low-Cost Introduction to Machine Learning Training on Microsoft Azure nag.com

Published October 1, 2020 under Machine Learning

This tutorial demonstrates porting an existing machine learning model to a virtual machine on the Microsoft Azure cloud platform. We will train a small movie recommendation model using a single GPU to give personalised recommendations. The total cost of performing this training should be no more than $5 using any of the single GPU instances currently available on Azure.

Azure, NAG

Algorithmic Trading Using Logistic Regression handsoffinvesting.com

Published September 20, 2020 under Machine Learning

Algorithmic Trading Using Logistic Regression

In order to implement an algorithmic trading strategy though, you have to first narrow down a list of stocks that you want to analyze. This walk-through provides an automated process (using python and logistic regression) for determining the best stocks to algo-trade.

I will dive deeper into the logic and code below, but here is a high-level overview of the process:

  1. Import the historical data of every stock using yahoo finance.
  2. Pull in over 32 technical indicators for each stock using the technical analysis library.
  3. Perform a logistic regression on each stock using 5, 30, and 60 day observation time periods.
  4. Interpret the results.

Algorithmic Trading, Quant Trading

Time-Series Forecasting with TensorFlow 2.0 theclickreader.com

Published September 7, 2020 under Machine Learning

In this tutorial, you will be learning how to build powerful time-series forecasting model of your own using various kinds of deep learning algorithms such as Dense Neural Networks (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN). Also, this course is an elaboration of the time-series forecasting tutorial by TensorFlow.

Python, Time Series Analysis

Building AI Trading Systems dennybritz.com

Published July 13, 2020 under Machine Learning

Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.

Algorithmic Trading, Cryptocurrency, Reinforcement Learning

Machine Learning Financial Laboratory (mlfinlab) readthedocs.io

Published May 12, 2020 under Machine Learning

Machine Learning Financial Laboratory (mlfinlab)

MlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. Adding MlFinLab to your companies pipeline is like adding a department of PhD researchers to your team.

Algorithmic Trading, Python

Machine Learning & Deep Learning Fundamentals https://deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU

Published May 12, 2020 under Machine Learning

This series explains concepts that are fundamental to deep learning and artificial neural networks for beginners. In addition to covering these concepts, we also show how to implement some of the concepts in code using Keras, a neural network API written in Python. We will learn about layers in an artificial neural network, activation functions, backpropagation, convolutional neural networks (CNNs), data augmentation, transfer learning and much more!

Deep Learning

PyKrylov: Accelerating Machine Learning Research at eBay ebayinc.com

Published February 11, 2020 under Machine Learning

PyKrylov: Accelerating Machine Learning Research at eBay

The experience while accessing the AI platform and running machine learning (ML) training code on the platform must be smooth and easy for the researchers. Migrating any ML code from a local environment to the platform should not require any refactoring of the code at all. Infrastructure configuration overhead should be minimal. Our mission while developing PyKrylov was to abstract the ML logic from the infrastructure and Krylov core components as much as possible in order to achieve the best experience for the platform users.

Open Source, Python

Open Registration for Loominus Data Science Platform – Use it Free loominus.ai

Published December 15, 2019 under Machine Learning

Loominus has opened up registration and is offering free accounts for a limited time. There’s a whole slew of new features including private data repos, data pipeline cloning, automated data pipelines, enhanced column type detection, UI/UX improvements, detailed information for active tasks, model stream updates and updated API documentation.

Loominus is an end-to-end platform that helps teams ingest and stage data, build advanced machine learning models with no code and deploy them into production. Loominus makes it easy for individuals and teams without experience building machine learning pipelines to take advantage of machine learning faster. Loominus is equally great for experienced data scientists that need to focus on model selection and tuning.

Data Science, Loominus

Using Convolutional Neural Networks to Classify Street Signs medium.com

Published December 8, 2019 under Machine Learning

Since the invention of the automobile, manufacturers have steadily added more safety features and improved car design over time with the goal of keeping drivers safer on the road. Automotive manufacturers have spent millions of dollars researching safety improvements for seatbelts, tires, and pretty much every car piece or part imaginable. Despite all of this investment, driving remains substantially more fatal than alternatives such as air travel in 2019. According to the National Safety Council, approximately 40,000 people died in automotive accidents in the United States alone in 2018. In fact, there were a total of ~500 deaths resulting from plane crashes recorded globally in 2018 — that’s 80 times fewer deaths when compared to car crash fatalities in the US only.

Computer Vision, Convolutional NN, Neural Network

End to End Machine Learning: From Data Collection to Deployment ahmedbesbes.com

Published December 8, 2019 under Machine Learning

Python

Fire and smoke detection with Keras and Deep Learning pyimagesearch.com

Published November 28, 2019 under Machine Learning

n this tutorial, you will learn how to detect fire and smoke using Computer Vision, OpenCV, and the Keras Deep Learning library.

Computer Vision, Keras

Face Detection and Recognition with Keras sitepoint.com

Published November 28, 2019 under Machine Learning

Computer Vision, Keras

Dijkstra’s Shortest Path Algorithm in Python medium.com

Published November 28, 2019 under Machine Learning

From GPS navigation to network-layer link-state routing, Dijkstra’s Algorithm powers some of the most taken-for-granted modern services. Utilizing some basic data structures, let’s get an understanding of what it does, how it accomplishes its goal, and how to implement it in Python (first naively, and then with good asymptotic runtime!)

Algorithms, Graph

How To Run TensorFlow Lite on Raspberry Pi for Object Detection youtube.com

Published November 28, 2019 under Machine Learning

TensorFlow Lite is a framework for running lightweight machine learning models, and it’s perfect for low-power devices like the Raspberry Pi! This video shows how to set up TensorFlow Lite on the Raspberry Pi for running object detection models to locate and identify objects in real-time webcam feeds, videos, or images.

Computer Vision, TensorFlow

Healthcare Fraud Detection With Python theseattledataguy.com

Published November 25, 2019 under Machine Learning

This April a 1.5 billion dollar medicare scheme took advantage of hundreds of thousands of seniors in the US. In reality, this is just a small sliver of the billions of dollars healthcare fraud costs both consumers and insurance providers annually.

Healthcare fraud can come from many different directions. Some people might think of the patient who pretends to be injured, but actually, much of fraud is caused by providers(as in the NYT article).

Providers often have financial incentives for increasing performing unnecessary surgeries or claiming work they never even did. This leads to many different flavors of fraud that can all be difficult to detect on a claim by claim basis.

Pandas

Traffic Sign Classification with Keras and Deep Learning pyimagesearch.com

Published November 25, 2019 under Machine Learning

In this tutorial, you will learn how to train your own traffic sign classifier/recognizer capable of obtaining over 95% accuracy using Keras and Deep Learning.

Computer Vision, Keras

How to Implement Bayesian Optimization from Scratch in Python machinelearningmastery.com

Published October 14, 2019 under Machine Learning

Bayesian Optimization provides a principled technique based on Bayes Theorem to direct a search of a global optimization problem that is efficient and effective. It works by building a probabilistic model of the objective function, called the surrogate function, that is then searched efficiently with an acquisition function before candidate samples are chosen for evaluation on the real objective function.

Bayesian Analysis, Python

TensorFlow 2.0 + Keras Overview for Deep Learning Researchers google.com

Published October 14, 2019 under Machine Learning

This document serves as an introduction, crash course, and quick API reference for TensorFlow 2.0.

Keras, TensorFlow

How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit (NLTK) digitalocean.com

Published October 5, 2019 under Machine Learning

A large amount of data that is generated today is unstructured, which requires processing to generate insights. Some examples of unstructured data are news articles, posts on social media, and search history. The process of analyzing natural language and making sense out of it falls under the field of Natural Language Processing (NLP). Sentiment analysis is a common NLP task, which involves classifying texts or parts of texts into a pre-defined sentiment. You will use the Natural Language Toolkit (NLTK), a commonly used NLP library in Python, to analyze textual data.

NLTK, Text Classification

Logistic Regression Models in Scikit-Learn https://towardsdatascience.com/dont-sweat-the-solver-stuff-aea7cddc3451

Published October 5, 2019 under Machine Learning

Logistic regression is the bread-and-butter algorithm for machine learning classification. If you’re a practicing or aspiring data scientist, you’ll want to know the ins and outs of how to use it. Also, Scikit-learn’s LogisticRegression is spitting out warnings about changing the default solver, so this is a great time to learn when to use which solver. 😀

Logistic Regression, scikit-learn

TensorFlow 2.0 Tutorial 01: Basic Image Classification lambdalabs.com

Published October 5, 2019 under Machine Learning

TensorFlow 2 is now live! This tutorial walks you through the process of building a simple CIFAR-10 image classifier using deep learning. In this tutorial, we will:

  • Define a model
  • Set up a data pipeline
  • Train the model
  • Accelerate training speed with multiple GPUs
  • Add callbacks for monitoring progress/updating learning schedules

The code in this tutorial is available here.

Python, TensorFlow

Logo Recognition Using Machine Learning and Flask API https://heartbeat.fritz.ai/logo-recognition-ios-application-using-machine-learning-and-flask-api-aec4eff3be11

Published October 5, 2019 under Machine Learning

Comparing 5 popular neural net architectures on iOS: VGG16, ResNet50, InceptionV3, GoogleNet, and SqueezeNet using PyTorch.

Deep Learning, Neural Network, Transfer Learning

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch berkeley.edu

Published September 27, 2019 under Machine Learning

Since the advent of deep reinforcement learning for game play in 2013, and simulated robotic control shortly after, a multitude of new algorithms have flourished. Most of these are model-free algorithms which can be categorized into three families: deep Q-learning, policy gradients, and Q-value policy gradients.

Reinforcement Learning

How to train a neural net to play cards apptic.me

Published September 7, 2019 under Machine Learning

In this article, we’ll use some basic machine learning methods to train a bot to play cards against me. The card game that I’m interested in is called Literature, a game similar to Go Fish.

The version of Literature that we implemented is roughly similar to the rules I linked above. Literature is played in two teams, and the teams compete to collect “sets.” A set is a collection of either A – 6 of a suit or 8 – K of a suit (7’s are not included in the game).

Deep Learning, Neural Network

Reverse Image Search with Machine Learning commercetools.com

Published August 30, 2019 under Machine Learning

The Machine Learning team at commercetools is excited to release the beta version of our new Image Search API.

Image search (sometimes called reverse image search) is a tool, where given an image as a query, a duplicate or similar image is returned as a response. The technology driving this search engine is called computer vision, and advancements in this field are giving way to some compelling product features.

Convolutional NN, Neural Network

Web Scraping 101 with Python daolf.com

Published August 30, 2019 under Machine Learning

In this post, which can be read as a follow up to our ultimate web scraping guide, we will cover almost all the tools Python offers you to web scrape. We will go from the more basic to the most advanced one and will cover the pros and cons of each. Of course, we won’t be able to cover all aspect of every tool we discuss, but this post should be enough to have a good idea of which tools does what, and when to use which.

Data

A Simple Explanation of the Softmax Function victorzhou.com

Published August 10, 2019 under Machine Learning

What Softmax is, how it’s used, and how to implement it in Python.

Neural Network

Cyclical Learning Rates with Keras and Deep Learning pyimagesearch.com

Published August 10, 2019 under Machine Learning

In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.

Computer Vision, Keras

The fastest way to be productive with machine learning loominus.ai

Published July 24, 2019 under Machine Learning

Advanced machine learning everyone can use. Stage data. Build models with no code. Manage models in production.

LightGBM, scikit-learn, TensorFlow, XGBoost

Keras Mask R-CNN pyimagesearch.com

Published June 18, 2019 under Machine Learning

In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).

Using Mask R-CNN we can perform both: Object detection, giving us the (x, y)-bounding box coordinates of for each object in an image; Instance segmentation, enabling us to obtain a pixel-wise mask for each individual object in an image.

Computer Vision, Convolutional NN, Keras

Artificial Intelligence Made Easy with H2O.ai towardsdatascience.com

Published June 18, 2019 under Machine Learning

A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python

AutoML, H2O.ai, Python

Fine-tuning with Keras and Deep Learning pyimagesearch.com

Published June 10, 2019 under Machine Learning

In this tutorial, you will learn how to perform fine-tuning with Keras and Deep Learning.

We will take a CNN pre-trained on the ImageNet dataset and fine-tune it to perform image classification and recognize classes it was never trained on.

Today is the final post in our three-part series on fine-tuning:

  1. Part #1: Transfer learning with Keras and Deep Learning
  2. Part #2: Feature extraction with on large datasets with Keras and Deep Learning
  3. Part #3: Fine-tuning with Keras and Deep Learning (today’s post)

Computer Vision, Keras

CNNs, Part 2: Training a Convolutional Neural Network victorzhou.com

Published June 10, 2019 under Machine Learning

In this post, we’re going to do a deep-dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN, including deriving gradients, implementing backprop from scratch (using only numpy), and ultimately building a full training pipeline!

Convolutional NN, Deep Learning

Image Recognition in Python with TensorFlow and Keras

Published June 3, 2019 under Machine Learning

One of the most common utilizations of TensorFlow and Keras is the recognition/classification of images. If you want to learn how to use Keras to classify or recognize images, this article will teach you how.

Keras, TensorFlow

Text Analysis in Python to Test a Hypothesis dataquest.io

Published June 3, 2019 under Machine Learning

People often complain about important subjects being covered too little in the news. One such subject is climate change. The scientific consensus is that this is an important problem, and it stands to reason that the more people are aware of it, the better our chances may be of solving it. But how can we assess how widely covered climate change is by various media outlets? We can use Python to do some text analysis!

NLP, Python

Understanding PyTorch with an example: a step-by-step tutorial towardsdatascience.com

Published May 15, 2019 under Machine Learning

PyTorch is the fastest growing Deep Learning framework and it is also used by Fast.ai in its MOOC, Deep Learning for Coders and its library. PyTorch is also very pythonic, meaning, it feels more natural to use it if you already are a Python developer.

Python, PyTorch

OpenCV-Python Cheat Sheet: From Importing Images to Face Detection fritz.ai

Published April 21, 2019 under Machine Learning

Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.

Computer Vision, OpenCV, Python

How to Automate Tasks on GitHub With Machine Learning for Fun and Profit towardsdatascience.com

Published April 19, 2019 under Machine Learning

A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

Python, TensorFlow

PyTorch Sentiment Analysis github.com

Published April 19, 2019 under Machine Learning

Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.

The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.

Data Science, Python, PyTorch

Text Classification in Python Using spaCy dataquest.io

Published April 19, 2019 under Machine Learning

NLP, spaCy, Text Classification

A Complete Machine Learning Project Walk-Through in Python codequs.com

Published March 3, 2019 under Machine Learning

Tutorial

Putting a machine learning model in production using Flask, Docker, and Kubernetes mikulskibartosz.name

Published February 2, 2019 under Machine Learning

Flask, Kubernetes

Using the latest advancements in deep learning to predict stock price movements towardsdatascience.com

Published January 22, 2019 under Machine Learning

GAN, LTSM, Python, Time Series Analysis

A very simple framework for state-of-the-art NLP github.com

Published January 6, 2019 under Machine Learning

NLP, Python, PyTorch

Homemade Machine Learning github.com

Published December 31, 2018 under Machine Learning

Python

How to use Keras fit and fit_generator (a hands-on tutorial) pyimagesearch.com

Published December 31, 2018 under Machine Learning

Keras, Python, Tutorial

Building and Testing Recommender Systems With Surprise, Step-By-Step towardsdatascience.com

Published December 31, 2018 under Machine Learning

Python, Recommenders, Surprise

Develop a NLP Model in Python & Deploy It with Flask, Step by Step towardsdatascience.com

Published December 20, 2018 under Machine Learning

API, Data Science, Flask, NLP

Faster Neural Networks Straight from JPEG uber.com

Published December 20, 2018 under Machine Learning

Deep Learning, Neural Network, Uber

Image Stitching with OpenCV and Python pyimagesearch.com

Published December 20, 2018 under Machine Learning

Computer Vision, OpenCV, Python

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