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Machine Learning

Clustering with Scikit-Learn in Python programminghistorian.org

under Machine Learning

This tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis.

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.

under Machine Learning

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.

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.

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

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!

under Machine Learning

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.

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.

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.

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!)

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.

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.

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.

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

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. 😀

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.

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).

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.

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.

Keras Mask R-CNN pyimagesearch.com

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.

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)

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!