Resources for developers using Python for scientific computing and quantitative analysis
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.
This document serves as an introduction, crash course, and quick API reference for TensorFlow 2.0.
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 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. 😀
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:
The code in this tutorial is available here.
Comparing 5 popular neural net architectures on iOS: VGG16, ResNet50, InceptionV3, GoogleNet, and SqueezeNet using PyTorch.
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.
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).
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.
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.
What Softmax is, how it’s used, and how to implement it in Python.
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.
Advanced machine learning everyone can use. Stage data. Build models with no code. Manage models in production.
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.
A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
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:
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!
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.
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!
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.
Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.
A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
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.
