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.
Archives for October 2019
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.
In this post, we are going to work with Pandas iloc, and loc. More specifically, we are going to learn slicing and indexing by iloc and loc examples.
Once we have a dataset loaded as a Pandas dataframe, we often want to start accessing specific parts of the data based on some criteria. For instance, if our dataset contains the result of an experiment comparing different experimental groups, we may want to calculate descriptive statistics for each experimental group separately.
In this tutorial, we’re going to dig into how to transform data using Python scripts and the command line.
But first, it’s worth asking the question you may be thinking: “How does Python fit into the command line and why would I ever want to interact with Python using the command line when I know I can do all my data science work using IPython notebooks or Jupyter lab?”
Notebooks are great for quick data visualization and exploration, but Python scripts are the way to put anything we learn into production. Let’s say you want to make a website to help people make Hacker News posts with ideal headlines and submission times. To do this, you’ll need scripts.
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:
- 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.
Comparing 5 popular neural net architectures on iOS: VGG16, ResNet50, InceptionV3, GoogleNet, and SqueezeNet using PyTorch.