Introduction to Deep Neural Networks with Keras/TensorFlow

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Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including the popular library TensorFlow. In this workshop, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. Computers will be available to complete exercises. We will run the models using Google Colab, which requires a Google account.

Introduction to Python’s NumPy library

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This workshop will introduce you to the NumPy library in Python, which is useful in scientific computing. We will cover NumPy’s n-dimensional array object and associated functions in depth, along with related linear algebra and random number capabilities. Some familiarity with Python is expected. Computers will be available to complete exercises.

Introduction to Deep Neural Networks with Keras/TensorFlow

By |

Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including the popular library TensorFlow. In this workshop, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. Computers will be available to complete exercises. We will run the models using Google Colab, which requires a Google account.

Introduction to Deep Neural Networks with Keras/TensorFlow

By |

Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including the popular library TensorFlow. In this workshop, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. Computers will be available to complete exercises. We will run the models using Google Colab, which requires a Google account.

Introduction to NumPy (Python)

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This workshop will introduce you to the NumPy library in Python, which is useful in scientific computing. We will cover NumPy’s n-dimensional array object and associated functions in depth, along with related linear algebra and random number capabilities. Some familiarity with Python is expected. Computers will be available to complete exercises.

Web Scraping with Python

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This workshop will provide an overview of how to scrape data from html pages and website APIs using Python. This will mostly be accomplished using the requests, beautifulsoup, and retry modules with the browser developer tools. The workshop is intended for users with basic Python knowledge. Anaconda Python 3 will be used.

Introduction to Deep Neural Networks with Keras/TensorFlow

By |

Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level, Python interface running on top of multiple neural network libraries, including the popular library TensorFlow. In this workshop, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. Computers will be available to complete exercises. We will run the models using Google Colab, which requires a Google account.

Web Scraping with Python

By |

This workshop will provide an overview of how to scrape data from html pages and website APIs using Python. This will mostly be accomplished using the requests, beautifulsoup, retry modules and the browser developer tools. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

Intro to Natural Language Processing with Python

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This workshop will provide a quick overview of natural language processing using Python. We’ll cover the basics. Segmenting text into tokens, assigning part-of-speech, assigning dependency labels, detecting and labeling named-entities. We’ll also cover sentiment analysis, topic modelling and maybe some visualizations. The workshop will be conducted in Python and is intended for users with basic Python programming knowledge. Anaconda Python 3.5 and a Jupyter Notebook will be used.

Parallel Processing with Python

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Modern computers have a CPU with multiple cores (usually between 4-8). Come learn how to take advantage of them to parallelize and speed up your code. We’ll show you how to structure your code so you can parallelize it in 5 lines or less. We will also cover some theory, a few practical considerations along with some basic exercises. We’ll be using the multiprocessing module in Python. The workshop is intended for users with basic Python knowledge. The workshop assumes you know how to do the following in Python: i) write a for loop, ii) write a function that has inputs and outputs.  Anaconda Python 3.5 will be used.