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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 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.

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.

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, retry modules and the browser developer tools. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

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 Python 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.

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 Python 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.

Classification, Regression and Model Selection using Python’s Scikit-learn

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This workshop will introduce participants to machine learning in Python. We’ll start with a brief explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). After an introduction to classification, regression and model selection, we’ll use a couple of example datasets to demonstrate how to create, apply and evaluate models in Scikit-learn. Although not required, we recommend all participants to have a basic knowledge of Python.

Data Processing in Python using Pandas

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This workshop will introduce participants to Python’s Pandas. We’ll start with a brief explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). After a brief introduction to main Python’s standard data types as well as Pandas data structures, we’ll demonstrate how to retrieve information from Pandas Series and DataFrames. We’ll also demonstrate basic input/output, selection, dropping, sorting, ranking, grouping and apply operations.  Although not required, we recommend all participants to have a basic knowledge of Python.

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.