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    <title>Teaching Python - Episodes Tagged with “Deep Learning”</title>
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    <pubDate>Tue, 02 Apr 2019 00:00:00 -0400</pubDate>
    <description>Teaching Python is a podcast about Python programming, computer science education, AI literacy, software development, cloud computing, cybersecurity, data, and how people learn technical skills. Hosted by Kelly Schuster-Paredes, Sean Tibor, and Julian Sequeira, the show is for educators, developers, technology leaders, and lifelong learners who want to better understand how Python connects to the wider world of computing. Episodes explore not only how people learn to code, but also how they build technical judgment, understand systems, evaluate AI-generated code, work with data, think about security, and move from beginner programming into real-world software development. About the Hosts Kelly Schuster-Paredes is a teacher who codes whose work has expanded from classroom computer science into AI strategy, curriculum design, professional learning, educational technology, and responsible technology adoption. Her background in Python and computer science education shapes her focus on learning, AI literacy, computational thinking, and what people need to understand in an AI-shaped world. Sean Tibor is Vice President of Infrastructure and Cloud at Pfizer and a former computer science teacher. He brings expertise in cloud computing, infrastructure, engineering operations, and technical leadership, connecting what people learn about computing with how large-scale systems are actually built, operated, secured, and maintained. Julian Sequiera is a technologist, Fractional CTO, and Senior Program Manager with more than 20 years of experience in infrastructure, cloud, engineering operations, and large-scale technology programs. He is also the co-founder of PyBites, a Python learning platform and community that has helped thousands of developers improve their Python and software development skills. What We Cover Python Programming and Computer Science Education: Learning Python, teaching programming, computational thinking, debugging, code literacy, and helping beginners build strong mental models. AI and AI Literacy: AI-assisted programming, evaluating AI-generated code, responsible AI use, human judgment, and what learners still need to understand when AI can produce code. Cloud, Infrastructure, and Cybersecurity: Systems, networks, deployment, security, reliability, architecture, and the operational side of software. Data and Software Engineering: APIs, databases, testing, maintainability, version control, software design, and moving from simple scripts to real-world applications. Learning and Technical Growth: How people learn difficult technical concepts, get unstuck, build confidence, and develop the judgment needed to use technology well. Expert Interviews: Conversations with educators, developers, engineers, researchers, technology leaders, and others shaping the future of computing and technical education. Teaching Python remains grounded in Python, but the conversation extends beyond syntax. The podcast explores the knowledge, skills, systems, and judgment people need to learn, build, and make responsible decisions with technology.</description>
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    <itunes:subtitle>We're two computer science educators learning and teaching Python</itunes:subtitle>
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  <title>Episode 16: When Philosophy and Python COLLIDE! - Part 2</title>
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  <pubDate>Tue, 02 Apr 2019 00:00:00 -0400</pubDate>
  <author>Sean Tibor and Kelly Paredes</author>
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  <itunes:episode>16</itunes:episode>
  <itunes:title>When Philosophy and Python COLLIDE! - Part 2</itunes:title>
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  <itunes:author>Sean Tibor and Kelly Paredes</itunes:author>
  <itunes:subtitle>Sean and Kelly continue their conversation about the philosophy and ethics of machine learning and artificial intelligence in Python. This episode focuses more on resources and tools for AI learning after last episode's focus on philosophy and ethics.</itunes:subtitle>
  <itunes:duration>44:31</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
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  <description>&lt;p&gt;Sean and Kelly continue their conversation about the philosophy and ethics of machine learning and artificial intelligence in Python. This episode focuses more on resources and tools for AI learning after last episode's focus on philosophy and ethics.&lt;/p&gt;</description>
  <itunes:keywords>python,artificial intelligence,machine learning, philosophy</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>Sean and Kelly continue their conversation about the philosophy and ethics of machine learning and artificial intelligence in Python. This episode focuses more on resources and tools for AI learning after last episode's focus on philosophy and ethics.</p><p>Sponsored By:</p><ul><li><a rel="nofollow" href="https://www.patreon.com/teachingpython">Patreon</a>: <a rel="nofollow" href="https://www.patreon.com/teachingpython">Want to hear more episodes from Kelly and Sean? Support us on Patreon so we can hire an audio editor!
</a></li></ul><p><a rel="payment" href="https://www.patreon.com/teachingpython">Support Teaching Python</a></p><p>Links:</p><ul><li><a title="What is Amazon Machine Learning? - Amazon Machine Learning" rel="nofollow" href="https://docs.aws.amazon.com/machine-learning/latest/dg/what-is-amazon-machine-learning.html">What is Amazon Machine Learning? - Amazon Machine Learning
</a> &mdash; Amazon Machine Learning (Amazon ML) is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. 
</li><li><a title="Build a PID Controller with Python – Onion" rel="nofollow" href="https://onion.io/2bt-pid-control-python/">Build a PID Controller with Python – Onion
</a> &mdash; This week we‘ll be learning how to build a PID Controller using Python, the Omega2, and our recently released ADC Expansion. We’re going to use our PID Controller to keep an incubator at a constant temperature, but this&nbsp;setup can be easily modified and the code reused for your own purposes!&nbsp;
</li><li><a title="The Difference Between YouTube’s Automatic Captions, DIY Captions, and 3Play Media Captions – 3Play Media" rel="nofollow" href="https://www.3playmedia.com/2019/02/04/the-difference-between-youtubes-automatic-captions-diy-captions-and-3play-media-captions/">The Difference Between YouTube’s Automatic Captions, DIY Captions, and 3Play Media Captions – 3Play Media
</a> &mdash; Have you ever watched a seemingly innocuous video with YouTube’s automatic captions? If not, go check it out.
</li><li><a title="Experiments with style transfer" rel="nofollow" href="http://genekogan.com/works/style-transfer/">Experiments with style transfer
</a> &mdash; Style transfer is the technique of recomposing images in the style of other images. These were mostly created using Justin Johnson’s code based on the paper by Gatys, Ecker, and Bethge demonstrating a method for restyling images using convolutional neural networks.
</li><li><a title="New Sims - PhET Simulations" rel="nofollow" href="https://phet.colorado.edu/en/simulations/category/new">New Sims - PhET Simulations
</a> &mdash; By converting our sims to HTML5, we make them seamlessly available across platforms and devices. Whether you have laptops, iPads, chromebooks, or BYOD, your favorite PhET sims are always right at your fingertips.
</li><li><a title="New App Makes It Easier to Colorize Old Photos | Smart News | Smithsonian" rel="nofollow" href="https://www.smithsonianmag.com/smart-news/new-app-joins-ai-and-artists-colorize-old-photos-180963396/">New App Makes It Easier to Colorize Old Photos | Smart News | Smithsonian
</a> &mdash; The software combines human input and a sophisticated neural network to make historical images pop
</li><li><a title="Jason Yosinski" rel="nofollow" href="http://yosinski.com/deepvis">Jason Yosinski
</a> &mdash; Deep neural networks have recently been producing amazing results! But how do they do what they do? Historically, they have been thought of as “black boxes”, meaning that their inner workings were mysterious and inscrutable. Recently, we and others have started shinning light into these black boxes to better understand exactly what each neuron has learned and thus what computation it is performing.
</li><li><a title="Convolution -- from Wolfram MathWorld" rel="nofollow" href="http://mathworld.wolfram.com/Convolution.html">Convolution -- from Wolfram MathWorld
</a> &mdash; A convolution is an integral that expresses the amount of overlap of one function  as it is shifted over another function .
</li><li><a title="TensorSpace.js" rel="nofollow" href="https://tensorspace.org/">TensorSpace.js
</a> &mdash; Interactive
Use Keras-like API to build interactive models in browsers

Intuitive
Visualize the information from intermediate inferences

Integrative
Support pre-trained models from TensorFlow, Keras, TensorFlow.js
</li><li><a title="A Neural Network Playground" rel="nofollow" href="https://playground.tensorflow.org/#activation=tanh&amp;batchSize=10&amp;dataset=circle&amp;regDataset=reg-plane&amp;learningRate=0.03&amp;regularizationRate=0&amp;noise=0&amp;networkShape=4,2&amp;seed=0.20651&amp;showTestData=false&amp;discretize=false&amp;percTrainData=50&amp;x=true&amp;y=true&amp;xTimesY=false&amp;xSquared=false&amp;ySquared=false&amp;cosX=false&amp;sinX=false&amp;cosY=false&amp;sinY=false&amp;collectStats=false&amp;problem=classification&amp;initZero=false&amp;hideText=false">A Neural Network Playground
</a> &mdash; Tinker With a Neural Network Right Here in Your Browser.
Don’t Worry, You Can’t Break It. We Promise.
</li><li><a title="Image Kernels explained visually" rel="nofollow" href="http://setosa.io/ev/image-kernels/">Image Kernels explained visually
</a> &mdash; An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.
</li><li><a title="(77) Convolutional Neural Network Visualization by Otavio Good - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=f0t-OCG79-U">(77) Convolutional Neural Network Visualization by Otavio Good - YouTube
</a> &mdash; Cropped and edited video-only excerpt of a great talk given by Otavio Good. Full talk titled "A visual and intuitive understanding of deep learning"
</li><li><a title="Like Animals, AI Is Learning From Experience" rel="nofollow" href="https://singularityhub.com/2019/03/18/like-animals-ai-is-learning-from-experience/#sm.000018t7uij157e0kpz0whobdt47b">Like Animals, AI Is Learning From Experience
</a> &mdash; Trial and error is one of the most fundamental learning strategies employed by animals, and we’re increasingly using it to teach intelligent machines too. Boosting the flow of ideas between biologists and computer scientists studying the approach could solve mysteries in animal cognition and help develop powerful new algorithms, say researchers.
</li><li><a title="(77) Numbers in Python Version 3 || Python Tutorial || Learn Python Programming - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=_87ASgggEg0&amp;feature=youtu.be">(77) Numbers in Python Version 3 || Python Tutorial || Learn Python Programming - YouTube
</a> &mdash; Today we talk about the different types of numbers available in Python version 3.  There are three types of numbers in Python V3: ints, floats and complex numbers. 
</li><li><a title="Mate Labs | Machine Learning for You" rel="nofollow" href="https://www.matelabs.ai/">Mate Labs | Machine Learning for You
</a> &mdash; Welcome to Mateverse: The Machine Learning Platform engineered for business professionals.Use Mateverse to craft tailored Machine Learning and Deep Learning models in a matter of minutes without writing a single line of code.
</li><li><a title="FAU Colab Notebook - Colaboratory" rel="nofollow" href="https://colab.research.google.com/drive/1uL7_woGrK7GviAlJkvB5IPu8P2Dwo2_L">FAU Colab Notebook - Colaboratory
</a> &mdash; Shared with permission from Elan Barenholtz (THANKS!)
</li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Sean and Kelly continue their conversation about the philosophy and ethics of machine learning and artificial intelligence in Python. This episode focuses more on resources and tools for AI learning after last episode's focus on philosophy and ethics.</p><p>Sponsored By:</p><ul><li><a rel="nofollow" href="https://www.patreon.com/teachingpython">Patreon</a>: <a rel="nofollow" href="https://www.patreon.com/teachingpython">Want to hear more episodes from Kelly and Sean? Support us on Patreon so we can hire an audio editor!
</a></li></ul><p><a rel="payment" href="https://www.patreon.com/teachingpython">Support Teaching Python</a></p><p>Links:</p><ul><li><a title="What is Amazon Machine Learning? - Amazon Machine Learning" rel="nofollow" href="https://docs.aws.amazon.com/machine-learning/latest/dg/what-is-amazon-machine-learning.html">What is Amazon Machine Learning? - Amazon Machine Learning
</a> &mdash; Amazon Machine Learning (Amazon ML) is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. 
</li><li><a title="Build a PID Controller with Python – Onion" rel="nofollow" href="https://onion.io/2bt-pid-control-python/">Build a PID Controller with Python – Onion
</a> &mdash; This week we‘ll be learning how to build a PID Controller using Python, the Omega2, and our recently released ADC Expansion. We’re going to use our PID Controller to keep an incubator at a constant temperature, but this&nbsp;setup can be easily modified and the code reused for your own purposes!&nbsp;
</li><li><a title="The Difference Between YouTube’s Automatic Captions, DIY Captions, and 3Play Media Captions – 3Play Media" rel="nofollow" href="https://www.3playmedia.com/2019/02/04/the-difference-between-youtubes-automatic-captions-diy-captions-and-3play-media-captions/">The Difference Between YouTube’s Automatic Captions, DIY Captions, and 3Play Media Captions – 3Play Media
</a> &mdash; Have you ever watched a seemingly innocuous video with YouTube’s automatic captions? If not, go check it out.
</li><li><a title="Experiments with style transfer" rel="nofollow" href="http://genekogan.com/works/style-transfer/">Experiments with style transfer
</a> &mdash; Style transfer is the technique of recomposing images in the style of other images. These were mostly created using Justin Johnson’s code based on the paper by Gatys, Ecker, and Bethge demonstrating a method for restyling images using convolutional neural networks.
</li><li><a title="New Sims - PhET Simulations" rel="nofollow" href="https://phet.colorado.edu/en/simulations/category/new">New Sims - PhET Simulations
</a> &mdash; By converting our sims to HTML5, we make them seamlessly available across platforms and devices. Whether you have laptops, iPads, chromebooks, or BYOD, your favorite PhET sims are always right at your fingertips.
</li><li><a title="New App Makes It Easier to Colorize Old Photos | Smart News | Smithsonian" rel="nofollow" href="https://www.smithsonianmag.com/smart-news/new-app-joins-ai-and-artists-colorize-old-photos-180963396/">New App Makes It Easier to Colorize Old Photos | Smart News | Smithsonian
</a> &mdash; The software combines human input and a sophisticated neural network to make historical images pop
</li><li><a title="Jason Yosinski" rel="nofollow" href="http://yosinski.com/deepvis">Jason Yosinski
</a> &mdash; Deep neural networks have recently been producing amazing results! But how do they do what they do? Historically, they have been thought of as “black boxes”, meaning that their inner workings were mysterious and inscrutable. Recently, we and others have started shinning light into these black boxes to better understand exactly what each neuron has learned and thus what computation it is performing.
</li><li><a title="Convolution -- from Wolfram MathWorld" rel="nofollow" href="http://mathworld.wolfram.com/Convolution.html">Convolution -- from Wolfram MathWorld
</a> &mdash; A convolution is an integral that expresses the amount of overlap of one function  as it is shifted over another function .
</li><li><a title="TensorSpace.js" rel="nofollow" href="https://tensorspace.org/">TensorSpace.js
</a> &mdash; Interactive
Use Keras-like API to build interactive models in browsers

Intuitive
Visualize the information from intermediate inferences

Integrative
Support pre-trained models from TensorFlow, Keras, TensorFlow.js
</li><li><a title="A Neural Network Playground" rel="nofollow" href="https://playground.tensorflow.org/#activation=tanh&amp;batchSize=10&amp;dataset=circle&amp;regDataset=reg-plane&amp;learningRate=0.03&amp;regularizationRate=0&amp;noise=0&amp;networkShape=4,2&amp;seed=0.20651&amp;showTestData=false&amp;discretize=false&amp;percTrainData=50&amp;x=true&amp;y=true&amp;xTimesY=false&amp;xSquared=false&amp;ySquared=false&amp;cosX=false&amp;sinX=false&amp;cosY=false&amp;sinY=false&amp;collectStats=false&amp;problem=classification&amp;initZero=false&amp;hideText=false">A Neural Network Playground
</a> &mdash; Tinker With a Neural Network Right Here in Your Browser.
Don’t Worry, You Can’t Break It. We Promise.
</li><li><a title="Image Kernels explained visually" rel="nofollow" href="http://setosa.io/ev/image-kernels/">Image Kernels explained visually
</a> &mdash; An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.
</li><li><a title="(77) Convolutional Neural Network Visualization by Otavio Good - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=f0t-OCG79-U">(77) Convolutional Neural Network Visualization by Otavio Good - YouTube
</a> &mdash; Cropped and edited video-only excerpt of a great talk given by Otavio Good. Full talk titled "A visual and intuitive understanding of deep learning"
</li><li><a title="Like Animals, AI Is Learning From Experience" rel="nofollow" href="https://singularityhub.com/2019/03/18/like-animals-ai-is-learning-from-experience/#sm.000018t7uij157e0kpz0whobdt47b">Like Animals, AI Is Learning From Experience
</a> &mdash; Trial and error is one of the most fundamental learning strategies employed by animals, and we’re increasingly using it to teach intelligent machines too. Boosting the flow of ideas between biologists and computer scientists studying the approach could solve mysteries in animal cognition and help develop powerful new algorithms, say researchers.
</li><li><a title="(77) Numbers in Python Version 3 || Python Tutorial || Learn Python Programming - YouTube" rel="nofollow" href="https://www.youtube.com/watch?v=_87ASgggEg0&amp;feature=youtu.be">(77) Numbers in Python Version 3 || Python Tutorial || Learn Python Programming - YouTube
</a> &mdash; Today we talk about the different types of numbers available in Python version 3.  There are three types of numbers in Python V3: ints, floats and complex numbers. 
</li><li><a title="Mate Labs | Machine Learning for You" rel="nofollow" href="https://www.matelabs.ai/">Mate Labs | Machine Learning for You
</a> &mdash; Welcome to Mateverse: The Machine Learning Platform engineered for business professionals.Use Mateverse to craft tailored Machine Learning and Deep Learning models in a matter of minutes without writing a single line of code.
</li><li><a title="FAU Colab Notebook - Colaboratory" rel="nofollow" href="https://colab.research.google.com/drive/1uL7_woGrK7GviAlJkvB5IPu8P2Dwo2_L">FAU Colab Notebook - Colaboratory
</a> &mdash; Shared with permission from Elan Barenholtz (THANKS!)
</li></ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 15: When Philosophy and Python COLLIDE! - Part 1</title>
  <link>https://www.teachingpython.fm/15</link>
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  <pubDate>Wed, 20 Mar 2019 00:30:00 -0400</pubDate>
  <author>Sean Tibor and Kelly Paredes</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/c8ea6bdf-0c80-46e7-a00a-639d7dc2be91/ca5a574b-c9cc-42d2-a805-ee547223f288.mp3" length="19024683" type="audio/mpeg"/>
  <itunes:episode>15</itunes:episode>
  <itunes:title>When Philosophy and Python COLLIDE! - Part 1</itunes:title>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Sean Tibor and Kelly Paredes</itunes:author>
  <itunes:subtitle>After a two day workshop on AI and Deep Learning, Kelly and Sean get philosophical about Python. From deep fakes to logical proofs to future non-driving generations that rely on self-driving cars, they'll explore the close relationship between computer science, ethics, and philosophy</itunes:subtitle>
  <itunes:duration>40:28</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://cdn.fireside.fm/file/fireside-images-2024/podcasts/images/c/c8ea6bdf-0c80-46e7-a00a-639d7dc2be91/cover.jpg?v=3"/>
  <description>&lt;p&gt;After a two day workshop on AI and Deep Learning, Kelly and Sean get philosophical about Python. From deep fakes to logical proofs to future non-driving generations that rely on self-driving cars, they'll explore the close relationship between computer science, ethics, and philosophy&lt;/p&gt;</description>
  <itunes:keywords>python, philosophy, ai, deep learning</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>After a two day workshop on AI and Deep Learning, Kelly and Sean get philosophical about Python. From deep fakes to logical proofs to future non-driving generations that rely on self-driving cars, they'll explore the close relationship between computer science, ethics, and philosophy</p><p><a rel="payment" href="https://www.patreon.com/teachingpython">Support Teaching Python</a></p><p>Links:</p><ul><li><a title="Pine Crest Innovation Institute" rel="nofollow" href="https://hubs.ly/H0gTnvB0">Pine Crest Innovation Institute
</a></li><li><a title="Department of Philosophy - Dietrich College of Humanities and Social Sciences - Carnegie Mellon University" rel="nofollow" href="https://www.cmu.edu/dietrich/philosophy/index.html">Department of Philosophy - Dietrich College of Humanities and Social Sciences - Carnegie Mellon University
</a> &mdash; Our vision of philosophy is interdisciplinary, rigorous, applied and relevant.
We do research on the foundations of science, computation, mathematics, rationality, causation, cognitive science, and other disciplines. We publish in philosophy, mathematics, computer science, biology, medicine, neuroscience, statistics, social science, educational research, linguistics, and other disciplines. 
</li><li><a title="Kelly&#39;s Jupyter Notebook" rel="nofollow" href="https://colab.research.google.com/drive/1TFa3kEhOEIGYAUy9vI5KQSCRUMDTyS_V">Kelly's Jupyter Notebook
</a></li><li><a title="About the MPCR | Machine Perception and Cognitive Robotics" rel="nofollow" href="http://mpcrlab.com/about-page/">About the MPCR | Machine Perception and Cognitive Robotics
</a> &mdash; The Machine Perception and Cognitive Robotic Lab (MPCR) is dedicated to applying, creating, and merging cutting edge technology across all disciplines.
</li><li><a title="micro:bit holder by geekmomprojects - Thingiverse" rel="nofollow" href="https://www.thingiverse.com/thing:2750805">micro:bit holder by geekmomprojects - Thingiverse
</a> &mdash; This stand holds 20 micro:bit boards vertically. It's a good way to organize and quickly count the micro:bit boards in a classroom setting.
</li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>After a two day workshop on AI and Deep Learning, Kelly and Sean get philosophical about Python. From deep fakes to logical proofs to future non-driving generations that rely on self-driving cars, they'll explore the close relationship between computer science, ethics, and philosophy</p><p><a rel="payment" href="https://www.patreon.com/teachingpython">Support Teaching Python</a></p><p>Links:</p><ul><li><a title="Pine Crest Innovation Institute" rel="nofollow" href="https://hubs.ly/H0gTnvB0">Pine Crest Innovation Institute
</a></li><li><a title="Department of Philosophy - Dietrich College of Humanities and Social Sciences - Carnegie Mellon University" rel="nofollow" href="https://www.cmu.edu/dietrich/philosophy/index.html">Department of Philosophy - Dietrich College of Humanities and Social Sciences - Carnegie Mellon University
</a> &mdash; Our vision of philosophy is interdisciplinary, rigorous, applied and relevant.
We do research on the foundations of science, computation, mathematics, rationality, causation, cognitive science, and other disciplines. We publish in philosophy, mathematics, computer science, biology, medicine, neuroscience, statistics, social science, educational research, linguistics, and other disciplines. 
</li><li><a title="Kelly&#39;s Jupyter Notebook" rel="nofollow" href="https://colab.research.google.com/drive/1TFa3kEhOEIGYAUy9vI5KQSCRUMDTyS_V">Kelly's Jupyter Notebook
</a></li><li><a title="About the MPCR | Machine Perception and Cognitive Robotics" rel="nofollow" href="http://mpcrlab.com/about-page/">About the MPCR | Machine Perception and Cognitive Robotics
</a> &mdash; The Machine Perception and Cognitive Robotic Lab (MPCR) is dedicated to applying, creating, and merging cutting edge technology across all disciplines.
</li><li><a title="micro:bit holder by geekmomprojects - Thingiverse" rel="nofollow" href="https://www.thingiverse.com/thing:2750805">micro:bit holder by geekmomprojects - Thingiverse
</a> &mdash; This stand holds 20 micro:bit boards vertically. It's a good way to organize and quickly count the micro:bit boards in a classroom setting.
</li></ul>]]>
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