Copy Other Peoples Work

Brandon Rohrer · Intermediate ·🖊️ Copywriting & Content Strategy ·2y ago

Key Takeaways

The video discusses how to leverage the Cortana Intelligence Gallery, a part of Microsoft Azure Machine Learning, to find and borrow examples of machine learning experiments and models as a starting point for one's own work, specifically for copywriting and data science applications.

Full Transcript

welcome to the fifth video in the series data science for beginners in this one you'll discover a place to find examples that you can borrow from as a starting point for your own work you might get the most out of this video If you first watch the earlier videos in this series one of the trade secrets of data science is getting other people to do your work for you Microsoft has a cloud-based service called meure machine learning that you're welcome to try for free it provides you with a workspace where you can experiment with different machine learning algorithms and when you've got your solution worked out you can launch it as a web service part of this service is something called the Cortana intelligence Gallery it contains a variety of resources one of which is a collection of azure machine learning experiments or models that people have built and contributed for other to use these experiments are a great way to leverage the thought and hard work of others to get you started on your own Solutions you can find The Gallery at aka.ms Cortana intelligence Gallery everyone is welcome to browse through it if you click experiments at the top you'll see a number of the most recent and popular experiments in the gallery you can search through the rest of the experiments by clicking browse all at the top of the screen and there you can enter Search terms and choose search filters so for instance let's say you want to see an example of how clustering works so you search for clustering experiments here's an interesting one that someone contributed to the gallery click on that experiment and you get a web page that describes the work that this contributor did along with some of their results notice the link that says open in studio I can click on that and it takes me right to Azure machine Learning Studio it creates a copy of the experiment and puts it in my own workspace and this includes the contributor data set all the processing that they did all of the algorithms that they used and how they saved out the results and now I have a starting point I can swap out their data for my own and do my own tweaking of the model this gives me a running start and it lets me build on the work of people who really know what they're doing there are other experiments in the Cortana intelligence gallery that were contributed specifically to provide how-to examples for people new to data science for instance there's an experiment in the gallery that demonstrates how to handle missing values it walks you through 15 different ways of substituting empty values and talks about the benefits of each method and when to use it Cortana intelligence Gallery is a place to find working experiments that you can use as a starting point for your own Solutions be sure to check out the other videos in data science for beginners from Microsoft Azure machine learning [Music]
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3 Robot Learning with a Biologically-Inspired Brain (BECCA), The Sequel
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10 Watching for the unexpected: BECCA detects anomalies in video data
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11 BECCA finds a stationary target
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12 BECCA finds a stationary target at 3X speed
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13 BECCA watches the X-men and Bruce Lee
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14 BECCA plays Quidditch
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15 BECCA chases a ball
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16 BECCA chases a ball, part 2
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17 Becca chases a ball, part 3
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18 BECCA creates features from MNIST
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19 How reinforcement learning works in Becca 7
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20 Deep Learning Demystified
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21 How Data Science Works
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22 How Convolutional Neural Networks work
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23 How Bayes Theorem works
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24 How Deep Neural Networks Work
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25 Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
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26 How Support Vector Machines work / How to open a black box
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27 How autocorrelation works
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28 Getting closer to human intelligence through robotics
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29 A minimalist's guide to slicing and indexing pandas DataFrames
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30 How decision trees work
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31 Data scientist archetypes
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32 How to use python's datetime package
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33 How optimization for machine learning works, part 1
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34 How optimization for machine learning works, part 2
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35 How optimization for machine learning works, part 3
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36 How optimization for machine learning works, part 4
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37 How convolutional neural networks work, in depth
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38 How to pick a machine learning model 4: Splitting the data
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39 How to pick a machine learning model 3: Choosing a loss function
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40 How to pick a machine learning model 2: Separating signal from noise
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41 How to pick a machine learning model 1: Choosing between models
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42 How to pick a machine learning model 5: Navigating assumptions
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43 What do neural networks learn?
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44 Interview with iRobot's Director of Data Science Angela Bassa
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45 How Backpropagation Works
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46 Evolutionary Powell's method: A discrete optimizer for hyperparameter optimization
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47 1D convolution for neural networks, part 1: Sliding dot product
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48 1D convolution for neural networks, part 2: Convolution copies the kernel
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49 1D convolution for neural networks, part 3: Sliding dot product equations longhand
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50 1D convolution for neural networks, part 4: Convolution equation
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51 1D convolution for neural networks, part 5: Backpropagation
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52 1D convolution for neural networks, part 6: Input gradient
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53 1D convolution for neural networks, part 7: Weight gradient
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54 1D convolution for neural networks, part 8: Padding
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55 1D convolution for neural networks, part 9: Stride
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56 The Four Grand Challenges of Robots in the Home
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57 How Convolution Works
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58 The Softmax neural network layer
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59 Batch normalization
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The Cortana Intelligence Gallery provides a platform to discover and borrow machine learning experiments and models, enabling data scientists and copywriters to leverage the work of others and build upon existing solutions. This resource can be used to find examples of how to handle common data science tasks, such as clustering and handling missing values. By using the Gallery, users can save time and effort by building upon the work of others.

Key Takeaways
  1. Browse the Cortana Intelligence Gallery
  2. Search for relevant experiments
  3. Open an experiment in Azure Machine Learning Studio
  4. Swap out the contributor's data with your own
  5. Tweak the model to suit your needs
  6. Deploy the model as a web service
💡 The Cortana Intelligence Gallery provides a valuable resource for data scientists and copywriters to leverage the work of others and build upon existing solutions, saving time and effort.

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