Computer Vision.pptx

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This document provides an introduction to computer vision and discusses several key concepts. It describes common computer vision applications such as image recognition, object detection, image segmentation, video analysis, style transfer, and generating new images. It then explains how deep learning and neural networks are used for image classification. The document outlines the process of feature extraction using convolutional neural networks, which involve filtering images with convolution kernels to extract visual features like lines, colors and textures, detecting those features with ReLU, and condensing the images with maximum pooling. It discusses concepts like convolution and pooling windows, strides, and padding used in this process.

Engineering

Computer Vision Applications
● Image Recognition and Classification
● Object Detection
● Image Segmentation
● Video Analysis
● Style Transfer
● Generating new images

● Deep learning is an approach to machine learning
characterized by deep stacks of computations.
● This depth of computation is what has enabled
deep learning models to disentangle the kinds of
complex and hierarchical patterns found in the
most challenging real-world datasets.
ML Extended
PROPRIETARY + CONFIDENTIAL

● You could think of each layer
in a neural network as
performing some kind of
relatively simple
transformation.
● Through a deep stack of
layers, a neural network can
transform its inputs in more
and more complex ways.
Neural Network

Image Classification
Extract - Visual Features
● a line,
● a color,
● a texture,
● a shape,
● a pattern or complex
pattern

Feature Extraction
The feature extraction performed by the
base consists of three basic operations:
1. Filter an image for a particular
feature (convolution)
2. Detect that feature within the filtered
image (ReLU)
3. Condense the image to enhance the
features (maximum pooling)

Condense with Maximum Pooling
The pooling step increases the proportion of active pixels to zero pixels.

How Convolution and Pooling happens?
● The convolution and pooling
operations share a common
feature: they are both performed
over a sliding window.
● With convolution, this "window"
is given by the dimensions of the
kernel, the parameter
kernel_size.
● With pooling, it is the pooling
window, given by pool_size

Stride - The Step Window moves
● We want high-quality features to
use for classification,
convolutional layers will most
often have strides=(1, 1).
● Increasing the stride Effect?
● For Pooling what should be
stride?

Padding - Treating Each Pixel Same
● When performing the sliding
window computation, there is a
question as to what to do at the
boundaries of the input.
● Staying entirely inside the input
image means the window will
never sit squarely over these
boundary pixels like it does for
every other pixel in the input.
Since we aren't treating all the
pixels exactly the same, could
there be a problem?

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1. Introduction to Computer Vision

3. Computer Vision Applications ● Image Recognition and Classification ● Object Detection ● Image Segmentation ● Video Analysis ● Style Transfer ● Generating new images

4. Computer Vision Applications

5. Image Classification Extract ???

6. Deep Learning

7. ● Deep learning is an approach to machine learning characterized by deep stacks of computations. ● This depth of computation is what has enabled deep learning models to disentangle the kinds of complex and hierarchical patterns found in the most challenging real-world datasets. ML Extended PROPRIETARY + CONFIDENTIAL

8. How it is done?

9. ● You could think of each layer in a neural network as performing some kind of relatively simple transformation. ● Through a deep stack of layers, a neural network can transform its inputs in more and more complex ways. Neural Network

10. Image Classification Extract - Visual Features ● a line, ● a color, ● a texture, ● a shape, ● a pattern or complex pattern

11. Whole Process

12. Feature Extraction The feature extraction performed by the base consists of three basic operations: 1. Filter an image for a particular feature (convolution) 2. Detect that feature within the filtered image (ReLU) 3. Condense the image to enhance the features (maximum pooling)

13. Filter with Convolution Kernel

14. Visual features Kernel Extracts

15. Detect with ReLU

16. Condense with Maximum Pooling The pooling step increases the proportion of active pixels to zero pixels.

17.

18. How Convolution and Pooling happens? ● The convolution and pooling operations share a common feature: they are both performed over a sliding window. ● With convolution, this "window" is given by the dimensions of the kernel, the parameter kernel_size. ● With pooling, it is the pooling window, given by pool_size

19. Stride - The Step Window moves ● We want high-quality features to use for classification, convolutional layers will most often have strides=(1, 1). ● Increasing the stride Effect? ● For Pooling what should be stride?

20. Padding - Treating Each Pixel Same ● When performing the sliding window computation, there is a question as to what to do at the boundaries of the input. ● Staying entirely inside the input image means the window will never sit squarely over these boundary pixels like it does for every other pixel in the input. Since we aren't treating all the pixels exactly the same, could there be a problem?

21. Want to Hear from You? Some Interesting CV applications

Editor's Notes

This session will go into a little more detail about machine learning. It is helpful if you have already completed some introduction to machine learning but if you have any questions please ask.
Here is a list representing some of the things a machine learning system can be trained to do. Question: What are some examples of features and products that you assume use machine learning? How could you tell it was ML?
Here is a list representing some of the things a machine learning system can be trained to do. Question: What are some examples of features and products that you assume use machine learning? How could you tell it was ML?
Here is a list representing some of the things a machine learning system can be trained to do. Question: What are some examples of features and products that you assume use machine learning? How could you tell it was ML?
Use your computer and go to news.google.com or some other search engine to find and read a couple of online news articles involving applications of ML. Note: Sometimes news stories refer to ML as artificial intelligence (AI). While you are reading, think about these questions. Give learners 20 minutes to complete this task.
Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.
Here's one definition.
Use your computer and go to news.google.com or some other search engine to find and read a couple of online news articles involving applications of ML. Note: Sometimes news stories refer to ML as artificial intelligence (AI). While you are reading, think about these questions. Give learners 20 minutes to complete this task.
Use your computer and go to news.google.com or some other search engine to find and read a couple of online news articles involving applications of ML. Note: Sometimes news stories refer to ML as artificial intelligence (AI). While you are reading, think about these questions. Give learners 20 minutes to complete this task.
In the previous slide, I said supervised machine learning is analogous to a student taking a test. Let's say I created the 4 machine learning regression models above. Which one is the best? It depends on your goal and what variable you are trying to optimize for. To grade how well a model is doing on its test, machine learning practitioners measure the distance between the model's prediction (indicated in these graphs by the blue line) and the example data. This is known as loss. [Click to animate slide] With the loss displayed. Which model is best at achieving the goal? [Click to animate slide] Model #3 has the lowest loss which indicates this model is best at achieving the goal.
Features are the variables which distinguish one example from another. They tell the machine learning model what parts of the data to look for patterns for achieving the goal. The first three variables would probably help the model determine a home price the other two probably would not. So lots of data is crucial to a machine learning system but it needs to be helpful and relevant data. Though you never know until you experiment to see what variables truly make an impact.
Ask learners to come up with a list of features which might be helpful in recommending the next video to watch. The last two might turn out to be very important variables, there's no way to know unless you experiment.
Ask learners to come up with a list of features which might be helpful in recommending the next video to watch. The last two might turn out to be very important variables, there's no way to know unless you experiment.
What about the scenario make it suitable for ML? Lots of examples of data Can run experiments to see which variables have an effect What is the benefit for the business or individual? The model can get the most visibility for their customer Would a human perform the job better? With so many variables, a human could get a sense for optimal times for a specific situation but it would become complex if the product, influencer, audience, etc were changed What are the inputs to the system? Potential variables could include: Time of day, person posting, brand, product, social network How could the ML model go wrong? If trained only for a specific type of post, product, etc it might not generalize to another scenario If the training data is for the wrong demographic (gender, age, marital status, etc) the patterns might not be relevant.
Give learners 2 minutes to create their own scenario. Have them turn to the person next to them and present the scenario. The other person has 2 minutes to answer the questions on the slide. Learners swap roles and repeat steps 2 and 3.
Features are the variables which distinguish one example from another. They tell the machine learning model what parts of the data to look for patterns for achieving the goal. The first three variables would probably help the model determine a home price the other two probably would not. So lots of data is crucial to a machine learning system but it needs to be helpful and relevant data. Though you never know until you experiment to see what variables truly make an impact.
Features are the variables which distinguish one example from another. They tell the machine learning model what parts of the data to look for patterns for achieving the goal. The first three variables would probably help the model determine a home price the other two probably would not. So lots of data is crucial to a machine learning system but it needs to be helpful and relevant data. Though you never know until you experiment to see what variables truly make an impact.
Features are the variables which distinguish one example from another. They tell the machine learning model what parts of the data to look for patterns for achieving the goal. The first three variables would probably help the model determine a home price the other two probably would not. So lots of data is crucial to a machine learning system but it needs to be helpful and relevant data. Though you never know until you experiment to see what variables truly make an impact.
Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.

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Editor's Notes