Computer Vision

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Welcome to Computer Vision

Welcome to the Computer Vision Nanodegree program!

Image Representation & Classification

Learn how images are represented numerically and implement image processing techniques, such as color masking and binary classification.

Convolutional Filters and Edge Detection

Learn about frequency in images and implement your own image filters for detecting edges and shapes in an image. Use a computer vision library to perform face detection.

Types of Features & Image Segmentation

Program a corner detector and learn techniques, like k-means clustering, for segmenting an image into unique parts.

Feature Vectors

Learn how to describe objects and images using feature vectors.

CNN Layers and Feature Visualization

Define and train your own convolution neural network for clothing recognition. Use feature visualization techniques to see what a network has learned.

Project: Facial Keypoint Detection

Apply your knowledge of image processing and deep learning to create a CNN for facial keypoint (eyes, mouth, nose, etc.) detection.

Cloud Computing with Google Cloud

Learn how to leverage GPUs on Google Cloud for machine learning and scientific computing.

Optional: Cloud Computing with AWS

Take advantage of Amazon's GPUs to train your neural network faster. In this lesson, you'll learn how to setup an instance on AWS and train a neural network on a GPU.

Advanced CNN Architectures

Learn about advances in CNN architectures and see how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images.

YOLO

Learn about the YOLO (You Only Look Once) multi-object detection model and work with a YOLO implementation.

RNN's

Explore how memory can be incorporated into a deep learning model using recurrent neural networks (RNNs). Learn how RNNs can learn from and generate ordered sequences of data.

Long Short-Term Memory Networks (LSTMs)

Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.

Hyperparameters

Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.

Optional: Attention Mechanisms

Attention is one of the most important recent innovations in deep learning. In this section, you'll learn how attention models work and go over a basic code implementation.

Image Captioning

Learn how to combine CNNs and RNNs to build a complex, automatic image captioning model.

Project: Image Captioning

Train a CNN-RNN model to predict captions for a given image. Your main task will be to implement an effective RNN decoder for a CNN encoder.

Introduction to Motion

This lesson introduces a way to represent motion mathematically, outlines what you'll learn in this section, and introduces optical flow.

Robot Localization

Learn to implement a Bayesian filter to locate a robot in space and represent uncertainty in robot motion.

Mini-project: 2D Histogram Filter

Write sense and move functions (and debug) a 2D histogram filter!

Introduction to Kalman Filters

Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.

Representing State and Motion

Learn about representing the state of a car in a vector that can be modified using linear algebra.

Matrices and Transformation of State

Linear Algebra is a rich branch of math and a useful tool. In this lesson you'll learn about the matrix operations that underly multidimensional Kalman Filters.

Simultaneous Localization and Mapping

Learn how to implement SLAM: simultaneously localize an autonomous vehicle and create a map of landmarks in an environment.

Optional: Vehicle Motion and Calculus

Review the basics of calculus and see how to derive the x and y components of a self-driving car's motion from sensor measurements and other data.

Project: Landmark Detection & Tracking

Implement SLAM, a robust method for tracking an object over time and mapping out its surrounding environment, using elements of probability, motion models, and linear algebra.

Applying Deep Learning Models

Try out a few really cool applications of computer vision and deep learning, such as style transfer, using pre-trained models that others have generously provided on Github.

Feedforward and Backpropagation

Short introduction to neural networks: how they train by doing a feedforward pass then performing backpropagation.

Training Neural Networks

Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.

Deep Learning with PyTorch

Learn how to use PyTorch for building deep learning models

Deep Learning for Cancer Detection with Sebastian Thrun

Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with convolutional neural networks.

Sentiment Analysis

In this lesson, Andrew Trask, the author of Grokking Deep Learning, will walk you through using neural networks for sentiment analysis.

Fully-Convolutional Neural Networks & Semantic Segmentation

Get a high-level overview of how fully-convolutional neural networks work, and see how they can be used to classify every pixel in an image.

C++ Getting Started

The differences between C++ and Python and how to write C++ code.

C++ Vectors

To program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.

Practical C++

Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.

C++ Object Oriented Programming

Learn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.

Python and C++ Speed

In this lesson, we'll compare the execution times of C++ and Python programs.

C++ Intro to Optimization

Optimizing C++ involves understanding how a computer actually runs your programs. You'll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.

C++ Optimization Practice

Now you understand how C++ programs execute. It's time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson's code optimization project.

Project: Optimize Histogram Filter

Get ready to optimize some C++ code. You are provided with a working 2-dimensional histogram filter; your job is to get the histogram filter code to run faster!

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