Computer Vision with Python
Computer Vision with Python, available at $19.99, has an average rating of 4, with 20 lectures, based on 4 reviews, and has 35 subscribers.
You will learn about Computer Vision Neural Networks Object detection Build and Train your own Computer Vision Model This course is ideal for individuals who are Computer Science Students or Computer Vision scientists It is particularly useful for Computer Science Students or Computer Vision scientists.
Enroll now: Computer Vision with Python
Summary
Title: Computer Vision with Python
Price: $19.99
Average Rating: 4
Number of Lectures: 20
Number of Published Lectures: 20
Number of Curriculum Items: 20
Number of Published Curriculum Objects: 20
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Computer Vision
- Neural Networks
- Object detection
- Build and Train your own Computer Vision Model
Who Should Attend
- Computer Science Students
- Computer Vision scientists
Target Audiences
- Computer Science Students
- Computer Vision scientists
In this class you will learn how to build Computer Vision algorithms for Image classification and Object detection using the Python Programming Language. We will first go through Neural Networks Basics: what are Neural networks , what is the theory behind neural networks , then we will talk about binary classifiers like an SVM for classifying the MNIST datasets, Students will learn how to classify the hand written digits of the MNIST dataset into multiple classes. We will discuss the different types of edge detectors to detect edges in images. After this we will discuss convolutionnal neural networks: how are they built, what are the most common and efficient CNN architectures and how do you implement them in Python. The topic of Object detection and Exhaustive search will also be dealt with. The last part of the class will be an example application of building and training a custom built Convolutionnal neural Network on the cloud to classify images from an open source dataset. All the steps from getting data, reading the data , building the network and training the network on the cloud will be carefully explained so that the student has a working example to be able to reuse for its own purpose.
Course Curriculum
Chapter 1: Neural Networks Basics
Lecture 1: Introduction
Lecture 2: What is a Neural network?
Lecture 3: The Neuron
Lecture 4: Activation Functions
Lecture 5: Gradient Descent and Back Propagation
Chapter 2: Predicting Digits with MNIST
Lecture 1: Training a Binary Classifier
Lecture 2: Training a MultiClass Classifier
Lecture 3: Evaluating the Confusion Matrix
Chapter 3: Edge Detectors
Lecture 1: Prewitt edge detection
Lecture 2: Sobel edge detection
Lecture 3: Laplacian edge detection
Chapter 4: Convolutionnal Neural networks
Lecture 1: What is a Convolutionnal Neural network ?
Lecture 2: Convolutionnal Layer
Lecture 3: Famous CNNs
Lecture 4: CNN Python Implementations
Lecture 5: Exhaustive Search
Chapter 5: Build and Train a Convolutionnal Neural network for classification
Lecture 1: Getting Data
Lecture 2: Reading the data
Lecture 3: Build and Train the Model
Lecture 4: Training of the Model on the cloud
Instructors
-
Lucas Mayrhofer
Computer scientist and Teacher
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 0 votes
- 5 stars: 3 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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