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

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5 stars: 3 votes

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