Deep Learning: CNNs for Visual Recognition
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Deep Learning: CNNs for Visual Recognition

Learn Convolutional Neural Networks for Visual Recognition and the building blocks and methods associated with them.
New
4.1 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
30 students enrolled
Created by Skillbox, LLC
Last updated 8/2017
English
Current price: $10 Original price: $195 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 2 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Get a practical deep dive into machine learning and deep learning algorithms
  • Explore CNN applications, visualization, and image enhancement
  • Understand the advantages and trade-offs of various CNN architectures
  • Understand how convolution can be applied to image effects
  • Understand how convolution helps image classification
View Curriculum
Requirements
  • Basic knowledge of deep learning principles
  • Some computer vision experience
Description

Welcome to this course: Deep Learning - Learn Convolutional Neural Networks.

Deep Learning has made some huge and significant contributions and it’s one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases. Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sensemaking of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars. 

In this course, we will learn an in-depth examination of CNNs, their fundamental processes, their applications, and their role in visualization and image enhancement. We will cover concepts, processes, and technologies such as CNN layers and architectures. You'll learn CNN image classification and segmentation, deep dream and style transfer, super-resolution, and generative adversarial networks (GANs). At the end of this course, students who come to this course with a basic knowledge of deep learning principles, some computer vision experience, and exposure to engineering math should gain the ability to implement CNNs and use them to create their own visualizations.

Don’t wait. Enroll in this course today.

Who is the target audience?
  • Software engineers
  • Students and professional computer scientists
  • Anyone who wants to apply deep learning to images
Compare to Other Deep Learning Courses
Curriculum For This Course
15 Lectures
01:47:25
+
Welcome
1 Lecture 01:40
+
Convolutional Neural Networks
6 Lectures 33:59
Introduction to Biological Analogies
04:05

Convolution Functions
03:42

Introduction to CNN Layers
06:11

CNN Layers in depth
05:37

Introduction to CNN Architectures
08:44

CNN Architectures in depth
05:40
+
CNN Applications
3 Lectures 29:16

Image Classification
11:43

CNN Segmentation
07:11
+
Visualizations
2 Lectures 18:31
CNN Visualizations
11:15

Deep Dream
07:16
+
Image Enhancement
2 Lectures 22:40
Super-Resolution Techniques
13:09

Generative Adversarial Networks
09:31
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Course Summary
1 Lecture 01:19
Summary
01:19
About the Instructor
Skillbox, LLC
4.2 Average rating
311 Reviews
8,430 Students
4 Courses
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Skillbox, LLC specializes in technical training via on-demand streaming. We are committed to providing students professional development, networking, and learning opportunities through our educational content. 

Our instructors are industry leading experts in the subjects they teach, because they continue to spend time working on real-world industry applications. They are expert trainers – able to communicate complex concepts clearly, understandably and with the enthusiasm that will inspire you to learn.

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