Note from author:
This course is your ‘local author’ for Deep Learning. The course is designed in a way for students to have a perfect kick-off towards building a career in Data Science. There are practical examples of applications using Deep Learning meeting industry standards. Having experience as a Data Scientist in both service based and product based companies, the author has blended this course to have a striking balance between the theoretical and practical concepts in Deep Learning for Computer Vision. Most of the code snippets you find in this course are going to be useful for you, when you solve a computer vision problem across your career as a data scientist. So, it is important for you to practice the code along to get a hands on experience as a data scientist.
This article has the complete list of lessons that covers the computer vision tutorials. A to Z of Computer Vision and Deep Learning.
Target Audience : Final year College Students, New to Data Science Career, IT employees who wants to switch to data science Career .
Important Note: The below hyperlinks are connected to my friend’s link, which means you can view them any number of time without having to be a premium member.
Welcome to Deep Learning for Computer Vision with Python. This course is your guide to mastering deep learning applied to practical, real-world computer vision problems utilizing the Python programming language and Keras library. Inside this series of course, you’ll learn how to apply deep learning to take-on projects such as image classification, object detection, training networks on large-scale datasets, and much more.
Table of Content:
Lesson 0: Set up your windows environment for Computer Vision
Lesson 1: Computer Vision Tutorial — Lesson 1
A. What pixels is, how they are used to form an image.
B. Images as Numpy array.
Lesson 2: Computer Vision Tutorial — Lesson 2
A. Loading an Image from Disk
B. Obtaining the ‘Height’, ‘Width’ and ‘Depth’ of Image
C .Finding R,G,B components of the Image
D. Drawing using OpenCV
Lesson 3: Computer Vision Tutorial — Lesson 3
Basic Image Processing
a) Rotation
b) Resizing
c) Flipping
e) Cropping
f) Image Arithmetic
Lesson 4: Computer Vision Tutorial — Lesson 4
A. Morphological operations
B. Exercise to extract the tabular structure in an invoice document using Morphological operations
Lesson 5: Computer Vision Tutorial — Lesson 5
A. Image Classification Using Machine Learning
B. Image Classification : Machine Learning way vs Deep Learning way
Lesson 6: Computer Vision Tutorial — Lesson 6
A. Machine Learning Introduction
B. Machine Learning Algorithms
a) K — Nearest neighbor Algorithm
b) Exercise : K-Nearest Neighbor classifier to classify hand written digits images from the MNIST datasets
Lesson 7: Computer Vision Tutorial — Lesson 7
A. Machine Learning Algorithms continuation …
a) Decision Trees
Lesson 8: Computer Vision Tutorial — Lesson 8
A. Machine Learning Algorithms continuation …
a) Logistic Regression
b) Approaching Logistic Regression with Neural Network Mindset
Lesson 9: Computer Vision Tutorial — Lesson 9: Coming soon…
A. Deep Learning Introduction
B. Neural Networks
Lesson 10: Computer Vision Tutorial — Lesson 10:
A. How CNN is Different from Traditional Neural Networks: Compositionality and Local Invariance
B. Convolution
C. Convolution Neural Networks Building Blocks
Lesson 11: Computer Vision Tutorial — Lesson 11:
A. CNN Network Visualizer
Lesson 12: Computer Vision Tutorial — Lesson 12:
A. Transfer Learning
B. Using Network as a Feature Extractor
C. Fine Tuning
Lesson 13: Computer Vision Tutorial — Lesson 13:
A. Resnet
Lesson 14: Computer Vision Tutorial — Lesson 14:
A. Object Detection
B. Exercise : Build Your First Object Detection Model
Lesson 15: Computer Vision Tutorial — Lesson 15:
A. Faster R-CNNs
Lesson 16: Computer Vision Tutorial — Lesson 16:
A. Single Shot Detector (SSD)
