DATA is available on the Trimble Learn platform: https://learn.trimble.com/lear....n/course/external/vi

This course is intended to introduce the Deep Learning (Convolutional Neural Network (CNN)) functionalities within the Trimble eCognition Developer Software and consists of 4 videos.

+ Introduction to Deep Learning 1 of 4: Introduction and Set-up
+ Introduction to Deep Learning 2 of 4: Creating Samples
+ Introduction to Deep Learning 3 of 4: Create / Train / Save CNN
+ Introduction to Deep Learning 4 of 4: Apply CNN with OBIA

This course is for free and can be conducted also with the Developer Trial version: https://geospatial.trimble.com/ecognition-trial.

Accessing this course from the Trimble Learn platform, you will have to create an account (also for free) and enroll to this course. Additionally to the DATA you will also receive a CERTIFICATE if you finish the course on the Trimble Learn platform.

Enjoy diving into eCognitions Deep Learning world!

______________Video Content_________________

00:00​ - Introduction
00:29​ - Create a model - Theory
01:46 - Train a model - Theory
02:59 - Create Convolutional Neural Network (alg.)
04:33 - Shuffle labeled sample patches (alg.)
05:27 - Train Convolutional Neural Network (alg.)
06:52 - Save Convolutional Neural Network (alg.)


(⊙_☉)

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Machine Learning by Andrew Ng - https://coursera.pxf.io/yRx0dD
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IBM Applied AI Professional Certificate - https://coursera.pxf.io/jWjkEn
DeepLearning.AI TensorFlow Developer Professional Certificate - https://coursera.pxf.io/AoY9zR
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So what was the breakthrough that allowed deep nets to combat the vanishing gradient problem? The answer has two parts, the first of which involves the RBM, an algorithm that can automatically detect the inherent patterns in data by reconstructing the input.

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Geoff Hinton of the University of Toronto, a pioneer and giant in the field, was able to devise a method for training deep nets. His work led to the creation of the Restricted Boltzmann Machine, or RBM.

Structurally, an RBM is a shallow neural net with just two layers – the visible layer and the hidden layer. In this net, each node connects to every node in the adjacent layer. The “restriction” refers to the fact that no two nodes from the same layer share a connection.

The goal of an RBM is to recreate the inputs as accurately as possible. During a forward pass, the inputs are modified by weights and biases and are used to activate the hidden layer. In the next pass, the activations from the hidden layer are modified by weights and biases and sent back to the input layer for activation. At the input layer, the modified activations are viewed as an input reconstruction and compared to the original input. A measure called KL Divergence is used to analyze the accuracy of the net. The training process involves continuously tweaking the weights and biases during both passes until the input is as close as possible to the reconstruction.

If you’ve ever worked with an RBM in one of your own projects, please comment and tell me about your experiences.

Because RBMs try to reconstruct the input, the data does not have to be labelled. This is important for many real-world applications because most data sets – photos, videos, and sensor signals for example – are unlabelled. By reconstructing the input, the RBM must also decipher the building blocks and patterns that are inherent in the data. Hence the RBM belongs to a family of feature extractors known as auto-encoders.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
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https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Machine Learning Specialization: https://bit.ly/3UzcejB

I recently completed the machine learning specialization by Andrew Ng that replaces the original legendary machine learning course now with updated coding assignments in Python and improved lecture quality. In this video I share my thoughts on the specialization, who it's designed for and what you'll learn if you decide to take it.

Timestamps:
0:00 - Introduction
0:41 - Why this specialization?
2:03 - Thoughts on the instructor Andrew Ng
2:50 - Overall rating
3:10 - Who is it for?
4:06 - Overview of the courses
7:40 - Course structure
10:35 - Thoughts on what can be improved
14:48 - More detailed content walkthrough
19:58 - Time to complete
20:30 - Cost
21:10 - Summary of the specialization