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Machine Learning is an absolutely fantastic skill to pick up. Here are some of my favorite Machine Learning courses that I have taken on Coursera.

LINKS:
Machine Learning by Andrew Ng - https://coursera.pxf.io/yRx0dD
Mathematics for Machine Learning - https://coursera.pxf.io/qnGJRn
IBM Applied AI Professional Certificate - https://coursera.pxf.io/jWjkEn
DeepLearning.AI TensorFlow Developer Professional Certificate - https://coursera.pxf.io/AoY9zR
Data Engineering, Big Data, and Machine Learning on GCP Specialization - https://coursera.pxf.io/VyjErR

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Coursera Courses:
Google Data Analyst Certification: https://coursera.pxf.io/5bBd62
Data Analysis with Python - https://coursera.pxf.io/BXY3Wy
IBM Data Analysis Specialization - https://coursera.pxf.io/AoYOdR
Tableau Data Visualization - https://coursera.pxf.io/MXYqaN

Udemy Courses:
Python for Data Analysis and Visualization- https://bit.ly/3hhX4LX
Statistics for Data Science - https://bit.ly/37jqDbq
SQL for Data Analysts (SSMS) - https://bit.ly/3fkqEij
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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.

Deep Learning TV on
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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
Isabel Descutner (Voice) -
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

DISCUSSION MEETING

WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE)

ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India)

DATE: 25 October 2021 to 29 October 2021

VENUE: Online

This week-long program will consist of several lectures by experts on different types of inverse problems and the underlying basic techniques to understand them. The targeted audience for these lectures are Master's and PhD students, and researchers with a strong background in PDEs. Prior experience of research in inverse problems will not be assumed.

The topics that we cover during this week-long program are:

1. Calderon problem.
2. Fractional Calderon problem.
3. Geometric inverse problems.
4. Integral geometry problems.
5. Inverse problems for Maxwell's equations.
6. Inverse problems involving non-linear equations.
7. Inverse problems for hyperbolic and transport equations.
8. Probabilistic inverse problems.
9. Machine Learning approaches in inverse problems.

CONTACT US: [email protected]
PROGRAM LINK: https://www.icts.res.in/discussion-meeting/ip2021