Code generated in the video can be downloaded from here: https://github.com/bnsreenu/py....thon_for_microscopis

Special Thanks To Ashish Patel And All The Bloggers For the Amazing Contribution
https://www.linkedin.com/in/ashishpatel2604/
https://github.com/ashishpatel....26/500-AI-Machine-le
⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only

Starter In Data Science

1 Complete Machine Learning Playlist:(Top 24 videos)

https://www.youtube.com/playli....st?list=PLZoTAELRMXV

2 Statistics in Machine Learning:(Understand some Concepts With Respect To Data)- Complete Playlist
https://www.youtube.com/playli....st?list=PLZoTAELRMXV

3. Feature Engineering(Complete Playlist)
https://www.youtube.com/playli....st?list=PLZoTAELRMXV

4. Continue The Complete Machine Learning Playlist(24-all the videos)

5. Live Stream Playlist:(Top 10 videos)
https://www.youtube.com/playli....st?list=PLZoTAELRMXV

6. Machine Learning Pipelines

https://www.youtube.com/playli....st?list=PLZoTAELRMXV

7. Complete Deep Learning Playlist:
Tensorflow And Keras-https://www.youtube.com/playli....st?list=PLZoTAELRMXV

Pytorch: https://www.youtube.com/playli....st?list=PLZoTAELRMXV

8. Live Projects Playlist:
https://www.youtube.com/playli....st?list=PLZoTAELRMXV
9. Live stream Playlist: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
10.Docker Playlist: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
11. Mongodb: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
12. Machine Learning Interviews: https://www.youtube.com/playli....st?list=PLZoTAELRMXV

Deeplearning4j is one of the few libraries that allows you to train your net over a distributed, multi-node cluster. The library provides an Iterative Map-Reduce procedure as well as a set of tools for configuring a Deep Net using hyper-parameters.

Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv

The Deeplearning4j Java library was created by Adam Gibson in response to the lack of distributed, multi-node capabilities in other Deep Net libraries. Deeplearning4j can run on both Scala and Clojure, and it provides built-in GPU support for a distributed framework. You can also use the library to set up a deep net by configuring its hyper-parameters.

Deeplearning4j supports nearly every type of deep net, including the MLP, RBM/DBN, Convolutional Net, Recurrent Net, RNTN, and autoencoders. In addition, the Canova vectorization library is included with the package.

How does the Iterative Map-Reduce procedure differ from standard Map-Reduce? In Deeplearning4j, there are two different steps:

- MAP: Input data is distributed throughout the cluster, with every node receiving a different portion of the data. Each node begins training with its input set.
- REDUCE: After training, the parameters of all the nets are averaged. Every node overwrites its net’s parameters with this global average.

These two steps are repeated iteratively until the error is sufficiently small.

Have you ever trained a deep net over a distributed architecture? Please comment and share your experiences.

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
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Submission Link (for Bootcamp candidates):https://forms.gle/iG3dYP35cwDptAkc7

👽👽Last submission Date: 16th July 2022👽👽
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01:01:29 Dataset Link and Jam board Link


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