https://www.oxml.co.uk/publications/2016-Assael_Shillingford_LipNet.pdf

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#DeepLearning #Unsupervised
#Book
@Machine_learn

#DeepLearning #Video #Tensorflow #Tutorial —------------------------------------------------
@Machine_learn

#deeplearning
#j.patterson and Adam
#book
#page count=523
@Machine_learn

#deeplearning
#j.patterson and Adam
#book
#page count=523
@Machine_learn

#Deep Learning Papers Reading Roadmap
#Roadmap
#deeplearning
#papers
@Machine_learn


https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap

ODSC is bringing you Blockbuster workshop in Quantitative Finance+ Data Science absolutely FREE. The workshop has three presenters from diverse domains coming together to deliver it to you on June 29th ..Hurry Up!!! Limited seats Only.
Pankaj is Quantitative Finance researcher for State Street who is also one CFA level 2 candidate
Abinash Panda is CEO and Founder of Prodios is the Founding member of the famous pgmpy package. He has also written two books for Pakt publications in Probabilistic Graphical Models and Markov Models
Usha Rengaraju is an expert in Quantitative Finance and Bayesian Networks.
The workshop will also be followed by the AMA session by Swiggy Data Science Leaders.
RSVP here : https://bit.ly/2IiAzGc
#datascience #odsc #openai #neuralnetworks #ml #deeplearning #analytics #machinelearning #ai #artificialintelligence
@Machine_learn

@Machine_learn

Deep Learning For Real Time Streaming Data With Kafka And Tensorflow
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0

Reinforcement learning
#deeplearning #reinforcement
#python
@Machine_learn

Deep Learning
on Windows #deeplearning #2021 #book @Machine_learn

Implementing original #UNet paper using #PyTorch

Video tutorial on how to code your own neural network from scratch.

Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597

@Machine_learn

​​ByT5: Towards a token-free future with pre-trained byte-to-byte models

Pre-trained language models usually operate on the sequences of tokens, which are based on words or subword units.

Token-free models operate directly on the raw text (characters or bytes) instead. They can work with any language, are more robust to the noise, and don’t require preprocessing.

The authors use a modified mT5 architecture and show that their approach is competitive with token-level models.

Paper: https://arxiv.org/abs/2105.13626
Code: https://github.com/google-research/byt5

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-byt5

#nlp #deeplearning #transformer #pretraining
@Machine_learn

I gladly announce my first online course on #Statistics and #Mathematics for #MachineLearning and #DeepLearning.

The course will be in English, QA sessions with instructor will be in Turkish, Azerbaijani , or English. TA sessions will be in English.

This is the first course of tribology courses to help attendees to capture foundations and mathematics behind ML,DL models.

The courses are listed as follow:
1. Statistics Foundation for ML
2. Introduction to Statistical Learning for ML
3. Advanced Statistical Learning for DL

The course starts on 15 Jan 2022, at 13:00 to 15:00 (Istanbul time):

Course Fee:
Free for unemployed attendees. :)
200 USD for employed candidates :).

Course contents:
https://lnkd.in/dcXKxUjE

Course Registration:
https://lnkd.in/dMpzMfMG

Please kindly share with the ones who are interested.

https://web.njit.edu/~ym329/dlg_book/dlg_book.pdf

Deep Learning on Graphs

📖 Book

#deeplearning #DL

@Machine_learn

​
Recognize Anything: A Strong Image Tagging Model

Get ready for a breakthrough in the realm of AI: introducing the Recognize Anything Model (RAM), a powerful new model that is set to revolutionize image tagging. RAM, a titan in the world of large computer vision models, astoundingly exhibits the zero-shot ability to recognize any common category with an impressive level of accuracy. Shattering traditional approaches, RAM employs a unique paradigm for image tagging, utilizing large-scale image-text pairs for training instead of relying on tedious manual annotations.

Paper link: https://arxiv.org/abs/2306.03514
Code link: https://github.com/xinyu1205/recognize-anything
Project link: https://recognize-anything.github.io/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-ram

#deeplearning #cv #imagecaptioning
@Machine_lean