Deep learning beyond_the_hype-franceia@lapaillasse

Mar 8, 20171 like2,567 views

This document provides an overview of deep learning techniques. It discusses the differences between machine learning, deep learning, and artificial intelligence. It then covers various deep learning architectures like convolutional neural networks, recurrent neural networks, LSTMs, and generative adversarial networks. It shows how deep learning can be used for tasks beyond classification like music generation and image generation from text descriptions. It also discusses limitations and challenges like adversarial examples and the need for large amounts of labeled training data compared to unsupervised learning approaches.

Deep Learning,
beyond the hype!
3 mars 2017, La Paillasse
Sébastien TREGUER

Machine Learning vs Deep learning
vs A.I.

What are the different species in the
zoo?

Performances
2012: from 26% to 16% error rate in image
classification with convolutional neural nets Similar disruption observed in speech

What’s the secret sauce of Deep
Neural Nets ?
● RMSProp
● ADAM
● SGD
● Sigmoid
● Tanh
● ReLU
● Leaky ReLU
● CNN
● RNN
● LSTM
● GAN
● WaveNet
● PathNet
The more,
the cleaner
and well
distributed
the better

How does it works?
From details captured in the first layers to higher level of abstraction in deeper layers

What about creativity?
Representation
Learning
Reinforcement Learning,
Alphago learning by
playing against itself
Music improvisation
Logic operations on images

Input = “A small yellow bird with a black crown and a short black pointed beak”
Which images are real, which images are generated by the algorithm?
From classification to generation

A small yellow bird with a back crown and a short black pointed beak
From classification to generation
Answer: None of these images ever existed. They are all generated by the algorithm.
StackGAN, Han Zhang & al, paper here

GAN
Generative Adversarial Networks
Ian J. Goodfellow & al, 2014
StackGAN
Text to Photo-realistic Image Synthesis
Han Zhang & al, 2016
From classification to generation

Adversarial examples
“Panda”
57.7% Confidence
“Nematode”
8.2% Confidence
“Gibbon”
99.9% Confidence
Any Limits?

Unsurpervised learning
Reinforcement Learning
→ predict a scalar reward
→ A few bits per samples
Supervised Learning = on-sight navigation
→ Predicting human supplied data
→ 10 to 10000 bits per samples
All the rest is unsupervised Learning = offshore navigation
→ Predicting unknown parts from observation
→ Building its own representation
→ Millions of bits per samples
Ex: predicting frames in videos, generating images, music...
Any Limits?

Energy
efficiency?
VS
Brain = 20W
2.2 billion megaflops
Super Computers =
until 10 millions watts
The race for power

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A summary report into attitudes to and implications of AI as it relates to disability. Will AI enabled solutions create greater opportunities or amplify biases in society and datasets? Informed by primary mixed methods research conducted in the UK and globally by Open Inclusion on behalf of the Institute of People Centred AI, Uni of Surrey and Royal Holloway University. Initially presented at Google London in Jan 2025. If you prefer an audio visual format you can access the full video recorded at Google ADC London where we presented this research in January 2025. It has captioned content and audio described visuals and is available at https://www.youtube.com/watch?v=p_1cv042U_U. There is also a short Fireside Chat about the research held at Zero Project Conference March 2025 available at https://www.youtube.com/live/oFCgIg78-mI?si=EoIaEgDw2U7DFXsN&t=11879. If 狠狠撸 Share's format is not accessible to you in any way, please contact us at contact@openinclusion.com and we can provide you with the underlying document.

The truth behind the numbers: spotting statistical misuse.pptxandyprosser3

As a producer of official statistics, being able to define what misinformation means in relation to data and statistics is so important to us. For our sixth webinar, we explored how we handle statistical misuse especially in the media. We were also joined by speakers from the Office for Statistics Regulation (OSR) to explain how they play an important role in investigating and challenging the misuse of statistics across government.

HIRE MUYERN TRUST HACKER FOR AUTHENTIC CYBER SERVICESanastasiapenova16

It’s hard to imagine the frustration and helplessness a 65-year-old man with limited computer skills must feel when facing the aftermath of a crypto scam. Recovering a hacked trading wallet can feel like an absolute nightmare, especially when every step seems to lead you into an endless loop of failed solutions. That’s exactly what I went through over the past four weeks. After my trading wallet was compromised, the hacker changed my email address, password, and even removed my phone number from the account. For someone with little technical expertise, this was not just overwhelming, it was a disaster. Every suggested solution I came across in online help centers was either too complex or simply ineffective. I tried countless links, tutorials, and forums, only to find myself stuck, not even close to reclaiming my stolen crypto. In a last-ditch effort, I turned to Google and stumbled upon a review about MUYERN TRUST HACKER. At first, I was skeptical, like anyone would be in my position. But the glowing reviews, especially from people with similar experiences, gave me a glimmer of hope. Despite my doubts, I decided to reach out to them for assistance.The team at MUYERN TRUST HACKER immediately put me at ease. They were professional, understanding, and reassuring. Unlike other services that felt impersonal or automated, they took the time to walk me through every step of the recovery process. The fact that they were willing to schedule a 25-minute session to help me properly secure my account after recovery was invaluable. Today, I’m grateful to say that my stolen crypto has been fully recovered, and my account is secure again. This experience has taught me that sometimes, even when you feel like all hope is lost, there’s always a way to fight back. If you’re going through something similar, don’t give up. Reach out to MUYERN TRUST HACKER. Even if you’ve already tried everything, their expertise and persistence might just be the solution you need.I wholeheartedly recommend MUYERN TRUST HACKER to anyone facing the same situation. Whether you’re a novice or experienced in technology, they’re the right team to trust when it comes to recovering stolen crypto or securing your accounts. Don’t hesitate to contact them, it's worth it. Reach out to them on telegram at muyerntrusthackertech or web: ht tps :// muyerntrusthacker . o r g for faster response.

CloudMonitor - Architecture Audit Review February 2025.pdfRodney Joyce

CloudMonitor FinOps is now a Microsoft Certified solution in the Azure Marketplace. This little badge means that we passed a 3rd-party Technical Audit as well as met various sales KPIs and milestones over the last 12 months. We used our existing Architecture docs for CISOs and Cloud Architects to craft an Audit Response - I've shared it below to help others obtain their cert. Interestingly, 90% of our customers are in the USA, with very few in Australia. This is odd as the first thing I hear in every meetup and conference, from partners, customers and Microsoft, is that they want to optimise their cloud spend! But very few Australian companies are using the FinOps Framework to lower Azure costs.

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As the demand for vector databases and Generative AI continues to rise, integrating vector storage and search capabilities into traditional databases has become increasingly important. This session introduces the *MyVector Plugin*, a project that brings native vector storage and similarity search to MySQL. Unlike PostgreSQL, which offers interfaces for adding new data types and index methods, MySQL lacks such extensibility. However, by utilizing MySQL's server component plugin and UDF, the *MyVector Plugin* successfully adds a fully functional vector search feature within the existing MySQL + InnoDB infrastructure, eliminating the need for a separate vector database. The session explains the technical aspects of integrating vector support into MySQL, the challenges posed by its architecture, and real-world use cases that showcase the advantages of combining vector search with MySQL's robust features. Attendees will leave with practical insights on how to add vector search capabilities to their MySQL

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Deep learning beyond_the_hype-franceia@lapaillasse

1. Deep Learning, beyond the hype! 3 mars 2017, La Paillasse Sébastien TREGUER

2. Machine Learning vs Deep learning vs A.I.

3. What are the different species in the zoo?

4. Hype or reality?

5. Performances 2012: from 26% to 16% error rate in image classification with convolutional neural nets Similar disruption observed in speech

6. What’s the secret sauce of Deep Neural Nets ? ● RMSProp ● ADAM ● SGD ● Sigmoid ● Tanh ● ReLU ● Leaky ReLU ● CNN ● RNN ● LSTM ● GAN ● WaveNet ● PathNet The more, the cleaner and well distributed the better

7. How does it works? From details captured in the first layers to higher level of abstraction in deeper layers

8. Depth and Performances

9. What about creativity? Representation Learning Reinforcement Learning, Alphago learning by playing against itself Music improvisation Logic operations on images

10. Input = “A small yellow bird with a black crown and a short black pointed beak” Which images are real, which images are generated by the algorithm? From classification to generation

11. A small yellow bird with a back crown and a short black pointed beak From classification to generation Answer: None of these images ever existed. They are all generated by the algorithm. StackGAN, Han Zhang & al, paper here

12. GAN Generative Adversarial Networks Ian J. Goodfellow & al, 2014 StackGAN Text to Photo-realistic Image Synthesis Han Zhang & al, 2016 From classification to generation

13. Adversarial examples “Panda” 57.7% Confidence “Nematode” 8.2% Confidence “Gibbon” 99.9% Confidence Any Limits?

14. Unsurpervised learning Reinforcement Learning → predict a scalar reward → A few bits per samples Supervised Learning = on-sight navigation → Predicting human supplied data → 10 to 10000 bits per samples All the rest is unsupervised Learning = offshore navigation → Predicting unknown parts from observation → Building its own representation → Millions of bits per samples Ex: predicting frames in videos, generating images, music... Any Limits?

15. Energy efficiency? VS Brain = 20W 2.2 billion megaflops Super Computers = until 10 millions watts The race for power

狠狠撸

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