Uploaded December 2020 | Updated September 2026, 2 weeks ago
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Learn how autoencoders work in an intuitive way. Learn about representation learning, latent space, and other fundamental concepts. I also explain how autoencoders are applied to important tasks such as data generation and denoising.
Slides:
github.com/musikalkemist/generating-sound-with-neural-networks/blob/main/03%20Autoencoders%20explained%20easily/Autoencoders%20Explained%20Easily%20.pdf
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===============================
Context
0:00 Intro
1:17 Key idea in autoencoders
3:03 Encoder
4:15 PCA vs Encoders
7:05 Decoder
8:21 Training autoencoders
10:13 Optimal autoencoder
12:57 Deep Autoencoder
14:11 Deep Convolutional Autoencoder
16:15 What's the point of compression/decompression?
17:12 Autoencoder applications
17:51 Generation with autoencoders
22:29 Denoising with autoencoders
26:06 Anomaly detection with autoencoders
26:39 Coming next
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Learn how autoencoders work in an intuitive way. Learn about representation learning, latent space, and other fundamental concepts. I also explain how autoencoders are applied to important tasks such as data generation and denoising.
Slides:
github.com/musikalkemist/generating-sound-with-neural-networks/blob/main/03%20Autoencoders%20explained%20easily/Autoencoders%20Explained%20Easily%20.pdf
Interested in hiring me as a consultant/freelancer?
valeriovelardo.com
Follow Valerio on Facebook:
facebook.com/TheSoundOfAI
Connect with Valerio on Linkedin:
linkedin.com/in/valeriovelardo
Follow Valerio on Twitter:
twitter.com/musikalkemist
===============================
Context
0:00 Intro
1:17 Key idea in autoencoders
3:03 Encoder
4:15 PCA vs Encoders
7:05 Decoder
8:21 Training autoencoders
10:13 Optimal autoencoder
12:57 Deep Autoencoder
14:11 Deep Convolutional Autoencoder
16:15 What's the point of compression/decompression?
17:12 Autoencoder applications
17:51 Generation with autoencoders
22:29 Denoising with autoencoders
26:06 Anomaly detection with autoencoders
26:39 Coming next
![This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind]](https://i.ytimg.com/vi/zO49vZ31xb0/mqdefault.jpg "This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind]
DeepMind published a revolutionary paper 🔥 They introduced Gato, a generalist AI agent that can carry out more than 600 tasks with a single transformer neural architecture. The tasks are varied, from playing Atari games to providing captions to images.
This paper demonstrates that:
📌 Generalist agents can perform reasonably well on many tasks / embodiments / modalities
📌 Generalist agents have the potential to learn new tasks with few data points
📌 By scaling up the parameter size, we can build a general-purpose agent
This work shocked me. I’ve always tackled AI from the perspective of Narrow Intelligence: build a specialised model that does well on a single - quite constrained - task.
👉 Gato paves the way for Artificial General Intelligence (AGI). In so doing, it opens new ethical dilemmas that should at least spark discussions in the AI community.
Since I’ve finished reading this paper, I can’t stop asking a question: is it ethical to push this research line given the grave dangers which may come with quasi-AGI agents?
Would you like to learn more? Check my last video, where I provide a breakdown of the paper, and analyse its ethical implications.
A Generalist Agent by DeepMind:
https://www.deepmind.com/publications/a-generalist-agent
Interested in hiring me as a consultant/freelancer?
https://valeriovelardo.com/
Join The Sound Of AI Slack community:
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Content:
0:00 Intro
1:11 General vs Narrow intellicence
3:06 Research hypotheses
4:25 Idea to approach AGI
8:23 Benefits of single network for many tasks
10:04 Datasets used
11:59 Data preparation
18:24 Model architecture
20:31 Training
22:48 Loss function
27:25 Recognising a task
30:50 Inference
33:37 How does the model perform?
39:00 Scale analysis
40:18 Can the model tackle unseen tasks?
44:14 Key discoveries
47:40 Ethical implications")
