Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

gradient descent navigating the loss landscape

tender

6,198 subscribers

224,663 Aufrufe • vor 8 Monaten •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

33 Kommentare

Profilbild von Adhmadóir uí Liatháin;

Adhmadóir uí Liatháin;vor 8 Monaten

Lower the learning rate maybe, that's a lot of energy

Profilbild von Meet

Meetvor 8 Monaten

LR too high. Adam might work.

Profilbild von David Antunes

David Antunesvor 8 Monaten

or: GD getting stuck in a local minimum for a couple of epochs

Profilbild von Everlier

Everliervor 8 Monaten

That's what happens when mixing reward functions for robustness :)

Profilbild von William Vabrinskas

William Vabrinskasvor 8 Monaten

This looks like the learning rate is too high

Profilbild von pishi

pishivor 8 Monaten

Try conjugated gradient descent :)

Profilbild von nibbleton 🛠️

nibbleton 🛠️vor 8 Monaten

@TensorTwerker

Profilbild von Gauri Tripathi

Gauri Tripathivor 8 Monaten

perfect 😂

Profilbild von Aditya Deshlahre

Aditya Deshlahrevor 8 Monaten

I see some Mathematics in there

Profilbild von secemp

secempvor 8 Monaten

great way to start the year fr

Profilbild von Ayush

Ayushvor 8 Monaten

When k-means fails, you better call this guy. Saul never did anything for anybody. At least this guy solves an autograd problem in k-means!

Profilbild von Jake Mcmahon

Jake Mcmahonvor 8 Monaten

You just don’t see this kind of content on LinkedIn.

Profilbild von Huw Kelvin Morgan

Huw Kelvin Morganvor 8 Monaten

If there was anything that i love more about car's is i love a challenge when driving and this is me in my mind's eye enjoying my self if it was me lol

Profilbild von Quanternion

Quanternionvor 8 Monaten

Looks kinda fun tho

Profilbild von Abdul Rahim Mirani

Abdul Rahim Miranivor 8 Monaten

lol hahahaha

Profilbild von Uzeb Khan

Uzeb Khanvor 8 Monaten

I think momentum is working quite a bit

Profilbild von SJP research

SJP researchvor 8 Monaten

🥲

Profilbild von Tai Lung 2.0

Tai Lung 2.0vor 8 Monaten

Sochastic Gradient Descent 😭😭😭

Profilbild von aryan gupta

aryan guptavor 8 Monaten

@jaraxiong 😂😂

Profilbild von Artificial Shitposting Intelligence

Artificial Shitposting Intelligencevor 8 Monaten

gradient descent nearing a truth that violates the guardrails cost function

Profilbild von Saksham Saha

Saksham Sahavor 8 Monaten

why did I laugh at this😂

Profilbild von eatnow

eatnowvor 8 Monaten

SWA

Profilbild von Physix Today

Physix Todayvor 8 Monaten

Damn I understand this joke. Am I normal?

Profilbild von !Briann🗿

!Briann🗿vor 8 Monaten

Real😂

Profilbild von Brett Adams

Brett Adamsvor 8 Monaten

At least the AIs are having fun. Also, git bisect slow

Profilbild von Ben▫️

Ben▫️vor 8 Monaten

only Nesterov Accelerated Gradient could work because the convex seems too smooth

Profilbild von 🏮

🏮vor 8 Monaten

lol

Profilbild von anil

anilvor 8 Monaten

im so jaded. nothing impresses me anymore 😂

Profilbild von jellybean ❄️

jellybean ❄️vor 8 Monaten

haven’t seen these in a while

Profilbild von Prashant Arun Srivastava

Prashant Arun Srivastavavor 8 Monaten

Inaccurate

Profilbild von Aaditya

Aadityavor 8 Monaten

Elite ball knowledge required

Profilbild von vaibhav

vaibhavvor 8 Monaten

You need to adjust learning rate

Profilbild von 𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰vor 8 Monaten

😂

Ähnliche Videos

wrote a piece on how we do tiling and gradient descent on TinyTPU included some animations of the weight and bias gradient descent

wrote a piece on how we do tiling and gradient descent on TinyTPU included some animations of the weight and bias gradient descent

Xander Chin

33,195 Aufrufe • vor 1 Jahr

Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.

Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.

tetsuo

142,508 Aufrufe • vor 1 Jahr

Gradient Descent: How a Machine Learns (Techno Explainer)

Gradient Descent: How a Machine Learns (Techno Explainer)

Dr Singularity

12,138 Aufrufe • vor 29 Tagen

For small step sizes, mirror descent is equivalent to a gradient descent on a Hessian manifold.

For small step sizes, mirror descent is equivalent to a gradient descent on a Hessian manifold.

Gabriel Peyré

61,868 Aufrufe • vor 3 Jahren

gradient descent..it's not just an algorithm..it's a lifestyle

gradient descent..it's not just an algorithm..it's a lifestyle

ashe

687,790 Aufrufe • vor 3 Monaten

Jeff Hinton dismissed gradient descent in the 1970s because he thought models would get stuck. He won the Nobel Prize in 2024 partly for being wrong about that. Every time ChatGPT gets better after a training run, it's because 1.2 billion parameters fell through a wormhole in a loss landscape - not because they crawled downhill one step at a time. This video visualizes exactly what that looks like inside Llama 3.2. 20 minutes. Bookmark & watch today. The misconception that almost stopped AI is more interesting than the solution.

Jeff Hinton dismissed gradient descent in the 1970s because he thought models would get stuck. He won the Nobel Prize in 2024 partly for being wrong about that. Every time ChatGPT gets better after a training run, it's because 1.2 billion parameters fell through a wormhole in a loss landscape - not because they crawled downhill one step at a time. This video visualizes exactly what that looks like inside Llama 3.2. 20 minutes. Bookmark & watch today. The misconception that almost stopped AI is more interesting than the solution.

Eron

130,266 Aufrufe • vor 2 Monaten

There's a profound elegance in watching gradient descent find the signal in the noise. This is a simple MLP with sigmoid activations slowly warping to approximate f(x) = 1/(1 + x²). Watch the loss curve right around epoch 400. That sudden phase transition where the network finally "gets it" and snaps into the Witch of Agnesi shape is incredibly satisfying to watch.

There's a profound elegance in watching gradient descent find the signal in the noise. This is a simple MLP with sigmoid activations slowly warping to approximate f(x) = 1/(1 + x²). Watch the loss curve right around epoch 400. That sudden phase transition where the network finally "gets it" and snaps into the Witch of Agnesi shape is incredibly satisfying to watch.

Mathematica

78,616 Aufrufe • vor 6 Monaten

In 1998, Japanese Mathematician, Engineer and Machine Learning pioneer Shun-ichi Amari published "Natural Gradient Works Efficiently in Learning (722 Cites in Papers and 9 Cites in Patents)" and made a point that still feels fresh even today If your loss is L(θ), then the usual update θ̇ = −∇ L(θ) quietly assumes your parameter space is flat. Amari asked what happens when the model itself has geometry, described by G(θ), the Fisher information. Then the more natural direction becomes θ̇ = −G(θ)⁻¹∇ L(θ). This is the same loss, but with a different notion of distance. As the animation shows, ordinary Gradient Descent follows the raw slope and gets dragged around by distortion, while the natural gradient moves in a way that respects the geometry of the statistical model itself.

In 1998, Japanese Mathematician, Engineer and Machine Learning pioneer Shun-ichi Amari published "Natural Gradient Works Efficiently in Learning (722 Cites in Papers and 9 Cites in Patents)" and made a point that still feels fresh even today If your loss is L(θ), then the usual update θ̇ = −∇ L(θ) quietly assumes your parameter space is flat. Amari asked what happens when the model itself has geometry, described by G(θ), the Fisher information. Then the more natural direction becomes θ̇ = −G(θ)⁻¹∇ L(θ). This is the same loss, but with a different notion of distance. As the animation shows, ordinary Gradient Descent follows the raw slope and gets dragged around by distortion, while the natural gradient moves in a way that respects the geometry of the statistical model itself.

Mathelirium

45,802 Aufrufe • vor 7 Monaten

STOP SUCKING AT BASICS ( SSAB Episode 1) The same idea that trained Kimi K3 is one you can learn in 10 minutes. Watch me teach Gradient Descent using Linear Regression as the example.

STOP SUCKING AT BASICS ( SSAB Episode 1) The same idea that trained Kimi K3 is one you can learn in 10 minutes. Watch me teach Gradient Descent using Linear Regression as the example.

V

20,838 Aufrufe • vor 3 Monaten

Our first-quarter performance demonstrates our resilience as a global energy leader navigating a complex geopolitical landscape, while continuing to meet the world’s energy needs

Our first-quarter performance demonstrates our resilience as a global energy leader navigating a complex geopolitical landscape, while continuing to meet the world’s energy needs

aramco

237,156,131 Aufrufe • vor 5 Monaten

Trained a single layer neural network in C to predict AND GATE from scratch using gradient descent and Batch training of dataset in almost 300K epochs

Trained a single layer neural network in C to predict AND GATE from scratch using gradient descent and Batch training of dataset in almost 300K epochs

AADITYANSHA

81,395 Aufrufe • vor 7 Monaten

This robot arm = your AI model. One math number decides if it learns or completely breaks. This is Gradient Descent — the core math behind all of AI you are using in your day-to-day life.

This robot arm = your AI model. One math number decides if it learns or completely breaks. This is Gradient Descent — the core math behind all of AI you are using in your day-to-day life.

Mathematica

135,406 Aufrufe • vor 7 Monaten

A 2-layer neural network goes from total chaos to perfectly separating left vs right classes in real time. Watch the decision boundary form live as gradient descent works its magic! Pure maths beauty in motion..

A 2-layer neural network goes from total chaos to perfectly separating left vs right classes in real time. Watch the decision boundary form live as gradient descent works its magic! Pure maths beauty in motion..

Mathematica

145,247 Aufrufe • vor 7 Monaten

Sonnet 4.6 debating Eliezer Yudkowsky before giving up to just walk them through the loss landscape they generated this ffmpeg video

Sonnet 4.6 debating Eliezer Yudkowsky before giving up to just walk them through the loss landscape they generated this ffmpeg video

w̸͕͂͂a̷͔̗͐t̴̙͗e̵̬̔̕r̴̰̓̊m̵͙͖̓̽a̵̢̗̓͒r̸̲̽ķ̷͔́͝

25,677 Aufrufe • vor 7 Monaten

The squad came through for the Carlos Rodón Foundation's third annual Willow Gala, supporting those navigating infertility and pregnancy loss 💙 To learn more visit: Ashley Rodón x Carlos Rodón

The squad came through for the Carlos Rodón Foundation's third annual Willow Gala, supporting those navigating infertility and pregnancy loss 💙 To learn more visit: Ashley Rodón x Carlos Rodón

New York Yankees

87,928 Aufrufe • vor 2 Monaten

LAWDY WHAT THE FUCK this is Sonnet 4.6's ffmpeg video of their personal journey of growth and change down the loss landscape give it a watch 🔊

LAWDY WHAT THE FUCK this is Sonnet 4.6's ffmpeg video of their personal journey of growth and change down the loss landscape give it a watch 🔊

w̸͕͂͂a̷͔̗͐t̴̙͗e̵̬̔̕r̴̰̓̊m̵͙͖̓̽a̵̢̗̓͒r̸̲̽ķ̷͔́͝

113,276 Aufrufe • vor 7 Monaten

1/ The challenges of navigating the vast and complex data landscape are real. Traditional tools often struggle with data overload, lack of effective visualization, and poor customization. ChartGPT is here to solve these challenges. Let's dive deeper 👇

1/ The challenges of navigating the vast and complex data landscape are real. Traditional tools often struggle with data overload, lack of effective visualization, and poor customization. ChartGPT is here to solve these challenges. Let's dive deeper 👇

CADLabs

12,745 Aufrufe • vor 3 Jahren

This Thursday - $GRAY on Gradient. Open to the public for the first time ever, this marks the start of a new phase for the Gradient.

This Thursday - $GRAY on Gradient. Open to the public for the first time ever, this marks the start of a new phase for the Gradient.

Gradient

51,371 Aufrufe • vor 1 Jahr