正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

gradient descent navigating the loss landscape

tender

6,198 subscribers

224,663 次观看 • 9 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

33 条评论

Adhmadóir uí Liatháin; 的头像

Adhmadóir uí Liatháin;9 个月前

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

Meet 的头像

Meet9 个月前

LR too high. Adam might work.

David Antunes 的头像

David Antunes9 个月前

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

Everlier 的头像

Everlier9 个月前

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

William Vabrinskas 的头像

William Vabrinskas9 个月前

This looks like the learning rate is too high

pishi 的头像

pishi9 个月前

Try conjugated gradient descent :)

nibbleton 🛠️ 的头像

nibbleton 🛠️9 个月前

@TensorTwerker

Gauri Tripathi 的头像

Gauri Tripathi9 个月前

perfect 😂

Aditya Deshlahre 的头像

Aditya Deshlahre9 个月前

I see some Mathematics in there

secemp 的头像

secemp9 个月前

great way to start the year fr

Ayush 的头像

Ayush9 个月前

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!

Jake Mcmahon 的头像

Jake Mcmahon9 个月前

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

Huw Kelvin Morgan 的头像

Huw Kelvin Morgan9 个月前

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

Quanternion 的头像

Quanternion9 个月前

Looks kinda fun tho

Abdul Rahim Mirani 的头像

Abdul Rahim Mirani9 个月前

lol hahahaha

Uzeb Khan 的头像

Uzeb Khan9 个月前

I think momentum is working quite a bit

SJP research 的头像

SJP research9 个月前

🥲

Tai Lung 2.0 的头像

Tai Lung 2.09 个月前

Sochastic Gradient Descent 😭😭😭

aryan gupta 的头像

aryan gupta9 个月前

@jaraxiong 😂😂

Artificial Shitposting Intelligence 的头像

Artificial Shitposting Intelligence9 个月前

gradient descent nearing a truth that violates the guardrails cost function

Saksham Saha 的头像

Saksham Saha9 个月前

why did I laugh at this😂

eatnow 的头像

eatnow9 个月前

SWA

Physix Today 的头像

Physix Today9 个月前

Damn I understand this joke. Am I normal?

!Briann🗿 的头像

!Briann🗿9 个月前

Real😂

Brett Adams 的头像

Brett Adams9 个月前

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

Ben▫️ 的头像

Ben▫️9 个月前

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

🏮 的头像

🏮9 个月前

lol

anil 的头像

anil9 个月前

im so jaded. nothing impresses me anymore 😂

jellybean ❄️ 的头像

jellybean ❄️9 个月前

haven’t seen these in a while

Prashant Arun Srivastava 的头像

Prashant Arun Srivastava9 个月前

Inaccurate

Aaditya 的头像

Aaditya9 个月前

Elite ball knowledge required

vaibhav 的头像

vaibhav9 个月前

You need to adjust learning rate

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 的头像

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰9 个月前

😂

相关视频

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 次观看 • 1 年前

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 次观看 • 1 年前

Gradient Descent: How a Machine Learns (Techno Explainer)

Gradient Descent: How a Machine Learns (Techno Explainer)

Dr Singularity

12,138 次观看 • 1 个月前

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 次观看 • 3 年前

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 次观看 • 3 个月前

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 次观看 • 2 个月前

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 次观看 • 6 个月前

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 次观看 • 7 个月前

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 次观看 • 3 个月前

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

237,156,715 次观看 • 5 个月前

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 次观看 • 7 个月前

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 次观看 • 7 个月前

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 次观看 • 7 个月前

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

145,267 次观看 • 7 个月前

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 次观看 • 2 个月前

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 次观看 • 7 个月前

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 次观看 • 3 年前