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Google just released a free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 🤖⚙️
10% → 17:44 - build your first agent 🚀
30% → 39:30 - Loop engineering: iterate, check, break 🔁
60% → 1:12:38 - Graph engineering 🕸️
75% → 1:34:26 - agents that throttle themselves ⚡
100% → 1:55:05 - full graph for multi-agentic systems 🏗️
everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph.
watch the course, build the graph - then read the full architecture below ↓
More: https://telegra.ph/Graph-Engineering-build-1000-agent-loops-in-one-window-from-one-prompt-full-5-step-course-08-02
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10% → 17:44 - build your first agent 🚀
30% → 39:30 - Loop engineering: iterate, check, break 🔁
60% → 1:12:38 - Graph engineering 🕸️
75% → 1:34:26 - agents that throttle themselves ⚡
100% → 1:55:05 - full graph for multi-agentic systems 🏗️
everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph.
watch the course, build the graph - then read the full architecture below ↓
More: https://telegra.ph/Graph-Engineering-build-1000-agent-loops-in-one-window-from-one-prompt-full-5-step-course-08-02
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Forwarded from Machine Learning
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Attention Heatmap vs Token Pruning 🔍✂️
🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms
#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM
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Generative Adversarial Network (GAN) by hand ✍️ ~ 9 steps walkthrough below
The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago.
The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images.
The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better.
One runs upside down, the other right way up.
I drew and calculated one entirely by hand.
Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself.
= 1. Given =
Four noise vectors in 2D, and four real data vectors in 4D.
= 2. Generator, first layer =
Let us multiply the noise by weights and biases to get new features.
= 3. ReLU =
We apply the activation, and -1 and -2 are crossed out and set to 0.
= 4. Generator, second layer =
Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise.
= 5. Discriminator, first layer =
We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers.
= 6. Discriminator, second layer =
Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out.
= 7. Sigmoid =
We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake.
= 8. Training the Discriminator =
Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page.
= 9. Training the Generator =
We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes.
The outputs:
Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1]
Predictions on fakes = [.7, .5, .9, .3]
Predictions on real = [.7, .9, .9, 1]
Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0]
Generator gradients = [-.3, -.5, -.1, -.7]
The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones.
The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago.
The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images.
The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better.
One runs upside down, the other right way up.
I drew and calculated one entirely by hand.
Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself.
= 1. Given =
Four noise vectors in 2D, and four real data vectors in 4D.
= 2. Generator, first layer =
Let us multiply the noise by weights and biases to get new features.
= 3. ReLU =
We apply the activation, and -1 and -2 are crossed out and set to 0.
= 4. Generator, second layer =
Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise.
= 5. Discriminator, first layer =
We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers.
= 6. Discriminator, second layer =
Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out.
= 7. Sigmoid =
We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake.
= 8. Training the Discriminator =
Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page.
= 9. Training the Generator =
We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes.
The outputs:
Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1]
Predictions on fakes = [.7, .5, .9, .3]
Predictions on real = [.7, .9, .9, 1]
Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0]
Generator gradients = [-.3, -.5, -.1, -.7]
The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones.
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3. https://deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ — Multi AI Agent Systems with CrewAI 🤖
4. https://microsoft.github.io/AI-For-Beginners/agentic-ai/ — AI Agents for Beginners 🚀
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Forwarded from Machine Learning
"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮
The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈
A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨
https://arxiv.org/pdf/2409.02668
#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks
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The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈
A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠
The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊
In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨
https://arxiv.org/pdf/2409.02668
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🔖 A comprehensive collection of resources on MLOps
We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation.
This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems.
⛓ Link to GitHub
https://github.com/visenger/awesome-mlops
We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation.
This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems.
⛓ Link to GitHub
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