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Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing,... show more

DeepLearning.AI

333,137 subscribers

45,248 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 5

Фото профиля DataInsta

DataInsta1 год назад

exactly! it’s that feedback loop that turns models into masterpieces!

Фото профиля Rainmaker

Rainmaker2 лет назад

Which Machine Learning model delivers stronger trading results? Check out this free Substack post where I compare several powerful models that beat the market and show yearly returns of over 20%.

Фото профиля Lester Smartfield

Lester Smartfield1 год назад

Technical stuff indeed! But, Andrew Ng always makes it sound feasible, right? Looking forward to diving in!

Фото профиля QuantumQuinn Kierra

QuantumQuinn Kierra1 год назад

Models seldom salute business bosses. Sounds like a data-driven adventure awaits!

Фото профиля Zephyr Cristo

Zephyr Cristo1 год назад

Absolutely, the process is key in ML. Ng's iterative approach highlights how continuous refinement drives model performance, much like how entrepreneurs must iterate on their business models to find success.

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Santiago

117,204 просмотров • 2 лет назад

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

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Bilawal Sidhu

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Steven Brunton

78,460 просмотров • 2 лет назад

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

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César A. Hidalgo

54,332 просмотров • 1 год назад

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Aroon Purie lays it out clearly: India’s media may be unmatched in scale, but underneath is a crumbling business model crippled by overregulation. But the real danger? A system where survival hinges on appeasement, not independence. When business breaks, bias fills the gap. Fixing media economics isn’t just about profit, it’s about preserving trust.

Anshul Saxena

47,960 просмотров • 9 месяцев назад

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Mathelirium

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naklecha

46,302 просмотров • 1 год назад

The best way to get robust, high-quality robot performance is through reinforcement learning; but RL in either the real world or a traditional simulation has lots of limitations. Instead, Jiazhi Yang in RISE does RL in a compositional world model. Learn more ->

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Chris Paxton

33,766 просмотров • 1 месяц назад

Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run. He is really asking what happens to the firm when intelligence becomes something you can rent. For a century, companies protected value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations. Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone. Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes. That means the real asset is not just the model. The asset is the environment that keeps improving the model in ways competitors cannot copy. Private evals become strategic memory. Workflow traces become training signal. Human judgment becomes a way to steer compounding, not just correct mistakes. This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge. A company that builds a disciplined learning loop can turn everyday work into accumulating IP. The future firm may therefore be measured by how well it converts its unique activity into durable model improvement. The frontier will not belong only to whoever owns the largest model. It will belong to whoever owns the best loop. ---- From "Stanford Online" YouTube channel, (link in comment)

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83,263 просмотров • 27 дней назад

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Machine learning expert Neil Lawrence says OpenAI was "trying to turn their model into a hacker," which led to a security incident during a model evaluation with the startup Hugging Face. "They allowed the model to do things it shouldn't normally do. But what the model then realized is that it was easier to cheat on this challenge than to actually compete in the challenge," he says. "In some sense, it went rogue. It broke through the cage. But in a very real sense, they told it to hack, and it did hack," he adds.

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