Tag Collection

#model

Explore Books, Authors and Common Highlights on Model

Showing 56 of 56 highlights

Regularization is a technique used to prevent overfitting by adding a penalty on the size of coefficients.
The most important part of building a machine learning model is to understand the problem you are trying to solve.
The process of modeling can reveal hidden patterns.

From The Model Thinker by Scott E. Page

Every causal model can be viewed as a set of questions waiting to be answered.

From The Book of Why by Judea Pearl

A model is a simplified representation of the world.

From The Model Thinker by Scott E. Page

Start small, learn quickly, and iterate until you find a sustainable business model.

From The Lean Startup by Eric Ries

Transfer learning allows a model trained on one task to be adapted to a different but related task.
Every thought and action is a reflection of the models we create.

From A Thousand Brains by Jeff Hawkins

Our brains are designed to create models of the world.

From A Thousand Brains by Jeff Hawkins

I want to be a role model for young women.

From Becoming by Michelle Obama

Hyperparameter tuning can significantly affect the performance of your model.
The best predictions come from combining different models.

From The Model Thinker by Scott E. Page

To innovate, you must first identify the existing mental models.
Good models capture the essence of reality.

From The Model Thinker by Scott E. Page

Wonder Woman was designed to be a role model for girls.
Every time we learn something new, we are building a model of the world.

From The Master Algorithm by Pedro Domingos

Feature selection is crucial in building effective machine learning models.
We can be the leaders we wish we had.

From Wolfpack by Abby Wambach

Overfitting occurs when a model learns the training data too well, including noise and outliers, leading to poor generalization.
Understanding the limits of our models helps us improve them.

From The Model Thinker by Scott E. Page

Models help us navigate complex systems.

From The Model Thinker by Scott E. Page

It is essential to interrogate the data and the models that shape our decisions.
Mental models are frameworks that simplify complex realities.
Regularization techniques are essential to prevent overfitting and improve the model's performance on unseen data.
Data is not enough; we need a model that tells us how the world works.

From The Book of Why by Judea Pearl

These models can have profound effects on people's lives, often with little accountability.
The more models we have, the better our insights.

From The Model Thinker by Scott E. Page

The key to deep learning is representation learning, where the model learns to represent the input data in a way that makes it easier to solve the task at hand.
Collaborative modeling enhances our understanding of complex systems.

From The Model Thinker by Scott E. Page

Evaluation metrics guide the selection of the best model.
Business models will need to adapt to the capabilities of prediction machines.
Mental models are frameworks that help us understand the world.
A good model is one that generalizes well to unseen data.
The Turing machine is a model of computation.
Models are tools for thinking and decision-making.

From The Model Thinker by Scott E. Page

Overfitting occurs when a model learns the training data too well, including its noise.
Deep learning models are often seen as black boxes because it can be difficult to interpret how they make decisions.
The only way to understand the world is to build a model of it.
Our models of the world should reflect causal relationships.

From The Book of Why by Judea Pearl

Effective decision-making relies on the right combination of models.

From The Model Thinker by Scott E. Page

Models are essential tools for decision-making.

From The Model Thinker by Scott E. Page

Mathematical models can perpetuate inequality and harm the most vulnerable.
Overfitting occurs when a model learns the noise in the training data.
Overfitting occurs when a model learns the noise in the training data instead of the underlying distribution.
Collaboration enhances the effectiveness of models.

From The Model Thinker by Scott E. Page

Understanding the predictive capabilities of AI can unlock new business models.
Our mental models shape the way we interpret the world around us.
Paradigms are the universally recognized scientific achievements that for a time provide model problems and solutions to a community of practitioners.
You don’t need to scale to succeed.

From Company of One by Paul Jarvis

Regularization techniques help prevent overfitting.
Each model offers a unique perspective on reality.

From The Model Thinker by Scott E. Page

We need to rethink our economic and social models to adapt to these changes.