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on the atlas · 46
- Kaggle Solutions2 savers
- Active Prompting - Anote1 savers
- Prompt Engineering Guide | Prompt Engineering Guide1 savers
- Machine Learning Archive1 savers
- Guess and Check method, what is it? | Practicle1 savers
- 2402.079271 savers
- https://arxiv.org/pdf/1811.128081 savers
- How To Learn Anything With Ultra-Learning1 savers
- Dashboard1 savers
- styleguide | Style guides for Google-originated open-source projects2 savers
- How to Read a Book: The Ultimate Guide by Mortimer Adler - Farnam Street7 savers
- How to systematically improve your writing by Benjamin Franklin : r/writing1 savers
- 18 Life-Learnings from 18 Years of The Marginalian – The Marginalian3 savers
- Linear regression: Gradient descent | Machine Learning | Google for Developers1 savers
- Loss Functions in Machine Learning Explained | DataCamp1 savers
- What is the difference between a loss function and an error function? - Cross Validated1 savers
- Regularization (mathematics) - Wikipedia1 savers
- https://arxiv.org/pdf/2006.112392 savers
- Teaching - Gautam Kunapuli1 savers
- ensemble-methods-notebooks/Ch1.3-fit-vs-complexity.ipynb at master · gkunapuli/ensemble-methods-notebooks · GitHub1 savers
- Binary Prediction of Poisonous Mushrooms | Kaggle1 savers
- Neural networks and deep learning2 savers
- Host your Streamlit app for free1 savers
- Fast-Forward to Boredom: How Switching Behavior on Digital Media Makes People More Bored1 savers
- Study: You Should Watch this Video to the End - YouTube1 savers
- Matemática: como é o ensino nos países que se saem melhor no Pisa - YouTube1 savers
- Harvard CS50 (2023) – Full Computer Science University Course - YouTube1 savers
- If you're ambitious but lazy, please watch this video… - YouTube1 savers
- Hank Weisinger - Ozan Varol | Famous Failures Podcast1 savers
- Advice for Early Career — Celine Halioua39 savers
- Flatten() and Ravel() Numpy Functions and their Differences | Module NumPy Tutorial - Part 23 - YouTube1 savers
- My dream died, and now I'm here - YouTube2 savers
- Learning By Writing43 savers
- Sam Altman30 savers
- Michael Levin: The electrical blueprints that orchestrate life | TED - YouTube27 savers
- Lil'Log24 savers
- Excalidraw | Hand-drawn look & feel • Collaborative • Secure21 savers
- Practical Deep Learning for Coders | Practical Deep Learning for Coders18 savers
- Yes you should understand backprop | by Andrej Karpathy | Medium14 savers
- Why I Write | The Orwell Foundation14 savers
- Minimal-trust investigations9 savers
- A Recipe for Training Neural Networks7 savers
- benneo7 savers
- The Illustrated Transformer6 savers
- How to Build Anything Extremely Quickly - Learn How To Learn5 savers
- The ROI on Generative AI - by Tanay Jaipuria3 savers
highlights · 45
Think of these levels as reading to entertain, reading to inform, reading to understand, and reading to master.
How to Read a Book: The Ultimate Guide by Mortimer Adler - Farnam Streetnot your parents or your idols, not the philosophers or the poets, not your liberal arts education or your twelve-step program, not church or therapy or Tolstoy.
18 Life-Learnings from 18 Years of The Marginalian – The MarginalianExplicit regularization is regularization whenever one explicitly adds a term to the optimization problem. These terms could be priors, penalties, or constraints. Explicit regularization is commonly employed with ill-posed optimization problems. The regularization term, or penalty, imposes a cost on the optimization function to make the optimal solution unique.
Regularization (mathematics) - WikipediaAs mentioned earlier, the loss function, also known as the error function, quantifies how well a single prediction of the machine learning algorithm is compared to the actual target value. The key takeaway is that a loss function applies to a single training example and is part of the overall model's learning process that provides the signal by which the model's learning algorithm updates the weights and parameters. The cost function, sometimes called the objective function, is an average of the loss function of an entire training set containing several training examples. The cost function qua…
Loss Functions in Machine Learning Explained | DataCampGradient descent is a mathematical technique that iteratively finds the weights and bias that produce the model with the lowest loss. Gradient descent finds the best weight and bias by repeating the following process for a number of user-defined iterations.
Linear regression: Gradient descent | Machine Learning | Google for DevelopersThe resulting value, the loss, reflects the accuracy of the model's predictions. During training, a learning algorithm such as the backpropagation algorithm uses the gradient of the loss function with respect to the model's parameters to adjust these parameters and minimize the loss, effectively improving the model's performance on the dataset.
Loss Functions in Machine Learning Explained | DataCampIn the context of a predictive or inferential model, the term "error" generally refers to the deviation from an actual value by a prediction or expectation of that value. It is determined entirely by the prediction mechanism and the actual behaviour of the quantities under observation. The "loss" is a quantified measure of how bad it is to get an error of a particular size/direction, which is affected by the negative consequences that accrue for inaccurate prediction.
What is the difference between a loss function and an error function? - Cross ValidatedIn mathematics, statistics, finance,[1] and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer of a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting.[2]
Regularization (mathematics) - WikipediaThe goal of backpropagation is to compute the partial derivatives ∂C/∂w ∂ � / ∂ � and ∂C/∂b ∂ � / ∂ � of the cost function C � with respect to any weight w � or bias b � in the network.
Neural networks and deep learningThis expression gives us a much more global way of thinking about how the activations in one layer relate to activations in the previous layer: we just apply the weight matrix to the activations, then add the bias vector, and finally apply the σ � function
Neural networks and deep learningThe expression tells us how quickly the cost changes when we change the weights and biases.
Neural networks and deep learningAt the heart of backpropagation is an expression for the partial derivative
Neural networks and deep learningYou can however build unique context at the intersection of fields that rarely touch. Build and own your own niche.
Advice for Early Career — Celine Haliouawithout ads or anything like that.
GPT-4o - Sam AltmanNever let a formula just become a formula.
On Learning Deeply. Learning deeply, and to be able to… | by adam dhalla | MediumSomething I find that helps me is to write all my notes as if I am explaining the subject to someone else,
On Learning Deeply. Learning deeply, and to be able to… | by adam dhalla | Mediumlearn about why the subject matters, what prerequisites it requires, and make sure understanding the subject serves your pre-defined goal.
On Learning Deeply. Learning deeply, and to be able to… | by adam dhalla | Mediummake sure you understand the prerequisites.
On Learning Deeply. Learning deeply, and to be able to… | by adam dhalla | MediumMy current and ongoing goal is to understand, or at least comprehend, the mathematics behind machine learning concepts such as gradient descent and neural network backpropagation.
On Learning Deeply. Learning deeply, and to be able to… | by adam dhalla | MediumYou can’t understand everything, and that’s a critical thing to come to terms with.
On Learning Deeply. Learning deeply, and to be able to… | by adam dhalla | MediumComplete the following steps for this exercise:
Unsupervised Feature Learning and Deep Learning TutorialWe now want to find the choice of θ � that minimizes J(θ) � ( � ) as given above
Unsupervised Feature Learning and Deep Learning TutorialThis may also be called a “loss”, “penalty” or “objective” function.
Unsupervised Feature Learning and Deep Learning TutorialOur AI assistant now performs the work of 700 employees
The ROI on Generative AI - by Tanay Jaipuria“The problem isn't a lack of money, food, water or land. The problem is that you've given control of these things to a group of greedy psychopaths who care more about maintaining their own power than helping mankind."
Poetic Outlaws on Substack: "“The problem isn't a lack of money, food, water or land. The problem is that you've given control of these things to a group of greedy psychopaths who care more about maintaining their own power than helping mankind." —Bill Hicks"if you knew the end from the beginning, would you do things differently?
benneoA master in the art of living draws no sharp distinction between his work and his play
benneoUma rede neural sem função de ativação é essencialmente apenas um modelo de regressão linear.
Capítulo 8 - Função de Ativação - Deep Learning BookEm uma rede neural, atualizaríamos os pesos e bias dos neurônios com base no erro. Este processo é conhecido como backpropagation
Capítulo 8 - Função de Ativação - Deep Learning Bookm uma rede neural, atualizaríamos os pesos e bias dos neurônios com base no erro. Este processo é conhecido como backpropagation.
Capítulo 8 - Função de Ativação - Deep Learning BookEm uma rede neural, atualizaríamos os pesos e bias dos neurônios com base no erro. Este processo é conhecido como backpropagation
Capítulo 8 - Função de Ativação - Deep Learning BookThe qualities that in my experience correlate most strongly to success in deep learning are patience and attention to detail.
A Recipe for Training Neural Networkscan be computed by the chain rule; but doing this separately for each weight is inefficient
Backpropagation"What valuable company is nobody building?"
Zero to One"Love is an incurable disease." In love, there is permanent suffering". Those who love and those who are happy are not the same"
How Proust Can Change Your LifeSave your career by learning how to learn
Master Anything With The 9 Principles Of Ultra-LearningRetrieval-Augmented Generation (RAG) is a technique that enhances language model generation by incorporating external knowledge
Aman's AI Journal • NLP • Retrieval Augmented GenerationTime series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal
[2408.14484] Agentic Retrieval-Augmented Generation for Time Series Analysisis the central approach used in transformer-based models and foundation models like BERT, GPT-2/3/4, RoBERTa, and others.
Understanding Large Language Models - by Sebastian RaschkaThe 9 elements that make something enjoyable:
Creativity: The Psychology of Discovery and InventionWhenever you read new information, ask yourself “How does this fit into my picture of the universe?” and if it doesn’t, ask “Why doesn’t it fit?”
Creativity: The Psychology of Discovery and InventionCreative people are those that tend to learn quite a few, and would be learning them even if they weren’t paid to do so.
Creativity: The Psychology of Discovery and InventionDefinition: responds well to external expectations, but not internal expectations.
#61 Time to understand how you workDefinition: responds well to internal expectations, but not external expectations.
#61 Time to understand how you workPlanning is a fundamental of doing, and outlining is a fundamental of planning.
How to Build Anything Extremely Quickly - Learn How To Learn