Gradient Descent is one of the most popular methods to pick the model that best fits the training data. Typically, that’s the model that minimizes the loss function, for example, minimizing the Residual Sum of Squares in Linear Regression.
Stochastic Gradient Descent is a stochastic, as in probabilistic, spin on Gradient Descent. It improves on the limitations of Gradient Descent and performs much better in large-scale datasets. That’s why it is widely used as the optimization algorithm in large-scale, online machine learning methods like Deep Learning.
But to get a better understanding about Stochastic Gradient Descent, you need to start…
Several phenomena in the real world can be represented as counts of things. For example, the number of flights departing from an airport, number customers lining up at the store register, the number of earthquakes occurring in a year at a specific region.
Counting events is a relatively simple task, but if you want to go from just counting the occurrence of events to asking questions about how likely are these events to happen in a specific unit of time, you need more powerful tools like the Poisson distribution.
The Poisson distribution models the probability that a given number of…
Logistic regression is a machine learning classification model with quite a confusing name!
The name makes you think about Linear Regression, but it’s not used to predict an unbounded, continuous outcome. Instead, it is a statistical classification model, it gives you the likelihood that an observation belongs to a specific class.
Logistic regression is used across many scientific fields. In Natural Language Processing (NLP), it’s used to determine the sentiment of movie reviews, while in Medicine it can be used to determine the probability of a patient developing a particular disease.
Lately you’ve been interested in gauging your productivity. Not…
When you’re building a machine learning model you’re faced with the bias-variance tradeoff, where you have to find the balance between having a model that:
A model that is very expressive has a low bias, but it can also be too complex. While a model that generates predictions that aren’t too far off from the true value has low variance.
When the model is too complex and tries to encode more patterns from the training data than…
Andrei Markov didn’t agree with Pavel Nebrasov, when he said independence between variables was necessary for the Weak Law of Large Numbers to be applied.
The Weak Law of Large Numbers states something like this:
When you collect independent samples, as the number of samples gets bigger, the mean of those samples converges to the true mean of the population.
But Markov believed independence was not a necessary condition for the mean to converge. So he set out to define how the average of the outcomes from a process involving dependent random variables could converge over time.
Thanks to this…
Ever since I can remember, I’ve been asked long-game questions without realizing it. I couldn’t identify them as long-game questions, because no one teaches you how to develop a long-game mindset. It’s not something you learn in school, discuss at the dinner table or at a barbecue with friends.
The long-game mindset is a decision-making approach that focuses on the long-term outcomes and impact of your decisions.
In many points in life, you’re faced with decisions that go beyond short-term impact. In these situations, you can choose to cut some corners and make a quick decision without thinking much about…
SQL is a fundamental part of a Data Scientist’s toolbox. It’s a great tool to explore and prepare your data, either for analysis or to create a machine learning model.
An effective approach to learn SQL is to focus on the questions you want to answer, rather than on specific methods or functions. Once you know what you’re looking for, what questions you want to answer with data, the functions and operands you use to get there will make more sense.
This article is organized around what questions to ask about data, and you’ll become familiar with:
This year was challenging, stressful, messed up, overwhelming, brutal, … for everyone.
Lots of us found comfort in books, which might seem like a small thing, but it’s an immense privilege.
This year my readings gravitated around:
I love talking about books, and discover lots of new books through these kinds of lists. So I hope you enjoy this article and that you can find a book that sparks your interest on something new.
Happy reading 📚
Monte Carlo Methods is a group of algorithms that simulate the behavior of a complex system, or probabilistic phenomena, using inferential statistics. They simulate physical processes that are typically time-consuming, or too expensive to setup and run for a large number times.
Since it is a tool to model probabilistic real-world processes, Monte Carlo Methods are widely used in areas ranging from particle Physics and Biochemistry to Engineering. So, if you can model it, you can use Monte Carlo Methods and run simulations!
Monte Carlo simulations are great methodology when you want to:
The Central Limit Theorem (CLT) is one of the most popular theorems in statistics and it’s very useful in real world problems. In this article we’ll see why the Central Limit Theorem is so useful and how to apply it.
In a lot of situations where you use statistics, the ultimate goal is to identify the characteristics of a population.
Central Limit Theorem is an approximation you can use when the population you’re studying is so big, it would take a long time to gather data about each individual that’s part of it.
Population is the group of individuals that…
In-depth articles about Data Science and Machine Learning | Bookworm | @carolinabento