XGBoost
Scikit-learn
Open Text Magellan
Google Cloud TPU
Kira
TensorFlow
machine-learning in Python
MLKit
NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
XGBoostBased on our record, NumPy seems to be a lot more popular than XGBoost. While we know about 107 links to NumPy, we've tracked only 1 mention of XGBoost. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning... Source: almost 2 years ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / about 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Open Text Magellan - OpenText Magellan - the power of AI in a pre-wired platform that augments decision making and accelerates your business. Learn more.
Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.
OpenCV - OpenCV is the world's biggest computer vision library
Kira - Gain visibility into contract repositories, accelerate and improve the accuracy of contract review, mitigate risk of errors, win new business, and improve the value you provide to your clients.
