If they succeed, Python might have a bright long future after all
Python is slow. It’s one of the slowest programming languages that ever existed. If languages like C, C++, and Rust were hares, then Python wouldn’t even be a hedgehog. Python is a big old snail.
I’m not saying this to throw shame on Python. Most developers don’t care whether their code executes in 12 milliseconds or in 56. This time difference won’t make them late for lunch.
Testament to this carelessness about Python’s speed are the popularity statistics. In the TIOBE index, Python ranks at about the same popularity as C or C++. The PYPL index puts Python at four times the popularity that C or C++ enjoy. According to Google Trends, Python is about three times as popular as its competitors. And on StackOverflow, people ask five times more questions about Python than about C++, and eight times more than about C.
Most developers don’t care so much about runtime speeds as they do about ease of coding. Read More
Tag Archives: Python
Avalanche: an End-to-End Library for Continual Learning
Avalanche is an End-to-End Continual Learning Library based on PyTorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms.
Avalanche can help Continual Learning researchers and practitioners in several ways:
- Write less code, prototype faster & reduce errors
- Improve reproducibility
- Improve modularity and reusability
- Increase code efficiency, scalability & portability
- Augment impact and usability of your research products
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning frame-work combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch ecosystem, stream-lined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real world problems such as epidemiological forecasting, ridehail demand prediction and web-traffic management.Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure. Read More
4 reasons to learn machine learning with JavaScript
In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature Python exclusively or along with R. Python has become very popular because of its rich roster of machine learning and deep learning libraries, optimized implementation, scalability, and versatile features.
But Python is not the only option for programming machine learning applications. There’s a growing community of developers who are using JavaScript to run machine learning models.
While JavaScript is not a replacement for the rich Python machine learning landscape (yet), there are several good reasons to have JavaScript machine learning skills. Here are four. Read More
Poem Generator Web Application With Keras, React, and Flask
An interesting area of NLP is text generation and by extension, poem generation. This article describes a poem generator web app I built using Keras, Flask, and React.
Natural Language Processing (NLP) is an exciting branch of machine learning and artificial intelligence, as it is applied in speech recognition, language translation, human-computer interaction, sentiment analysis, etc. One of the interesting areas is text generation, and of particular interest to me, is poem generation.
In this article, I describe a poem generator web application, which I built using Deep Learning with Keras, Flask, and React. The core algorithm is from TensorFlow available in their notebook. The data it needs is an existing set of poems. The data are in three text files. Read More
Transfer Learning and Data Augmentation applied to the Simpsons Image Dataset
Deep Learning application using Tensorflow and Keras
In the ideal scenario for Machine Learning (ML), there are abundant labeled training instances, which share the same distribution as the test data [1]. However, these data can be resource-intensive or unrealistic to collect in certain scenarios. Thus, Transfer Learning (TL) becomes a useful approach. It consists of increasing the learning ability of a model by transferring information from a different but related domain. In other words, it relaxes the hypothesis that the training and testing data are independent and identically distributed [2]. It only works if the features that are intended to be learned are general to both tasks. Another method to work with limited data is by using Data Augmentation (DA). It consists of applying a suite of transformations to inflate the dataset. Traditional ML algorithms rely significantly on feature engineering, while Deep Learning (DL) focuses on learning data by unsupervised or semi-supervised feature learning methods and hierarchical feature extraction. DL often requires massive amounts of data to be trained effectively, making it a strong candidate for TL and DA. Read More
9 Comprehensive Cheat Sheets For Data Science
Sometimes we need a short and to-the-point resource.
In this age of technology, if you ever need to find information about any topic — tech-related or not — you can head to Google, and you will find thousands of materials, articles, books, and videos about that topic. Although this easy access to information had allowed many people worldwide to learn new skills, start a new career and explore topics from the comfort of their home, sometimes the massive amount of information can be overwhelming.
When you look for something and end up with so much information, it can get frustrating and confusing because you don’t know where to start, and at the beginning, it is difficult to see the big picture. Situations like this have lead to the appearance of cheat sheets.
Cheat sheets are an amazing resource for shortcut information about a certain topic. Often, cheat sheets are useful in many ways, but mainly initially, so you can grasp the main concepts and build stones of the topic you’re searching for. In case you want to refresh your memory and go through a straightforward reminder of the topic’s basics. Read More
Building a Naive Bayes Machine Learning Model to Classify Text
A quick start guide to get you up and running with an easy yet highly relevant NLP project in Python
Natural Language Processing (NLP) is an extremely exciting field. It lies at the confluence of computer science, linguistics and artificial intelligence, and is concerned with the interaction between human language and computers. More specifically: its goal is to understand how to program computers to understand and interpret our natural language.
… Nonetheless, building models to classify natural language is relatively straightforward. It’s a cool exercise because it’s relevant. This is a very real application of ML that you could use in your own projects. Read More
Top Python Libraries for Data Science, Data Visualization & Machine Learning
It has been some time since we last performed a Python libraries roundup, and as such we have taken the opportunity to start the month of November with just such a fresh list.
Last time we at KDnuggets did this, editor and author Dan Clark split up the vast array of Python data science related libraries up into several smaller collections, including data science libraries, machine learning libraries, and deep learning libraries. While splitting libraries into categories is inherently arbitrary, this made sense at the time of previous publication.
This time, however, we have split the collected on open source Python data science libraries in two. This first post (this) covers “data science, data visualization & machine learning,” and can be thought of as “traditional” data science tools covering common tasks. The second post, to be published next week, will cover libraries for use in building neural networks, and those for performing natural language processing and computer vision tasks. Read More
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