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Python is slow. I bet you might encounter this counterargument many times about using Python, especially from people who come from C or C++ or Java world. This is true in many cases, for instance, looping over or sorting Python arrays, lists, or dictionaries can be sometimes slow. After all, Python is developed to make programming fun and easy. Thus, the improvements of Python code in succinctness
Storytelling is an essential skill for us data scientists. To convey our ideas and be persuasive, we need effective communication. And aesthetic visualisations are a great tool for that. In this post, we’ll cover 5 visualisation techniques beyond the classics that can make your data story more aesthetic and effective. We’ll be using Plotly graphic library in python (also available in R), which pro
IntroductionPandas is an amazing library in the Python ecosystem for data analytics and machine learning. They form the perfect bridge between the data world, where Excel/CSV files and SQL tables live, and the modeling world where Scikit-learn or TensorFlow perform their magic. A data science flow is most often a sequence of steps — datasets must be cleaned, scaled, and validated before they can b
With the recent release of the TensorFlow 2 Object Detection API, it has never been easier to train and deploy custom state of the art object detection models with TensorFlow. To build a custom model you can leverage your own custom dataset to detect your own custom objects: foods, pets, mechanical parts, and more. In this blog and TensorFlow 2 Object Detection Colab Notebook, we walk through how
I like working with textual data. As for Computer Vision, in NLP nowadays there are a lot of ready accessible resources and opensource projects, which we can directly download or consume. Some of them are cool and permit us to speed up and bring to another level our…
Colab on steroids: free GPU instances with SSH access and Visual Studio Code Server A step-by-step guide to SSH into a free GPU instance provided by Google Colab and install Visual Studio Code Server.
This post commemorates the first anniversary of the series where we examine advancements in NLP and Graph ML powered by knowledge graphs! 🎂 1️⃣ The feedback of the audience drives me to continue, so fasten your seatbelts (and maybe brew some ☕️): in this episode, we are looking at the KG-related ACL 2020 proceedings! ACL 2020 went fully virtual this year and I can’t imagine how hard was it for th
Photo by Morning Brew on UnsplashIn my previous post, I have shared my first research results for predicting stock prices which will be subsequently used as input for a deep learning trading bot. While upscaling my datasets to thousands of equity tickers equating to almost 1…
Learn the basics of AI and Deep Learning with TensorFlow and Keras in this Live Training Session hosted by Me. “Wisdom is not a product of schooling but of the lifelong attempt to acquire it.” — Albert Einstein IntroductionThe ability to understand information produced by the individuals at the cutting edge of research within Artificial Intelligence and the Machine learning domain is a skill that
Photo by Chris Ried on Unsplash1. F-StringsF-Strings provide a concise and convenient way to embed Python expressions inside string literals for formatting. First, let’s define two variables name and age that you want to include in our print statement. name = "Pavel" age = 23To not deal with string concatenation or using commas inside the print statement, you can use Python’s improved String forma
I used to have an Apple laptop as my daily driver. I could do almost everything there; development, proposal writing, music composition etc. But the fear of vendor lock-in, the concern that I am depended on Apple’s whims and vices — which are arguably very expensive — led me to seek a new solution. See Part II here:
To productionize a machine learning model, the standard approach is to wrap it in a REST API and deploy it as a microservice. Flask is currently the de facto choice for writing these APIs for a couple of reasons: Flask is minimal. Because inference APIs have historically tended to be simple predict() methods, the complexities introduced by more opinionated frameworks (like Django) have been seen a
Streamlit Is The Game Changing Python Library That We’ve Been Waiting For Developing a user-interface is not easy. I’ve always been a mathematician and for me, coding was a functional tool to solve an equation and to create a model, rather than providing the user with an experience. I’m not artsy and nor am I actually that bothered by it. As a result of this, my projects always remained, well, pro
Although on the surface Python might appear to be a language of simplicity that anyone can learn, and it is, many might be surprised to know just how much mastery one can obtain in the language. Python is one of those things that is rather easy learn, but can be difficult to master. In Python, there are often multiple ways of doing things, but it can be easy to do the wrong thing, or reinvent the
WhyWhen confronting a new data science problem, one of the first questions to ask is which technology to use. There is hype; there are standard tools; there are bleeding-edge technologies, entire platforms and off-the-shelf solutions. Over the last few years, I’ve been building proof of concepts and solutions with any technology I could get my hands on. If there is a new platform, I register for t
Let’s say that we want to find the number of confirmed COVID cases over the past 3 days in various Canadian provinces. There is a BigQuery public dataset with information published by Johns Hopkins, and we can query it as follows: SELECT * FROM `bigquery-public-data`.covid19_jhu_csse.confirmed_cases WHERE country_region LIKE 'Canada'We get: There is a column for every dateYikes! There is a column
This article is about MLflow — an open-source MLOps tool. If you’ve never heard of it, here’s a tutorial. I am focusing on MLflow Tracking —functionality that allows logging and viewing parameters, metrics, and artifacts (files) for each of your model/experiment. When you log the models you experiment with, you can then summarize and analyze your runs within the MLflow UI (and beyond). You can und
SourceThis Post will provide you a detailed end to end guide for using Pytorch for Tabular Data using a realistic example. By the end of this post, you will be able to build your Pytorch Model. A few things before we start:Courses: I started with both fast.ai courses and DeepLearning.ai specialization (Coursera). They gave me the basic knowledge about DeepLearning. The great Stanford cs231n is als
This is an opinion piece. I’d love to hear your counter arguments below. Everyone and their grandmother wants to be a data scientist. But while data science may be the sexiest job of the 21 century, that discounts another rewarding and highly paid profession, the software engineer. I often get messages from new grads and career changers asking me for advice on getting into data science. I tell the
Don’t get me wrong. Python’s popularity is still backed by a rock-solid community of computer scientists, data scientists and AI specialists. But if you’ve ever been at a dinner table with these people, you also know how much they rant about the weaknesses of Python. From being slow to requiring excessive testing, to producing runtime errors despite prior testing — there’s enough to be pissed off
Good news to all data scientists and researchers🎉🎉 Springer has announced to give hundreds of expensive books on Science and technology worth thousands of dollars available for free download during this COVID-19 lockdown. Over more than 500 textbooks are available! If you are interested only in some of these books, you may download them one by one. But this chance won’t happen often, what about
Hundreds of books are now free to download Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field. Among the bo
If you are new to data science, this title is not intended to insult you. It is my second post on the theme of a popular interview question that goes something like: “explain [insert technical topic] to me as though I were a five-year-old.”
We are excited to announce PyCaret, an open source machine learning library in Python to train and deploy supervised and unsupervised machine learning models in a low-code environment. PyCaret allows you to go from preparing data to deploying models within seconds from your choice of notebook environment. In comparison with the other open source machine learning libraries, PyCaret is an alternate
Source: PexelsMost data scientists spend the majority of their working hours in a notebook. As a result, most production machine learning platforms prioritize notebook support. If you try out a new production ML platform, chances are its onboarding tutorial will begin with a .ipynb file. When we built Cortex, our production machine learning platform, we spent a lot of time considering the correct
Image from my Understanding Decision Trees for Classification (Python) Tutorial (blog, video).Decision trees are a popular supervised learning method for a variety of reasons. Benefits of decision trees include that they can be used for both regression and classification, they don’t require feature scaling…
Python has served us well — but will it last? Photo by David Clode on Unsplash It took the programming community a couple of decades to appreciate Python. But since the early 2010’s, it has been booming — and eventually surpassing C, C#, Java and JavaScript in popularity.
In this article, we will understand two popular data formats: COCO data format and Pascal VOC data formats. These data formats are used for annotating objects found in a data set used for computer vision. we will especially focus on annotations for object detection One of the most important tasks in computer vision is to label the data. There are several tools available where you can load the imag
In A Simple Approach To Templated SQL Queries In Python, I introduced the basics of SQL templates in Python using JinjaSql. This post further demonstrates the power of Jinja2 within JinjaSql templates using presets, loops, and custom functions. Let’s consider an everyday use case when we have a table with some dimensions and some numerical values, and we want to find some metrics for a given dimen
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