Publications by Dario Radečić
Python Parallelism: Essential Guide to Speeding up Your Python Code in Minutes
Essential guide to multiprocessing with Python. Executing tasks sequentially might not be a good idea. If the input to the second task isn’t an output of the first task, you’re wasting both time and CPU. As you probably know, Python’s Global Interpreter Lock (GIL) mechanism allows only one thread to execute Python bytecode at once. It’s...
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How to Create PDF Reports with Python – The Essential Guide
Reports are everywhere, so any tech professional must know how to create them. It’s a tedious and time-consuming task, which makes it a perfect candidate for automation with Python.You can benefit from an automated report generation whether you’re a data scientist or a software developer. For example, data scientists might use reports to show...
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How to Create PDF Reports with Python — The Essential Guide
Create PDF reports with beautiful visualizations in 10 minutes or lessReports are everywhere, so any tech professional must know how to create them. It’s a tedious and time-consuming task, which makes it a perfect candidate for automation with Python.You can benefit from an automated report generation whether you’re a data scientist or a soft...
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How to Create PDF Reports with Python — The Essential Guide
Create PDF reports with beautiful visualizations in 10 minutes or less. Reports are everywhere, so any tech professional must know how to create them. It’s a tedious and time-consuming task, which makes it a perfect candidate for automation with Python. You can benefit from an automated report generation whether you’re a data scientist or a s...
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3 Ways to Tune Hyperparameters of Machine Learning Models with Python
Machine learning models can be quite accurate out of the box. But more often than not, the accuracy can improve with hyperparameter tuning.Hyperparameter tuning is a lengthy process of increasing the model accuracy by tweaking the hyperparameters – values that can’t be learned and need to be specified before the training.Today you’ll learn ...
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Are The New M1 Macbooks Any Good for Data Science? Let’s Find Out
The new Intel-free Macbooks have been around for some time now. Naturally, I couldn’t resist and decided to buy one. What follows is a comparison between the 2019 Intel-based MBP and the new one in programming and data science tasks.If I had to describe the new M1 chip in a single word, I would be this one – amazing. Continue reading for a m...
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7 Must-Have Skills to Get a Job as a Data Scientist
Must-Have Skills for Data ScienceEverybody and their mother wants to learn data science. And there’s no reason not to – the job you do is interesting 95% of the time, the salaries are excellent, and most likely you can get the work done from the comfort of your bed. Think you have what it takes to join a world-class team? Apply for one or mo...
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7 Must-Have Skills to Get a Job as a Data Scientist
Must-Have Skills for Data ScienceEverybody and their mother wants to learn data science. And there’s no reason not to – the job you do is interesting 95% of the time, the salaries are excellent, and most likely you can get the job done from the comfort of your bed. You think you have what it takes? Apply for one or twelve open positions at ...
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Are The New M1 Macbooks Any Good for Deep Learning? Let’s Find Out
There’s a lot of hype behind the new Apple M1 chip. So far, it’s proven to be superior to anything Intel has offered. But what does this mean for deep learning? That’s what you’ll find out today.The new M1 chip isn’t just a CPU. On the MacBook Pro, it consists of 8 core CPU, 8 core GPU, and 16 core neural engine, among other things. Bot...
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PyTorch + SHAP = Explainable Convolutional Neural Networks
Black-box models are a thing of the past – even with deep learning. You can use SHAP to interpret the predictions of deep learning models, and it requires only a couple of lines of code. Today you’ll learn how on the well-known MNIST dataset.Convolutional neural networks can be tough to understand. A network learns the optimal feature extract...
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