Deep Learning, NLP, NMT, AI, ML
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. EVENT SERIES Register for Microsoft Research Forum Join Microsoft Research Forum (opens in new tab) for a continuous exchange of ideas about science and technology research in the era of general AI. This series, which begins on January 30, will
Read morePython lists are similar to real-life lists. You can use them to store and organize a collection of objects, which can be of any data type. Instead of just storing one item, a list can hold multiple items while allowing manipulation and retrieval of those items. Because lists are mutable, you can think of them as being written in pencil. In other words, you can make changes. Tuples, on the other hand, are written in ink. They’re similar to lists […]
Read moreHeuristic algorithms, often referred to as heuristics, are tools used to approximate optimal algorithms to make faster and more efficient decisions. These are particularly useful in operational scenarios in production systems, such as determining which server a virtual machine should be assigned to or deciding whether data should be
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One of Python’s most distinctive features is the list comprehension, which you can use to create powerful functionality within a single line of code. However, many developers struggle to fully leverage the more advanced features of list comprehensions in Python. Some programmers even use them too much, which can lead to code that’s less efficient and harder to read. By the end of this tutorial, you’ll understand the full power of Python list comprehensions and know how to use their […]
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Introduction In this post, we will understand what is Monte Carlo Simulation, what are its typical steps along with benefits and limitations. We will also take a look at its real-world application followed by a few examples of Monte Carlo simulation in Python along with visualization for better clarity. What is Monte Carlo Simulation Monte Carlo simulation is a computational technique to approximate the behavior or output of a complex system or problem by repeated random sampling. This method relies […]
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Do you use NumPy, Pandas, or scikit-learn and want to get faster results? Nvidia has created GPU-based replacements for each of these with the shared promise of extra speed. For example, if you visit the front page of NVidia’s RAPIDS project, you’ll see benchmarks showing cuDF, a GPU-based Pandas replacement, is 15× to 80× faster than Pandas! Unfortunately, while those speed-ups are impressive, they are also misleading. GPU-based libraries might be the answer to your performance problems… or they might […]
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We write a program to solve a problem or make a tool that we can repeatedly solve a similar problem. For the latter, it is inevitable that we come back to revisit the program we wrote, or someone else is reusing the program we write. There is also a chance that we will encounter data that we didn’t foresee at the time we wrote our program. After all, we still want our program to work. There are some techniques and […]
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You’ve probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those looking to build and train their own machine learning models, Kaggle also offers an in-browser notebook environment and some free GPU hours. You can also […]
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There are different ways of debugging code in Python, one of which is to introduce breakpoints into the code at points where one would like to invoke a Python debugger. The statements used to enter a debugging session at different call sites depend on the version of the Python interpreter that one is working with, as we shall see in this tutorial. In this tutorial, you will discover various ways of setting breakpoints in different versions of Python. After completing […]
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Static analyzers are tools that help you check your code without really running your code. The most basic form of static analyzers is the syntax highlighters in your favorite editors. If you need to compile your code (say, in C++), your compiler, such as LLVM, may also provide some static analyzer functions to warn you about potential issues (e.g., mistaken assignment “=” for equality “==” in C++). In Python, we have some tools to identify potential errors or point out […]
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