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Python News Saturday, July 7
- Comparison of top data science libraries for Python, R and Scala [Infographic]
Developer, node, nodejs, coding, js, reactjs
- Python Programming Tutorials
- Elegant Python code for a Markov chain text generator – Eli Bendersky’s website
- What’s New — pandas 0.23.2 documentation
- CULA – FREE active monitoring companion
monitoring, bloggers, blogs, website
- Deep Learning versus Machine Learning in One Picture
- PEP 572 — Assignment Expressions
- M4A4 Neo-Noir (bs) GiveAway
- So while many languages can be useful for a data scientist, these three remain the most popular and are developed to implement data science and machine learning solutions.
- In this post, we have prepared an infographic which shows top 20 libraries in each programming language which are beneficial to data scientists and data engineers work.
- Although there are many specific fields of application of different data science packages, we want to focus on those that are perfectly suited for machine learning, visualization, mathematics and engineering, data manipulation and analysis, and reproducible research.
- Therefore, the language has many great libraries for machine learning and engineering; however, it lacks data analysis and visualization possibilities comparing to previous languages.
- These are the languages and libraries that have proved to be extremely useful in various data science use cases.
@DataScienceCtrl: Comparison of top data science libraries for Python, R and Scala [Infographic] https://t.co/wcxGCa9rLF
- The library also supports high- performance animations.
- This library enables canvas – – interactivity by allowing you to draw shapes and images using the canvas API for both desktop and mobile applications.
- I know all the rage right now is Reinforcement Learning, specifically Q-learning, but I don’t see where Q-learning is fundamentally going to work here better than other options, unless we dramatically simplify this challenge, or have hardware that vastly exceeds anything I could reasonably afford along with an extremely complex…
- Evolutionary algorithms are similar to reinforcement learning algorithms, so much so that I would argue that they are a form of reinforcement learning algorithms.
- The main idea of reinforcement learning is to reinforce good choices, through an end target result.
- With an evolutionary algorithm, in the case of StarCraft II, you allow the winning algorithm to be a part of the gene pool (training data), and the loser is forgotten.
- Next, we can draw something like our Nexuses on the map with: – – Besides images being width and height, but arrays being height and width, OpenCV also starts 0, 0 as the top left.
@Sentdex: Getting started with a #DeepLearning AI with Python in StarCraft II: https://t.co/FuLZLkeWAE https://t.co/rjfDC7acCV
- Without going into too much details, a Markov Chain is a model describing the probabilities of events based on the current state only (without having to recall all past states).
- It’s a dictionary mapping a string state to the probabilities of characters following this state.
- The learning process is simply sliding a window of 4 characters over the input, recording these appearances: – – The learning loop is extremely concise; this is made possible by the right choice of Python data structures.
- Then, we loop for an arbitrary bound and at every step we randomly select the following character, and update the current state.
- The following character is selected using weighted random selection – precisely the right idiom here, as we already have in each counter the weights – the more often some char was observed after a given state, the higher the chance to select it for sampling will be.
@elibendersky: Elegant Python code for a Markov chain text generator — https://t.co/SAIp5CyHa8 https://t.co/XXrcLPBqBs
- Pandas 0.23.2 is first pandas release thats compatible with Python 3.7 ( GH20552 ) – – and now accept to reduce over all axes to a scalar (GH19976) – – This also provides compatibility with NumPy 1.15, which now dispatches to .
- With NumPy 1.15 and pandas 0.23.1 or earlier, will no longer reduce over every axis: – – With pandas 0.23.2, that will correctly return False, as it did with NumPy < 1.15.
@TomAugspurger: Pandas 0.23.2 is out! https://t.co/YBg4qTqhEPPython 3.7 support!
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@analyticbridge: New Book: Time Series Forecasting With Python https://t.co/ZPPu8dcGkD
@ats: 「:=」という演算子を追加して，式の中で変数への代入を行うPEP 572 – Assignment Expressions(代入記法)がAcceptされそうになってモメてる。もし承認されれば，Python 3.8に搭載される。https://t.co/ntrAKdapIV
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