The best Python IDEs for data science that make data analysis and machine learning easier! • Spyder • PyCharm • Rodeo • Atom • Jupyter Notebook
W ell, your python program is running slow? Here is an idea to boost its performance. The concept of parallel processing is very helpful for all those data scientists and programmers leveraging Python for Data Science. Python with its powerful libraries such as numpy, scipy, matplotlib etc., has already reduced the time and cost of development and other useful works. In this article, I’m going to discuss parallel processing to boost up the processing of the Python program.
I’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. Now, I believe I’ve finally found a book with practical techniques that I can use on real problems: Causal Inference by Miguel Hernán and Jamie Robins. It is available for free from their site, but is still in draft mode. This post is a short summary of the reasons why I think Causal Inference is a great practical resource.
There are lots of ways to assess how predictive a model is while correcting for overfitting. In Caret the main methods I use are leave one out cross validation, for when we have relatively few samples, and k fold cross validation when we have more. There also is another method called ‘optimism corrected bootstrapping’, that attempts to save statistical power, by first getting the overfitted result, then trying to correct this result by bootstrapping the data to estimate the degree of optimism. A few simple tests in Caret can demonstrate this method is bunk.
In the spirit of the coming new year and new beginnings, we created a tutorial for getting started or restarted with R. If you are new to R or have dabbled in R but haven’t used it much recently, then this post is for you. We will focus on data classes and types, as well as data wrangling, and we will provide basic statistics and basic plotting examples using real data. Enjoy!
In this article, we will see what are Convolutional Neural Networks, ConvNets in short. ConvNets are the superheroes that took working with images in deep learning to the next level. With ConvNets, the input is a image, or more specifically, a 3D Matrix.
Today’s trend of Artificial Intelligence (AI) and the increased level of Automation in manufacturing allow firms to flexibly connect assets and improve productivity through data-driven insights that has not been possible before. As more automation is used in manufacturing, the speed of responses required in dealing with maintenance issues is going to get faster and automated decisions as to what’s the best option from an economic standpoint are getting more complex.
Speech recognition systems has been one of the most developed areas in the deep learning ecosystem. The current generation of speech recognition models rely mostly on recurrent neural networks(RNNs) for acoustic and language modeling and on computationally-expensive artifacts such as feature extraction pipelines for knowledge building. While RNN-based techniques have proven to be effective in speech recognition tasks, they require large volumes of training data and computing power often resulting prohibited for most organizations. Recently, Facebook AI Research(FAIR) team published a research paper proposing a new speech recognition technique based solely on convolutional neural networks(CNNs). The FAIR team went beyond research and open sourced the Wav2letter++, a high performance speech recognition toolkit based on the fully-convolutional method.
A new version of Flair – simple Python NLP library has just been released by Zalando Research! Why is this big news for NLP? Flair delivers state-of-the-art performance in solving NLP problems such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and text classification. It’s an NLP framework built on top of PyTorch. This article explains how to use existing and build custom text classifiers with Flair.
Repositories of the research branch of Zalando SE.
I am assuming that the reader is familiar with Liner regression model and its functionality. Here I have tried to explain logistic regression with as easy explanation as it was possible for me. When I was trying to understand the logistic regression myself, I wasn’t getting any comprehensive answers for it, but after doing thorough study of the topic, this post is what I came up with. Note that this is more of an introductory article, to dive deep into this topic you would have to learn many different aspects of data analytics and their implementations.
Discretisation is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that span the range of variable values.
A few weeks back I wrote an article diving into NHL play-by-play shots, how the turn into rebounds, and how those two events affect the outcome of games. In the piece you are reading now, I wanted to look at the same analytics from a different perspective. I hope that it will help inform prospective/new data scientists on how to start building your own projects and give some clarity on how I think.
In this article we are going to study in depth how the process for developing a machine learning model is done. There will be a lot of concepts explained and we will reserve others that are more specific to future articles. 1. Define Appropiately the Problem 2. Collect Data 3. Choose a Measure of Success 4. Setting an Evaluation Protocol 5. Preparing The Data 6. Developing a Benchmark Model 7. Developing a Better Model & Tunning its Hyperparameters 8. Conclusion
Exploratory Data Analysis (EDA) is the bread and butter of anyone who deals with data. With information increasing by 2.5 quintillions bytes per day (Forbes, 2018), the need for efficient EDA techniques is at its all-time high. So where is this deluge coming from? The amount of useful information is almost certainly not increasing at such a rate. When we take a closer look, we would realize that most of this increase is contributed by noise. There are so many hypotheses to test, so many datasets to mine, but a relatively constant amount of objective truth. With most data scientists, their key objective is to able to distinguish the signal from the noise, and EDA is the main process to do this.
Causes of overfitting and how regularization improves it.
In natural language processing, there may come a time when you want your program to recognize that the words ‘ask’ and ‘asked’ are just different tenses of the1 same verb. This is the idea of reducing different forms of a word to a core root. Words that are derived from one another can be mapped to a central word or symbol, especially if they have the same core meaning. Maybe this is in an information retrieval setting and you want to boost your algorithm’s recall. Or perhaps you are trying to analyze word usage in a corpus and wish to condense related words so that you don’t have as much variability. Either way, this technique of text normalization may be useful to you.
Many people regard the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) as synonyms. This is actually quite far from the truth, and today we demystify these misconceptions.
In this post we will introduce few basic concepts of classical RL applied to a very simple task called gridworld in order to solve the so-called state-value function, a function that tells us how good is to be in a certain state t based on future rewards that can be achieved from that state. To do so we will use two approaches: (1) iterative policy evaluation and (2) Monte Carlo simulations.
mlFlow is a framework that supports the machine learning lifecycle. This means that it has components to monitor your model during training and running, ability to store models, load the model in production code and create a pipeline. The framework introduces 3 distinct features each with it’s own capabilities