AI News, Python Data Science Tutorials
Python Data Science Tutorials
Scikit-learn is far-and-away the go-to tool for implementing classification, regression, clustering, and dimensionality reduction, while StatsModels is less actively developed but still has a number of useful features.
It has seen monumental improvements over the last ~5 years, such as AlexNet in 2012, which was the first design to incorporate consecutive convolutional layers.
Big data is best defined as data that is either literally too large to reside on a single machine, or can’t be processed in the absence of a distributed environment.
API-driven services bring intelligence to any application
Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.
More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.
What is TensorFlow? The machine learning library explained
But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks—such as Google’s TensorFlow—that ease the process of acquiring data, training models, serving predictions, and refining future results.
TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor.
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
TensorFlow allows developers to create dataflow graphs—structures that describe how data moves through a graph, or a series of processing nodes.
Instead of dealing with the nitty-gritty details of implementing algorithms, or figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application.
The eager execution mode lets you evaluate and modify each graph operation separately and transparently, instead of constructing the entire graph as a single opaque object and evaluating it all at once.
Google has not only fueled the rapid pace of development behind the project, but created many significant offerings around TensorFlow that make it easier to deploy and easier to use: the above-mentioned TPU silicon for accelerated performance in Google’s cloud;
Why are GPUs necessary for training Deep Learning models?
Most of you would have heard exciting stuff happening using deep learning.
I have seen people training a simple deep learning model for days on their laptops (typically without GPUs) which leads to an impression that Deep Learning requires big systems to run execute.
When I first got introduced with deep learning, I thought that deep learning necessarily needs large Datacenter to run on, and “deep learning experts”
This is because every book that I referred or every talk that I heard, the author or speaker always say that deep learning requires a lot of computational power to run on.
I don’t have to take over Google to be a deep learning expert 😀 This is a common misconception that every beginner faces when diving into deep learning.
Although, it is true that deep learning needs considerable hardware to run efficiently, you don’t need it to be infinite to do your task.
We define an artificial neural network in our favorite programming language which would then be converted into a set of commands that run on the computer.
If you would have to guess which components of neural network do you think would require intense hardware resource, what would be your answer?
When you train a deep learning model, two main operations are performed: In forward pass, input is passed through the neural network and after processing the input, an output is generated.
Whereas in backward pass, we update the weights of neural network on the basis of error we get in forward pass.
So in a neural network, we can consider first array as input to the neural network, and the second array can be considered as weights of the network.
VGG16 (a convolutional neural network of 16 hidden layers which is frequently used in deep learning applications) has ~140 million parameters;
This is in a nutshell why we use GPU (graphics processing units) instead of a CPU (central processing unit) for training a neural network.
Before the boom of Deep learning, Google had a extremely powerful system to do their processing, which they had specially built for training huge nets.
GPGPUs were created for better and more general graphic processing, but were later found to fit scientific computing well.
In 2006, Nvidia came out with a high level language CUDA, which helps you write programs from graphic processors in a high level language.
If your tasks are going to be small or can fit in complex sequential processing, you don’t need a big system to work on.
Scenario 3: If you are regularly working on complex problems or are a company which leverages deep learning, you would probably be better off building a deep learning system or use a cloud service like AWS or FloydHub.
As mentioned above, there is a lot of research and active work happening to think of ways to accelerate computing.
In this article, we covered the motivations of using a GPU for deep learning applications and saw how to choose them for your task.
If you have any specific questions regarding the topic, feel free to comment below or ask them on discussion portal.
Top 20 Python libraries for data science in 2018
Python continues to take leading positions in solving data science tasks and challenges.
This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year.
It is intended for processing large multidimensional arrays and matrices, and an extensive collection of high-level mathematical functions and implemented methods makes it possible to perform various operations with these objects.
In addition to bug fixes and compatibility issues, the crucial changes regard styling possibilities, namely the printing format of NumPy objects.
The package contains tools that help with solving linear algebra, probability theory, integral calculus and many more tasks.
SciPy faced major build improvements in the form of continuous integration into different operating systems, new functions and methods and, what is especially important — the updated optimizers.
There have been a few new releases of the pandas library, including hundreds of new features, enhancements, bug fixes, and API changes.
The improvements regard pandas abilities for grouping and sorting data, more suitable output for the apply method, and the support in performing custom types operations.
Statsmodels is a Python module that provides many opportunities for statistical data analysis, such as statistical models estimation, performing statistical tests, etc.
Thus, this year brought time series improvements and new count models, namely GeneralizedPoisson, zero inflated models, and NegativeBinomialP, and new multivariate methods — factor analysis, MANOVA, and repeated measures within ANOVA.
As an example of an appearance improvements are an automatic alignment of axes legends and among significant colors improvements is a new colorblind-friendly color cycle.
The continuous enhancements of the library with new graphics and features brought the support for “multiple linked views” as well as animation, and crosstalk integration.
The library provides a versatile collection of graphs, styling possibilities, interaction abilities in the form of linking plots, adding widgets, and defining callbacks, and many more useful features.
Bokeh can boast with improved interactive abilities, like a rotation of categorical tick labels, as well as small zoom tool and customized tooltip fields enhancements.
With its help, it is possible to show the structure of graphs, which are very often needed when building neural networks and decision trees based algorithms.
It provides algorithms for many standard machine learning and data mining tasks such as clustering, regression, classification, dimensionality reduction, and model selection.
Gradient boosting is one of the most popular machine learning algorithms, which lies in building an ensemble of successively refined elementary models, namely decision trees.
These libraries provide highly optimized, scalable and fast implementations of gradient boosting, which makes them extremely popular among data scientists and Kaggle competitors, as many contests were won with the help of these algorithms.
PyTorch is a large framework that allows you to perform tensor computations with GPU acceleration, create dynamic computational graphs and automatically calculate gradients.
Therefore, dist-keras, elephas, and spark-deep-learning are gaining popularity and developing rapidly, and it is very difficult to single out one of the libraries since they are all designed to solve a common task.
Comparing to the previous year, some new modern libraries are gaining popularity while the ones that have become classical for data scientific tasks are continuously improving.
- On Friday, January 18, 2019
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