python


Top 10 Videos on Deep Learning in Python

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This'Top 10' list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of each of these 10 items, so you can choose one that best suits your needs. I have saved the best for last- the most comprehensive yet free YouTube course on DL . Before I actually list the best DL in Python videos, it is important that one understands the differences between the 5 most popular deep learning frameworks -SciKit Learn, TensorFlow, Theano, Keras, and Caffe.


Understanding Autoencoders using Tensorflow (Python)

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In this article, we will learn about autoencoders in deep learning. We will show a practical implementation of using a Denoising Autoencoder on the MNIST handwritten digits dataset as an example. In addition, we are sharing an implementation of the idea in Tensorflow. An autoencoder is an unsupervised machine learning algorithm that takes an image as input and reconstructs it using fewer number of bits. That may sound like image compression, but the biggest difference between an autoencoder and a general purpose image compression algorithms is that in case of autoencoders, the compression is achieved by learning on a training set of data.


5 Open-Source Machine Learning Frameworks and Tools - DZone AI

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Practical machine learning development has advanced at a remarkable pace. This is reflected by not only a rise in actual products based on, or offering, machine learning capabilities but also a rise in new development frameworks and methodologies, most of which are backed by open-source projects. In fact, developers and researchers beginning a new project can be easily overwhelmed by the choice of frameworks offered out there. These new tools vary considerably -- and striking a balance between keeping up with new trends and ensuring project stability and reliability can be hard. The list below describes five of the most popular open-source machine learning frameworks, what they offer, and what use cases they can best be applied to.


How to Prepare Univariate Time Series Data for Long Short-Term Memory Networks - Machine Learning Mastery

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It can be hard to prepare data when you're just getting started with deep learning. Long Short-Term Memory, or LSTM, recurrent neural networks expect three-dimensional input in the Keras Python deep learning library. If you have a long sequence of thousands of observations in your time series data, you must split your time series into samples and then reshape it for your LSTM model. In this tutorial, you will discover exactly how to prepare your univariate time series data for an LSTM model in Python with Keras. How to Prepare Univariate Time Series Data for Long Short-Term Memory Networks Photo by Miguel Mendez, some rights reserved.


Introduction to Python Deep Learning with Keras - Machine Learning Mastery

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Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. In this post, you will discover the Keras Python library that provides a clean and convenient way to create a range of deep learning models on top of Theano or TensorFlow. Introduction to the Python Deep Learning Library Keras Photo by Dennis Jarvis, some rights reserved. Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow.


Example of Deep Learning With R and Keras - DZone AI

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Users of R have long been deprived of the opportunity to join the deep learning movement while remaining within the same programming language. With the release of MXNet, the situation began to change, but the frequent updates to the original documentation and changes that break backward compatibility still limit the popularity of this library. TensorFlow, Theano, CNTK) combined with detailed documentation and a lot of examples looks much more attractive. This article presents a solution to the problem of segmenting images in Carvana Image Masking Challenge, in which you want to learn how to separate cars photographed from 16 different angles will be dismantled. The neural network part is fully implemented on Keras, image processing is answered by magick (interface to ImageMagick), and parallel processing is provided by parallel doParallel foreach (Windows) or parallel doMC foreach (Linux).


A Day in the Life of a Data Scientist

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A number of weeks ago I solicited feedback from my LinkedIn connections regarding what their typical day in the life of a data scientist consisted of. The response was genuinely overwhelming! Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavour. What follows is some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks.


How to install mxnet for deep learning - PyImageSearch

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What I like about mxnet is that it combines the best of both worlds in terms of performance and ease of use. Whenever I'm implementing a Convolutional Neural Network I tend to use Keras first. Keras is less verbose than mxnet and is often easier to implement a given neural network architecture training procedure. But when it's time for me to scale up from my initial experiments to ImageNet-size datasets (or larger) I often use mxnet to (1) build an efficiently packed dataset and then (2) train my network on multiple GPUs and/or multiple machines. Since the Python bindings to mxnet are compiled C/C binaries I'm able to milk every last bit of performance out of my machine(s).


Top 10 Videos on Deep Learning in Python

@machinelearnbot

This'Top 10' list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of each of these 10 items, so you can choose one that best suits your needs. I have saved the best for last- the most comprehensive yet free YouTube course on DL . Before I actually list the best DL in Python videos, it is important that one understands the differences between the 5 most popular deep learning frameworks -SciKit Learn, TensorFlow, Theano, Keras, and Caffe.


PokerBot: Create your poker AI bot in Python

@machinelearnbot

In this tutorial, you will learn step-by-step how to implement a poker bot in Python. First, we need an engine in which we can simulate our poker bot. It also has a GUI available which can graphically display a game. Both the engine and the GUI have excellent tutorials on their GitHub pages in how to use them. The choice for the engine (and/or the GUI) is arbitrary and can be replaced by any engine (and/or GUI) you like.