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).

Our world is advancing at an extremely rapid rate. Technologies such as artificial intelligence, machine learning, drones, internet of things, augmented reality, and blockchain are growing in popularity every single day. Personally, I feel another industrial revolution is approaching quickly and the world we will in is going to drastically change. Blockchain is a difficult technology to understand but it has the potential to impact many organizations across the globe. If you're looking to get a head start on an innovative idea that will change our world then you're in the right place!

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).

Human activity recognition (HAR) plays an important role in people's daily life by learning and identifying high-level knowledge about human activity from raw sensor inputs. Conventional pattern recognition approaches have made tremendous progress on HAR tasks by adopting machine learning algorithms such as decision tree, random forest or support vector machine, but the fast development and advancement of deep learning have overpass the accuracy of traditional machine learning results. This seminar is focused on Deep learning applied to HAR using wearable sensors. Current architectures used and how to implement them for achieving good results will be explained. Limitations and new challenges will be also discussed.

If you want a talk on Python with the Theano library in under an hour, targeted towards beginners, then you can refer to this talk by Alec Radford. Unlike most other talks on this topic, this one compares the features of an'old' net versus a'modern' net, ie nets prior to 2000 versus nets post-2012.

Automatic photo captioning is a problem where a model must generate a human-readable textual description given a photograph. It is a challenging problem in artificial intelligence that requires both image understanding from the field of computer vision as well as language generation from the field of natural language processing. It is now possible to develop your own image caption models using deep learning and freely available datasets of photos and their descriptions. In this tutorial, you will discover how to prepare photos and textual descriptions ready for developing a deep learning automatic photo caption generation model. How to Prepare a Photo Caption Dataset for Training a Deep Learning Model Photo by beverlyislike, some rights reserved. This tutorial assumes you have a Python 3 SciPy environment installed. You can use Python 2, but you may need to change some of the examples. You must have Keras (2.0 or higher) installed with either the TensorFlow or Theano backend.

Building on the success of UiPath Academy launched in April 2017 which counts 30,000 students of the RPA Developer Foundation Diploma course, Academy 2 is the first free-of-charge online Advanced Diploma for RPA Developers. The curriculum offers developers practical RPA development exercises focused on proven use cases, enabling them to master the UiPath RPA platform in a controlled environment. This is delivered alongside a series of webinars preparing users for a certification exam. Utilising UiPath's methodology of automation best practices, Academy 2 also provides in-depth teaching on how to develop scalable enterprise-grade RPA deployment. Also at UiPath Forward Americas a powerful line-up of early RPA adopters will present to an attendance of more than 700.

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.

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to maximize its reward. We discuss different algorithms for reinforcement learning including Q-Learning, policy gradients, and Actor-Critic. We show how deep reinforcement learning has been used to play Atari games and to achieve super-human Go performance in AlphaGo. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems.

Businesses increasingly rely on data analytics to inform everything from daily operations to customer service to marketing initiatives. As a result, data science has become a hot skill in high demand across a broad range of industries. And bootcamps are great way to hone data science skills, get up to speed on the latest data science trends, shift your career path or create greater job security within your industry. If you're interested in learning more about data science, one of these eight bootcamps will help you get the skills you need to boost your portfolio to land a new job or score a promotion. Thinkful offers a self-paced online bootcamp with a project-based curriculum, career prep, one-on-one mentorship and access to a full community of students, mentors and alumni.