This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Earlier this year, Kaggle ran a $1 million contest to build algorithms capable of identifying signs of lung cancer in CT scans, helping to fuel a larger effort to apply neural networks to health care.

Welcome to this course: Deep Learning - Learn Convolutional Neural Networks. Deep Learning has made some huge and significant contributions and it's one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases.

Description: Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples!

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The work of B. Zieliński was supported by the National Science Centre (Poland) under grant agreement no 2015/19/D/ST6/01215; 2016-2019. The work of P. Spurek was supported by the National Science Centre (Poland) under grant agreement no. The work of K. Misztal was supported by the National Science Centre (Poland) under grant agreement no. Competing interests: The authors have declared that no competing interests exist.

In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterative soft-thresholding algorithm (ISTA) that solves the optimization problem for sparse nonnegative matrix factorization (NMF) of spectrograms. We name this network architecture deep recurrent NMF (DR-NMF). The proposed DR-NMF network has three distinct advantages. First, DR-NMF provides better interpretability than other deep architectures, since the weights correspond to NMF model parameters, even after training. This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization. Second, like many deep networks, DR-NMF is an order of magnitude faster at test time than NMF, since computation of the network output only requires evaluating a few layers at each time step. Third, when a limited amount of training data is available, DR-NMF exhibits stronger generalization and separation performance compared to sparse NMF and state-of-the-art long-short term memory (LSTM) networks. When a large amount of training data is available, DR-NMF achieves lower yet competitive separation performance compared to LSTM networks.

NVIDIA announces it has brought together a dozen software partners for its Metropolis Software Partner Program. The program offers a curated list of applications that makes it easy for systems integrators and hardware vendors to build new products, according to the company. The NVIDIA Metropolis intelligent video analytics platform applies deep learning to video streams for applications such as public safety, traffic management and resource optimization. Together, we're taking advantage of the more than 1 billion video cameras that will be in our cities by the year 2020 to solve a dizzying array of problems. Imagine video-based, automated, real-time control of traffic signals to ease traffic congestion.

Join Philipp Schlunder, a member of the Data Science team at RapidMiner to learn about the basics of Deep Learning and its broader scope. Discover the main components used in creating neural networks and how RapidMiner enables you to leverage the power of Tensorflow, Microsoft Cognitive Toolkit and other frameworks in your existing RapidMiner analysis chain. In this 60-minute webinar we'll explore: Register anyway, and we'll send you the recording once it's available.

Google-owned DeepMind uses sophisticated computer simulations for computers to teach themselves how to accomplish certain tasks. The simulated training, known as reinforcement learning, involves the computer trying out thousands (or millions) of different things until it manages to figure out what to do. Using this approach combined with deep learning, the London-based artificial intelligence research unit is teaching computers how to beat the world's best Go players and training robots how to move around in the world. A tiny Berkeley, California-based AI startup, Bonsai, has invented a trick to beat DeepMind in this game. The trick -- the company is calling it "concept networks" -- massively increases the efficiency of reinforcement learning.