Dropout means to drop out units which are covered up and noticeable in a neural network. Dropout is a staggeringly in vogue method to overcome overfitting in neural networks.
Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep…Continue
Added by saurav singla on July 28, 2020 at 12:12am — No Comments
Summary: If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.
Summary: In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable. C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list. Here’s what’s changed.
For data scientists these are miraculous times. We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so. Still the rate…Continue
Added by William Vorhies on March 4, 2019 at 9:41am — No Comments
Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications. Here are five strategies for getting around the data problem including the latest in One Shot Learning.
Summary: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us.
Added by William Vorhies on November 18, 2018 at 11:14am — No Comments
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you’re aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit.
One of ML's…Continue
Added by Olexander Kolisnykov on September 18, 2018 at 2:52am — No Comments
Summary: How about we develop a ML platform that any domain expert can use to build a deep learning model without help from specialist data scientists, in a fraction of the time and cost. The good news is the folks at the Stanford DAWN project are hard at work on just such a platform and the initial results are extraordinary.
Added by William Vorhies on September 4, 2018 at 8:02am — No Comments
Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem. Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying…Continue
Added by Jonathan Symonds on August 30, 2018 at 7:00am — No Comments
The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist…Continue
Added by Denys Harnat on August 28, 2018 at 3:35am — No Comments
In last part we have seen the basics of Artificial intelligence and Artificial Neural Networks. As mentioned in the last part this part will be focused on applications of Artificial neural networks. ANN is very vast concept and we can find its…Continue
Added by Jayesh Bapu Ahire on August 25, 2018 at 9:00pm — No Comments
Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this. However, the latest trends in CNNs may make this obsolete.
In my earlier post I discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned.
The significance of this work can be summarized as follows:
Added by Jonathan Symonds on August 9, 2018 at 11:30am — No Comments
The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons — machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts.
And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they…Continue
Added by Kateryna Lytvynova on July 13, 2018 at 2:00am — No Comments
Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …Continue
Added by Ahmet Taspinar on July 5, 2018 at 11:48am — No Comments
TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR.
Neural networks have demonstrated a great…
Added by Jonathan Symonds on June 21, 2018 at 9:30am — No Comments
This blog explores a typical image identification task using a convolutional ("Deep Learning") neural network. For this purpose we will use a simple JavaCNN packageby D.Persson, and make our example small and concise using the Python scripting language. This example can also be rewritten in Java, Groovy, JRuby or any scripting language supported by the Java virtual machine.
This example will use images in the grayscale format (PGM). The name "PGM" is an acronym derived from…
Added by jwork.ORG on May 31, 2018 at 1:30pm — No Comments
"No one wants to be sold but everyone wants to buy."
Most of us hate being sold. The moment we know someone is selling something, we keep our guards up.
In the book, The Challenger Sale, authors Mathew Dixon and Brent Adamson surveyed over 6000 salespeople from around the world and found that ‘challenger salespeople’ outperformed every other group. Who are these challenger salespeople? These…Continue
Added by Rudradeb Mitra on March 1, 2018 at 9:00pm — No Comments
In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.
It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods. If you have been following the latest technical developments you probably know that CNN’s are…Continue
Added by Ahmet Taspinar on December 4, 2017 at 5:00am — No Comments
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you’re like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don’t ease the choice.
In this article, I will try to explain basic concepts and give some intuition of using different…Continue
Added by Luba Belokon on October 26, 2017 at 6:00am — No Comments
Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some domain knowledge of the discipline where the data has originated from. For example, if one is dealing with signals (i.e. classification of EEG signals), then possible features would involve…Continue