Neural Networks



Understanding the limits of deep learning

@machinelearnbot

Neural networks were invented in the '60s, but recent boosts in big data and computational power made them actually useful. A new discipline called "deep learning" has arisen that can apply complex neural network architectures to model patterns in data more accurately than ever before.


Medical Image Analysis with Deep Learning , Part 2

@machinelearnbot

Editor's note: This is a followup to the recently published part 1. You may want to check it out before moving forward.


Book: Java Deep Learning Essentials

@machinelearnbot

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset.


Caffe2 on iOS, Deep Learning Tutorial iOS Swift Tutorials by Jameson Quave

#artificialintelligence

At this years's F8 conference, Facebook's annual developer event, Facebook announced Caffe2 in collaboration with Nvidia. This framework gives developers yet another tool for building deep learning networks for machine learning. But I am super pumped about this one, because it is specifically designed to operate on mobile devices! So I couldn't resist but start digging in immediately.




Data Science 101: Preventing Overfitting in Neural Networks

@machinelearnbot

One of the major issues with artificial neural networks is that the models are quite complicated. For example, let's consider a neural network that's pulling data from an image from the MNIST database (28 by 28 pixels), feeds into two hidden layers with 30 neurons, and finally reaches a soft-max layer of 10 neurons. The total number of parameters in the network is nearly 25,000. This can be quite problematic, and to understand why, let's take a look at the example data in the figure below.


What's the Difference Between Machine Learning Techniques?

#artificialintelligence

Artificial intelligence (AI), machine learning (ML), and robots are the sights and sounds of science fiction books and movies. Isaac Asimov's Three Laws of Robotics, first introduced in the 1942 short story "Runaround," became the backbone for his novel I, Robot and its film adaptation (Fig. 1). Although we are still far away from achieving what movie producers and sci-fi writers have envisioned, the state of AI and ML has progressed significantly. AI software has also been in use for decades but advances in ML, including the use of deep neural networks (DNNs), are making headlines in application areas like self-driving cars.


The power of deep learning - SD Times

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Machine learning isn't the only term getting all the buzz. Deep learning, or a class of machine learning algorithms, is showing great promise, primarily because it's getting results.