If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
We extract losses at two levels, at the end of the generator and at the end of the full model. The first one is a perceptual loss computed directly on the generator's outputs. This first loss ensures the GAN model is oriented towards a deblurring task. It compares the outputs of the first convolutions of VGG. The second loss is the Wasserstein loss performed on the outputs of the whole model.
Back in November, we open-sourced our implementation of Mask R-CNN, and since then it's been forked 1400 times, used in a lot of projects, and improved upon by many generous contributors. We received a lot of questions as well, so in this post I'll explain how the model works and show how to use it in a real application. And, second, how to train a model from scratch and use it to build a smart color splash filter. Instance segmentation is the task of identifying object outlines at the pixel level. It's one of the harder computer vision problems, compared to other related tasks: Mask R-CNN is a two stage framework: the first stage scans the image and generates proposals(areas likely to contain an object).
The main focus of JDMP lies on a consistent data representation. Maybe you've heard that, for Linux everything is a file. For JDMP, everything is a Matrix! Well, not everything, but many objects do have a matrix representation. For example: you can combine several matrices to form a Variable, e.g. for a time series.
In this tutorial, we will introduce you to Machine Learning with Apache Spark. The hands-on lab for this tutorial is an Apache Zeppelin notebook that has all the steps necessary to ingest and explore data, train, test, visualize, and save a model. We will cover a basic Linear Regression model that will allow us perform simple predictions on a sample data. This model can be further expanded and modified to fit your needs. Most importantly, by the end of this tutorial, you will understand how to create an end-to-end pipeline for setting up and training simple models in Spark.
These interests in the soundscape and fantasizing AI led to my latest project, "Imaginary Soundscape". As I wrote, one can imagine scenes from a sound. Conversely, by taking a glance at a photo, we can imagine sounds we might hear if we were there. Can an AI system do the same? If so, what if we apply the method to images of Google Street View, so that we can walk around with the generated soundscape?
"AI," "big data," and "machine learning" are all trending buzzwords, and you might be curious about how they apply to your domain. You might even have startups beating down your door, pitching you their new "AI-powered" product. So how can you know which problems in your business are amenable to machine learning? To decide, you need to think about the problem to be solved and the available data, and ask questions about feasibility, intuition, and expectations. Machine learning can help automate your processes, but not all automation problems require learning.
Multiscale methods, in which a dataset is viewed and analyzed at different scales,are becoming more commonplace in machine learning recently and are proving to be valuable tools. At their core, multiscale methods capture the local geometry of neighborhoods defined by a series of distances between points or sets of nearest neighbors. This is a bit like viewing a part of a slide through a series of microscope resolutions. At high resolutions, very small features are captured in a small space within the sample. At lower resolutions, more of the slide is visible, and a person can investigate bigger features.Main advantages of multiscale methods include improved performance relative to state-of-the-art methods and dramatic reductions in necessary sample size to achieve these results.
This repo contains code for using a pre-trained TensorFlow model to classify the quality (e.g. Code for training a new model from a dataset of in-focus only images is included as well. This is not an official Google product. Add path to local repository (e.g. Run all tests to make sure everything works.
The microscope is mainly used for imaging applications to analyze terabytes of data per day. These applications can profit by late advances in computer vision and profound learning. Now, in collaboration with robotic microscopy applications, Google engineers have assembled high-quality image datasets that separate signal from noise. In "Assessing Microscope Image Focus Quality with Deep Learning", researchers trained a deep neural network to rate the focus quality of microscopy images with higher accuracy than previous methods. They added the pre-trained TensorFlow model with plugins in Fiji (ImageJ) and CellProfiler, two leading open source scientific image analysis tools to use with the graphical user interface or invoked via scripts.
Most likely, it might also be the key for us to unlock the future. We might be getting there sooner than later with the transformation of AI through Big Data. Artificial Intelligence or AI is becoming more and more rampant nowadays. AI's presence is more prominent when it comes to businesses such as content marketing, sales, and even Search Engine Optimization or SEO. It is no secret that AI is dependent on the data that it is given before it can go on its way.