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) …
New KDnuggets Poll is asking: Which Data Science / Machine Learning methods and tools you used in the past 12 months for work or a real-world project? Please vote below and we will summarize the results and examine the trends in early December. Poll Which Data Science / Machine Learning methods and tools you used in the past 12 months for a real-world application? Kaggle survey asked: What data science methods are used at work? and the top answers were Gradient Boosted Machines
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 for Statsbot, I will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you'll find the structured overview of the main features of described algorithms.
In the past few years, deep learning is all the fuss in the tech industry. To keep up on things I like to get my hands dirty implementing interesting network architectures I come across in article readings. Few months ago I came across a very nice article called Siamese Recurrent Architectures for Learning Sentence Similarity.It offers a pretty straightforward approach to the common problem of sentence similarity. Named MaLSTM ("Ma" for Manhattan distance), its architecture is depicted in figure 1 (diagram excludes the sentence preprocessing part). Notice that since this is a Siamese network, it is easier to train because it shares weights on both sides.
Get the code: To follow along, all the code is also available as an iPython notebook on Github. In this post we will implement a simple 3-layer neural network from scratch. We won't derive all the math that's required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details. Here I'm assuming that you are familiar with basic Calculus and Machine Learning concepts, e.g.
This article targets whom have a basic understanding for TensorFlow Core API. Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model. The loss returned is 53.76. Existence of error, specially for large error, means that the parameters used must be updated.
Summary: Convolutional Neural Nets are getting all the press but it's Recurrent Neural Nets that are the real workhorse of this generation of AI. We've paid a lot of attention lately to Convolutional Neural Nets (CNNs) as the cornerstone of 2nd gen NNs and spent some time on Spiking Neural Nets (SNNs) as the most likely path forward to 3rd gen, but we'd really be remiss if we didn't stop to recognize Recurrent Neural Nets (RNNs). Because RNNs are solid performers in the 2nd gen NN world and perform many tasks much better than CNNs. These include speech-to-text, language translation, and even automated captioning for images. By count, there are probably more applications for RNNs than for CNNs.
I get bored from doing two things: i) spot-checking optimising parameters of my predictive models and ii) reading about how'black box' machine learning (particularly deep learning) models are and how little we can do to better understand how they learn (or not learn, for example when they take a panda bear as a vulture!). In this post I'll test a) H2O's function h2o.automl() that may help me automate the former and b) Thomas Lin Pedersen's library(lime) that may help clarify the latter. This post would never happen if not for the inspiration I got from two excellent blog posts: Shirin Glander's Lime presentation and Matt Dancho's HR data analysis. There's no hiding that this post is basically copy-catting their work, at least I'm standing on the shoulders of giants, hey! I'll use the powerful h2o.automl() function to optimise and choose the most accurate model classifying benign and malignant cancer cells from the Wisconsin dataset. First, let's load the data: Now, let's set up the local H2O instance… Finally, we can now use the famous h2o.automl() function and set the model up: set the target, feature names, training and validation set, as well as how long we want the algorithm to run for (for this you can use either max_runtime_secs argument, like I did here, or max_models, see h2o.automl() documentation for details.
A neural network is a clever arrangement of linear and non-linear modules. When we choose and connect them wisely, we have a powerful tool to approximate any mathematical function. A topic that is not always explained in depth, despite of its intuitive and modular nature, is the backpropagation technique responsible for updating trainable parameters. Let's build a neural network from scratch to see the internal functioning of a neural network using LEGO pieces as a modular analogy, one brick at a time. The above figure depicts some of the Math used for training a neural network.
Forward propagation is a process of feeding input values to the neural network and getting an output which we call predicted value. Sometimes we refer forward propagation as inference. When we feed the input values to the neural network's first layer, it goes without any operations. Second layer takes values from first layer and applies multiplication, addition and activation operations and passes this value to the next layer. Same process repeats for subsequent layers and finally we get an output value from the last layer.
Roman Trusov is a researcher at XIX.ai. Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you want. This fact steadily becomes worrisome as more and more systems are powered by artificial intelligence -- and many of them are crucial for our safe and comfortable life. Lately, safety concerns about AI were revolving around ethics -- today we are going to talk about more pressuring and real issues. Machine learning algorithms accept inputs as numeric vectors.