AI News, Practical machine learning techniques for building intelligent applications

Practical machine learning techniques for building intelligent applications

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Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.

Here are some highlights from our conversation: One thing that I have found extremely interesting is the way that data scientists and engineers work together, which is something I really wasn't aware of before … when I was still at university, most of the people, or many people who end up in data science, are not actually computer scientists.

That's specifically when you work on more open-ended problems where there's some exploratory component … For a classical software engineer, it's really about code quality, building something.

General neural networks were invented in the 1980s, but of course, computers were much slower back then, so you couldn't train the networks of the size we have right now.

When I was a student studying the first neural networks, the first lecture was just about backprop, and then the next lecture was an exercise where you had to compute the update rules for yourself and then implement them.

Then some people, at some point, realized you can do this using the chain rule, in a way that you can just compose different network layers, and then you can automatically compute the update rule.

… I'm still waiting for some algorithm that is able to get internal representations about the world, which then allows it to reason about the world in a way that is very similar to what humans do.

Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks

First, the good news is that our “8” recognizer really does work well on simple images where the letter is right in the middle of the image: But now the really bad news: Our “8” recognizer totally fails to work when the letter isn’t perfectly centered in the image.

We can just write a script to generate new images with the “8”s in all kinds of different positions in the image: Using this technique, we can easily create an endless supply of training data.

But once we figured out how to use 3d graphics cards (which were designed to do matrix multiplication really fast) instead of normal computer processors, working with large neural networks suddenly became practical.

It doesn’t make sense to train a network to recognize an “8” at the top of a picture separately from training it to recognize an “8” at the bottom of a picture as if those were two totally different objects.

Instead of feeding entire images into our neural network as one grid of numbers, we’re going to do something a lot smarter that takes advantage of the idea that an object is the same no matter where it appears in a picture.

Here’s how it’s going to work, step by step — Similar to our sliding window search above, let’s pass a sliding window over the entire original image and save each result as a separate, tiny picture tile: By doing this, we turned our original image into 77 equally-sized tiny image tiles.

We’ll do the exact same thing here, but we’ll do it for each individual image tile: However, there’s one big twist: We’ll keep the same neural network weights for every single tile in the same original image.

It looks like this: In other words, we’ve started with a large image and we ended with a slightly smaller array that records which sections of our original image were the most interesting.

We’ll just look at each 2x2 square of the array and keep the biggest number: The idea here is that if we found something interesting in any of the four input tiles that makes up each 2x2 grid square, we’ll just keep the most interesting bit.

So from start to finish, our whole five-step pipeline looks like this: Our image processing pipeline is a series of steps: convolution, max-pooling, and finally a fully-connected network.

For example, the first convolution step might learn to recognize sharp edges, the second convolution step might recognize beaks using it’s knowledge of sharp edges, the third step might recognize entire birds using it’s knowledge of beaks, etc.

Here’s what a more realistic deep convolutional network (like you would find in a research paper) looks like: In this case, they start a 224 x 224 pixel image, apply convolution and max pooling twice, apply convolution 3 more times, apply max pooling and then have two fully-connected layers.

Can neural networks solve any problem?

At some point in your deep learning journey you probably came across the Universal Approximation Theorem.

I prove to myself visually and empirically that UAT holds for a non-trivial function (x³+x²-x -1) using a single hidden layer and 6 neurons.

eventually discovered Michael Neilson’s tutorial which is so good it nearly renders this post obsolete (I highly encourage you to read it!), but for now let’s travel back in time and pretend Michael took his family to Disneyland that day instead of writing the world’s greatest tutorial on neural networks ever.

realized early on I wasn’t going to win this battle perusing mathematical proofs, so I decided to take an experimental approach.

The negative signs in Z represent the final layer’s weights which I set to -1 in order to “flip” the graph across the x-axis to match our concave target.

The Universal Approximation Theorem states that a neural network with 1 hidden layer can approximate any continuous function for inputs within a specific range.

wanted to prove programmatically the weights I came up with actually work when plugged into a basic neural network with one hidden layer and 6 neurons.

But this actually supports our earlier conclusion: a neural network with one hidden layer can approximate any continuous function but only for inputs within a specific range.

If you train a network on inputs between -2 and 2, like we did, then it will work well for inputs in a similar range, but you can’t expect it to generalize to other inputs without retraining the model or adding more hidden neurons.

Now we know Brendan Fortuner can learn these weights on his own, but can a real-world neural network with 1 hidden layer and 6 neurons also learn these parameters or others that lead to the same result?

Supervised learning

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.[1] It infers a function from labeled training data consisting of a set of training examples.[2] In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances.

This requires the learning algorithm to generalize from the training data to unseen situations in a 'reasonable' way (see inductive bias).

There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem).

The prediction error of a learned classifier is related to the sum of the bias and the variance of the learning algorithm.[4] Generally, there is a tradeoff between bias and variance.

A key aspect of many supervised learning methods is that they are able to adjust this tradeoff between bias and variance (either automatically or by providing a bias/variance parameter that the user can adjust).

The second issue is the amount of training data available relative to the complexity of the 'true' function (classifier or regression function).

If the true function is simple, then an 'inflexible' learning algorithm with high bias and low variance will be able to learn it from a small amount of data.

But if the true function is highly complex (e.g., because it involves complex interactions among many different input features and behaves differently in different parts of the input space), then the function will only be learnable from a very large amount of training data and using a 'flexible' learning algorithm with low bias and high variance.

If the input feature vectors have very high dimension, the learning problem can be difficult even if the true function only depends on a small number of those features.

Hence, high input dimensionality typically requires tuning the classifier to have low variance and high bias.

In practice, if the engineer can manually remove irrelevant features from the input data, this is likely to improve the accuracy of the learned function.

In addition, there are many algorithms for feature selection that seek to identify the relevant features and discard the irrelevant ones.

This is an instance of the more general strategy of dimensionality reduction, which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm.

fourth issue is the degree of noise in the desired output values (the supervisory target variables).

If the desired output values are often incorrect (because of human error or sensor errors), then the learning algorithm should not attempt to find a function that exactly matches the training examples.

You can overfit even when there are no measurement errors (stochastic noise) if the function you are trying to learn is too complex for your learning model.

In such a situation, the part of the target function that cannot be modeled 'corrupts' your training data - this phenomenon has been called deterministic noise.

In practice, there are several approaches to alleviate noise in the output values such as early stopping to prevent overfitting as well as detecting and removing the noisy training examples prior to training the supervised learning algorithm.

There are several algorithms that identify noisy training examples and removing the suspected noisy training examples prior to training has decreased generalization error with statistical significance.[5][6] Other factors to consider when choosing and applying a learning algorithm include the following: When considering a new application, the engineer can compare multiple learning algorithms and experimentally determine which one works best on the problem at hand (see cross validation).

Given fixed resources, it is often better to spend more time collecting additional training data and more informative features than it is to spend extra time tuning the learning algorithms.

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For example, naive Bayes and linear discriminant analysis are joint probability models, whereas logistic regression is a conditional probability model.

empirical risk minimization and structural risk minimization.[7] Empirical risk minimization seeks the function that best fits the training data.

In both cases, it is assumed that the training set consists of a sample of independent and identically distributed pairs,

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This can be estimated from the training data as In empirical risk minimization, the supervised learning algorithm seeks the function

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contains many candidate functions or the training set is not sufficiently large, empirical risk minimization leads to high variance and poor generalization.

The regularization penalty can be viewed as implementing a form of Occam's razor that prefers simpler functions over more complex ones.

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The training methods described above are discriminative training methods, because they seek to find a function

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ORNL researchers turn to deep learning to solve science’s big data problem

August 25, 2017 – A team of researchers from Oak Ridge National Laboratory has been awarded nearly $2 million over three years from the Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis.

For example, neutron scattering data collected at ORNL’s Spallation Neutron Source contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties.

The team aims to revolutionize current analysis paradigms by using deep learning to identify patterns in scientific data that alert scientists to potential new discoveries.

Potok’s team plans to construct a deep learning network capable of deciphering data from hundreds of thousands of inputs, such as sensors, and learning from the complex matrices of sensor readings developed over time.

The researchers recently outlined their approach to deep learning in a paper titled “A study of complex deep learning networks on high performance, neuromorphic, and quantum computers” in the Proceedings of the Workshop on Machine Learning in High Performance Computing Environments.

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