AI News, Amazon Mechanical Turk: help for building your Machine Learning datasets

Amazon Mechanical Turk: help for building your Machine Learning datasets

Whether you build your own machine learning models in the Cloud or using complex mathematical tools, one of the most expensive and time consuming part of building your model is likely to be generating a high-quality dataset.

Sometimes you already have a large amount of historical data and a precise ground truth knowledge about each data point, in which case your dataset is already labelled and all you need to do is clean, normalize, sub-sample, analyze, and train a model, and then iterate until you achieve a good evaluation.

But more often, all you have is a big bucket of raw unlabelled data and the process of manually building a consistent ground truth might be the most painful phase of your machine learning workflow.

Some of these scenarios are well covered by companies and services that provide subject matter expertise about your specific context (linguistics, semantics, statistics, etc), usually at a very high cost.

Other contexts, for example in the case of multimedia annotations, are way harder to handle, and it turns out that crowdsourcing might be a great way to cut down both costs and time.

In case your task is particularly tough, you can raise the number of submissions to two and eventually lower the reward to $0.20, resulting in a total cost of $400, and so on until you find the best trade off between quality and cost.

The next step is to describe your task and, optionally, provide additional information (like real examples or doubtful cases), so your workers will know what each category should include or exclude.

In some cases, a few records might not have achieved any consensus, so could either improve your task instructions or, if the remaining dataset is big and statistically distributed enough to generate a useful model, simply discard them.

The tricky part you should keep in mind is that the very same features extraction logic will have to be executed before each classification request and for each image, since your AmazonML model has been trained that way and will expect the same features at runtime.

Besides the complexity of multimedia classification, which will hopefully be addressed by AWS soon, I think that Amazon Mechanical Turk and other crowdsourcing platforms can be very useful in helping you to build your machine learning model from an unlabelled dataset.

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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List of datasets for machine learning research

Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data.

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

Leveraging on transfer learning for image classification using Keras

The task of image classification has persisted from the beginning of computer vision.

Before the onset of Deep Learning, computer vision was heavily dependent on hardcoded mathematical formulas that worked on very specific use cases.

With the advances in neural networks, convolutional neural networks (CNN) have become very efficient at image classification.

This is a very efficient method to do image classification because, we can use transfer learning to create a model that suits our use case.

One important task that an image classification model needs to be good at is - they should classify images belonging to the same class and also differentiate between images that are different.

If our dataset is small and similar to the original dataset, we could use the pre-trained convnets as a fixed feature extractor.

A fixed length vector is computed for every image and then a linear classifier is trained for the new dataset.

While loading, we include the argument include_top = False this will remove the 3 top fully connected layers.

We are scaling the data between 1 to 255, with a image rotation range of 40 degrees along with a few other transformations.

Keras has all this inbuilt so, we don’t need to worry about doing it manually using tools like opencv or scikit-image.

Then we proceed to create a generator with a resized image of 150 X 150 and saving the features as numpy arrays.

One hot encoding is a process to transform categorical features into a format that is more suitable for classification and regression problems.

But a word of caution, make sure that the learning rate is very low to ensure that the learned weights of the network don’t undergo drastic changes.

It can reduce your effort considerably while building a classification model, also you could integrate other mechanisms to perform more complex tasks like object detection.

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