AI News,

This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules.

chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning methods have transformed the analysis of natural images and video, and similar examples are beginning to emerge with medical images.

An alternative in the domain is to train towards human-created features before subsequent fine-tuning [42], which can help to sidestep this challenge though it does give up deep learning techniques' strength as feature constructors.

Diagnosing diabetic retinopathy through colour fundus images became an area of focus for deep learning researchers after a large labelled image set was made publicly available during a 2015 Kaggle competition [47].

Such features were also repurposed to detect melanoma, the deadliest form of skin cancer, from dermoscopic [51,52] and non-dermoscopic images of skin lesions [5,53,54] as well as age-related macular degeneration [55].

Reusing features from natural images is also an emerging approach for radiographic images, where datasets are often too small to train large deep neural networks without these techniques [56–59].

However, the target task required either re-training the initial model from scratch with special preprocessing or fine-tuning of the whole network on radiographs with heavy data augmentation to avoid overfitting.

In contrast with the other selected literature, they found a smaller network trained with data augmentation on a few hundred images from a few dozen patients can outperform a pre-trained out-of-domain classifier.

Straightforward attempts to capture useful information from full-size images in all three dimensions simultaneously via standard neural network architectures were computationally unfeasible.

[61] compared 2D, 2.5D and 3D CNNs on a number of tasks for computer-aided detection from CT scans and showed that 2.5D CNNs performed comparably well to 3D analogues, while requiring much less training time, especially on augmented training sets.

[62] showed that multimodal, multi-channel 3D deep architecture was successful at learning high-level brain tumour appearance features jointly from MRI, functional MRI and diffusion MRI images, outperforming single-modality or 2D models.

Overall, the variety of modalities, properties and sizes of training sets, the dimensionality of input and the importance of end goals in medical image analysis are provoking a development of specialized deep neural network architectures, training and validation protocols, and input representations that are not characteristic of widely-studied natural images.

[63] demonstrated that deep neural networks outperform a traditional computer-aided diagnosis system at low sensitivity and perform comparably at high sensitivity.

Combining pre-trained deep network architectures with multiple augmentation techniques enabled accurate detection of breast cancer from a very small set of histology images with less than 100 images per class [66].

Billing information in the form of ICD codes are simple annotations but phenotypic algorithms can combine laboratory tests, medication prescriptions and patient notes to generate more reliable phenotypes.

The resulting dataset [73] consisted of 112 120 frontal-view chest X-ray images from 30 805 patients, and each image was associated with one or more text-mined (weakly labelled) pathology categories (e.g.

Another example of semi-automated label generation for hand radiograph segmentation employed positive mining, an iterative procedure that combines manual labelling with automatic processing [74].

First, the initial training set was created by manually labelling 100 of 12 600 unlabelled radiographs that were used to train a model and predict labels for the rest of the dataset.

Finally, there is a need for better pathologist–computer interaction techniques that will allow combining the power of deep learning methods with human expertise and lead to better-informed decisions for patient treatment and care.

Practical guide to implement machine learning with CARET package in R (with practice problem)

One of the biggest challenge beginners in machine learning face is which algorithms to learn and focus on.

In case of R, the problem gets accentuated by the fact that various algorithms would have different syntax, different parameters to tune and different requirements on the data format.

So, then how do you transform from a beginner to a data scientist building hundreds of models and stacking them together? There certainly isn’t any shortcut but what I’ll tell you today will make you capable of applying hundreds of machine learning models without having to: All this has been made possible by the years of effort that have gone behind CARET ( Classification And Regression Training) which is possibly the biggest project in R.

While caret definitely simplifies the job to a degree, it can not take away the hard work and practice you need to put in to become a master at machine learning.

Heads up: It might take some time: Now, let’s get started using caret package on Loan Prediction 3 problem: In this problem, we have to predict the Loan Status of a person based on his/ her profile.

For example, to apply, GBM, Random forest, Neural net and Logistic regression : You can proceed further tune the parameters in all these algorithms using the parameter tuning techniques.

The resampling technique used for evaluating the performance of the model using a set of parameters in Caret by default is bootstrap, but it provides alternatives for using k-fold, repeated k-fold as well as Leave-one-out cross validation (LOOCV) which can be specified using trainControl().

If the search space for parameters is not defined, Caret will use 3 random values of each tunable parameter and use the cross-validation results to find the best set of parameters for that algorithm.

Otherwise, there are two more ways to tune parameters: To find the parameters of a model that can be tuned, you can use Accuracy was used to select the optimal model using  the largest value.

Here, it keeps the shrinkage and n.minobsinnode parameters constant while alters n.trees and interaction.depth over 10 values and uses the best combination to train the final model with.

For type=”raw”, the predictions will just be the outcome classes for the testing data while for type=”prob”, it will give probabilities for the occurrence of each observation in various classes of the outcome variable.

Caret is a very comprehensive package and instead of covering all the functionalities that it offers, I thought it’ll be a better idea to show an end-to-end implementation of Caret on a real hackathon J dataset.

Machine learning

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to 'learn' (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.[2]

These analytical models allow researchers, data scientists, engineers, and analysts to 'produce reliable, repeatable decisions and results' and uncover 'hidden insights' through learning from historical relationships and trends in the data.[8]

Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'[9]

Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[13]:708–710;

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.

Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).

The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.[15]

The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

An artificial neural network (ANN) learning algorithm, usually called 'neural network' (NN), is a learning algorithm that is vaguely inspired by biological neural networks.

They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network.

Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.

Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar.

Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters.

Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG).

Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution.

Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.

genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem.

In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.

Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly.[37]

Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[44]

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.

In comparison, the N-fold-cross-validation method randomly splits the data in k subsets where the k-1 instances of the data are used to train the model while the kth instance is used to test the predictive ability of the training model.

For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[61][62]

There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed.[64]

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