AI News, DEEP LEARNING
I am starting a series of blog explaining concept of Machine Learning and Deep Learning or can say will provide short notes from following books.
The solution to the above problem is to allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept deﬁned in terms of its relation to simpler concepts.
In the case of probabilistic models, a good representation is often one that captures the posterior distribution of the underlying explanatory factors for the observed input(We will revisit this topic later in greater detail).
An autoencoder is the combination of an encoder function that converts the input data into a diﬀerent representation, and a decoder function that converts the new representation back into the original format.
It is not always clear which of these two views — the depth of the computational graph, or the depth of the probabilistic modeling graph — is most relevant, and because diﬀerent people choose diﬀerent sets of smallest elements from which to construct their graphs, there is no single correct value for the depth of an architecture, just as there is no single correct value for the length of a computer program.
Nor is there a consensus about how much depth a model requires to qualify as “deep.” Coming on to the division of various type of learning Fig 5 will give you a great idea about the difference and similarity between them.
These models were designed to take a set of n input values x1, . . . , xn and associate them with an output y.These models would learn a set of weights w1, . . . , wn and compute their output f(x, w) =x1*w1+···+xn*wn.
Most famously, they cannot learn theXOR function, where f([0,1], w) = 1 and f([1,0], w) = 1 but f([1,1], w) = 0 and f([0,0], w) = 0(Fig 7).
Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to 'learn' (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.
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.
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.'
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.: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.
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%.
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.
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.
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.
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
For the use of EHR data, we assessed the sample size, number of clinical events, the existence of labels (ie., the availability of gold standard targets of interest, such as mortality and target disease diagnosis), use of longitudinal or temporal information, handling of data quality (eg., missing or irregularly sampled data).
After reviewing the selected articles, we identify five categories of analytics tasks: Disease detection/classification refers to the tasks of detecting whether specific diseases can be confirmed in the EHR data.Sequential prediction of clinical events refers to predicting future clinical events based on past longitudinal event sequences.Concept embedding is algorithmically deriving feature representation of clinical concepts or phenotypes from EHR data.Data augmentation is creating realistic data elements or patient records based on real EHR data.EHR data privacy refers to the techniques that protect patient EHR privacy and confidentiality, eg., de-identification.
Table 1.Distributions of models over analytic tasks Disease Detection or Classification Sequential Prediction of Clinical Events Concept Embedding Data Augmentation EHR Privacy RNN and its variants [13, 20, 41–53] [23, 26–28, 42, 48–50, 54–56, 57, 45, 58–62, 41, 25, 45] [11, 14, 63–66]  [36,37] CNN and its variants [12, 15, 20, 68, 51, 69,70] [71,72, 57, 73] [31, 74, 22, 75–77] NA NA AE and its variants [78–81] NA [10, 30, 63, 82–87, 11, 30, 88] [53, 89,90, 86] NA Unsupervised embedding [91–93] [21, 24, 70, 91, 94] [29, 32, 95, 96, 85, 97] NA  GANs NA  NA [33, 35, 98, 89, 56, 98]  Disease Detection or Classification Sequential Prediction of Clinical Events Concept Embedding Data Augmentation EHR Privacy RNN and its variants [13, 20, 41–53] [23, 26–28, 42, 48–50, 54–56, 57, 45, 58–62, 41, 25, 45] [11, 14, 63–66]  [36,37] CNN and its variants [12, 15, 20, 68, 51, 69,70] [71,72, 57, 73] [31, 74, 22, 75–77] NA NA AE and its variants [78–81] NA [10, 30, 63, 82–87, 11, 30, 88] [53, 89,90, 86] NA Unsupervised embedding [91–93] [21, 24, 70, 91, 94] [29, 32, 95, 96, 85, 97] NA  GANs NA  NA [33, 35, 98, 89, 56, 98]  Table 1.Distributions of models over analytic tasks Disease Detection or Classification Sequential Prediction of Clinical Events Concept Embedding Data Augmentation EHR Privacy RNN and its variants [13, 20, 41–53] [23, 26–28, 42, 48–50, 54–56, 57, 45, 58–62, 41, 25, 45] [11, 14, 63–66]  [36,37] CNN and its variants [12, 15, 20, 68, 51, 69,70] [71,72, 57, 73] [31, 74, 22, 75–77] NA NA AE and its variants [78–81] NA [10, 30, 63, 82–87, 11, 30, 88] [53, 89,90, 86] NA Unsupervised embedding [91–93] [21, 24, 70, 91, 94] [29, 32, 95, 96, 85, 97] NA  GANs NA  NA [33, 35, 98, 89, 56, 98]  Disease Detection or Classification Sequential Prediction of Clinical Events Concept Embedding Data Augmentation EHR Privacy RNN and its variants [13, 20, 41–53] [23, 26–28, 42, 48–50, 54–56, 57, 45, 58–62, 41, 25, 45] [11, 14, 63–66]  [36,37] CNN and its variants [12, 15, 20, 68, 51, 69,70] [71,72, 57, 73] [31, 74, 22, 75–77] NA NA AE and its variants [78–81] NA [10, 30, 63, 82–87, 11, 30, 88] [53, 89,90, 86] NA Unsupervised embedding [91–93] [21, 24, 70, 91, 94] [29, 32, 95, 96, 85, 97] NA  GANs NA  NA [33, 35, 98, 89, 56, 98]  The goal of developing a deep learning model for disease classification is to map the input EHR data to the output disease target via multiple layers of neural networks.
Examples include the Pooled Resource Open-Access Amyotrophic Lateral Sclerosis (ALS) Clinical Trials data used in10 and the Parkinson’s Progression Markers Initiative data used in.11 Some studies include data from multiple modalities (eg., cognitive assessments, vital signs, medical images), and support both binary classification (eg., onset of disease12,13) and multi-class classification (eg., classification of stages of Parkinson’s disease14) Besides disease-specific multimodal data, some studies used multivariate time series data.
In the reviewed articles, some were conducted to predict the future onset of a new disease condition such as heart failure (HF) onset prediction using RNN on longitudinal outpatient data from Sutter Health.23 In24, using a cohort of 1328, 384 patients (3 295 775 visits) from the New Zealand National Minimum Dataset, the deep feedforward neural network was shown to have the best AUC performance (AUC = 0.734) in predicting next hospital admission.
However, deep learning models do not always outperform traditional models, as 32 compared deep models with shallow models (eg., random forest) using classification tasks on clinical notes and discovered that when training sample size is small (eg., 662 total subjects in this case), deep learning shows inferior performance.
Data augmentation includes various data synthesis and generation techniques that create either more training data to avoid overfitting or more labeled data to reduce the cost of label acquisition,33,34 or even generating adverse drug reaction trajectories to inform potential risks.35 For example, in35, patients from the Columbia University Irving Medical Center/New York Presbyterian database who were exposed to HMG-CoA reductase inhibitors or statins at any point in time were included.
built a RNN based de-identification system36 and evaluated their system using i2b2 2014 data (1304 notes with a 46 803 word vocabulary) and MIMIC de-identification data (1635 notes with a 69 525 word vocabulary) and showed better performance using RNN than existing systems.
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.2 This has dramatically improved machine learning performance in many domains, such as computer vision,38 natural language processing,39 and speech recognition,40 and has also demonstrated great performance in healthcare and medical domains, such as using deep neural networks to detect referable diabetic retinopathy.3 Various deep learning architectures besides fully connected neural networks were used to tackle different challenges as elaborated below.
RNNs are an extension of feedforward neural networks to model sequential data, such as time series,44 event sequences23 and natural language text.49 In particular, the recurrent structure in RNN can capture the complex temporal dynamics in the longitudinal EHR data, thus making them the preferred architecture for several EHR modeling tasks, including sequential clinical event prediction,23,26,42,47–50,54,55 disease classification,13,20,41–46 and computational phenotyping.11,14,63 The hidden states of the RNN work as its memory, since the current state of the hidden layer depends on the previous state of the hidden layer and the input at the current time.
DAE has been used for learning robust representations of human physiology,10,30,82 deriving robust patient representation from EHRs,30 or extracting EHR phenotypes that can be paired with genetic data to identify disease-gene associations.10 In image, speech, and video analysis, CNNs exploit local properties of data (stationarity and the compositionality through local statistics) and utilize convolutional and pooling layers to progressively extract abstract patterns.
For example, CNNs greatly improved the performance of automatic classification of skin lesions from image data.4 CNNs work as follows: the convolutional layers connect multiple local filters with their input data (raw data or outputs of previous layers) and produce translation invariant local features.
Word2vec variants have been applied to learn representation for medical codes.29,104 In particular, word2vec has been extended to create two-level representation for medical codes and clinical visits jointly.29 Word2vec has two variants: the continuous bag of words (CBOW) that predicts target (codes) given surrounding contexts, and the Skip-gram that predicts surrounding contexts given target (codes).
The short-term dependencies among medical events in EHRs were considered as local context for patient history and the long-term effects provided global context.29 Such contexts impact the hidden relations among the clinical variables (eg., diagnoses, procedures, medications, etc.) and future patient health outcomes (ie., disease or readmission).
However, it is challenging to identify the true signals from the long-term context due to the complex associations among the clinical events.11,14,54,106,107 In addition, some found patient records vary significantly in terms of data density, since events are irregularly sampled.11,14,25 Such irregularity, if not properly handled, would affect the model performance.
To solve the challenge of time irregularity, several strategies were proposed.14 borrowed the idea of dynamic time warping, an algorithm measuring similarity between two varying speed temporal sequences, and modeled it into the gate parameters of 2D-GRU, thus aligning EHR sequences pairwise.11 proposed to learn a subspace decomposition of the LSTM memory, thus discounting the effect of the memory according to the elapsed time.
Existing work often took a multitask learning approach to jointly learn data across multiple modalities.62,108–110 Multi-modal EHR learning often utilizes a strategy that requires certain neurons in the neural network model to be shared among all tasks, and certain neurons to be specialized for specific tasks.62,108–110 The tasks could be different types of lab tests58 or data modalities.62,108–110 For example, in109, the authors took a multitask learning approach to jointly model the prediction tasks based on two data modalities: medical codes and natural language text from clinical notes, and empirically demonstrated improved performance.
When introduced to EHR modeling, attention weights indicate the degree to which clinical events the model can predict disease onsets or future events.41,45 The attention mechanism is also used to derive a latent representation of medical codes (eg., diagnosis codes, medication codes).41 Biomedical ontology is a major source of biomedical knowledge that has been jointly modeled with the attention mechanism to add interpretability and model robustness.
It is noteworthy that deep learning models are ideal tools for recognizing diseases or predicting clinical events or outcomes (eg., mortality or treatment response) given time series data such as EEG or biosignals from ICU44,59,82 or imaging data.3,4 However, although deep learning techniques have shown promising results in performing many analytics tasks, several open challenges remain.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brain), which make them incompatible with neuroscience evidences.
Most modern deep learning models are based on an artificial neural network, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.
No universally agreed upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth >
For supervised learning tasks, deep learning methods obviate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures that remove redundancy in representation.
The universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions.
By 1991 such systems were used for recognizing isolated 2-D hand-written digits, while recognizing 3-D objects was done by matching 2-D images with a handcrafted 3-D object model.
But while Neocognitron required a human programmer to hand-merge features, Cresceptron learned an open number of features in each layer without supervision, where each feature is represented by a convolution kernel.
In 1994, André de Carvalho, together with Mike Fairhurst and David Bisset, published experimental results of a multi-layer boolean neural network, also known as a weightless neural network, composed of a 3-layers self-organising feature extraction neural network module (SOFT) followed by a multi-layer classification neural network module (GSN), which were independently trained.
In 1995, Brendan Frey demonstrated that it was possible to train (over two days) a network containing six fully connected layers and several hundred hidden units using the wake-sleep algorithm, co-developed with Peter Dayan and Hinton.
Simpler models that use task-specific handcrafted features such as Gabor filters and support vector machines (SVMs) were a popular choice in the 1990s and 2000s, because of ANNs' computational cost and a lack of understanding of how the brain wires its biological networks.
These methods never outperformed non-uniform internal-handcrafting Gaussian mixture model/Hidden Markov model (GMM-HMM) technology based on generative models of speech trained discriminatively.
The principle of elevating 'raw' features over hand-crafted optimization was first explored successfully in the architecture of deep autoencoder on the 'raw' spectrogram or linear filter-bank features in the late 1990s,
Many aspects of speech recognition were taken over by a deep learning method called long short-term memory (LSTM), a recurrent neural network published by Hochreiter and Schmidhuber in 1997.
showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation.
The impact of deep learning in industry began in the early 2000s, when CNNs already processed an estimated 10% to 20% of all the checks written in the US, according to Yann LeCun.
was motivated by the limitations of deep generative models of speech, and the possibility that given more capable hardware and large-scale data sets that deep neural nets (DNN) might become practical.
However, it was discovered that replacing pre-training with large amounts of training data for straightforward backpropagation when using DNNs with large, context-dependent output layers produced error rates dramatically lower than then-state-of-the-art Gaussian mixture model (GMM)/Hidden Markov Model (HMM) and also than more-advanced generative model-based systems.
offering technical insights into how to integrate deep learning into the existing highly efficient, run-time speech decoding system deployed by all major speech recognition systems.
In 2010, researchers extended deep learning from TIMIT to large vocabulary speech recognition, by adopting large output layers of the DNN based on context-dependent HMM states constructed by decision trees.
In 2009, Nvidia was involved in what was called the “big bang” of deep learning, “as deep-learning neural networks were trained with Nvidia graphics processing units (GPUs).”
In 2014, Hochreiter's group used deep learning to detect off-target and toxic effects of environmental chemicals in nutrients, household products and drugs and won the 'Tox21 Data Challenge' of NIH, FDA and NCATS.
Although CNNs trained by backpropagation had been around for decades, and GPU implementations of NNs for years, including CNNs, fast implementations of CNNs with max-pooling on GPUs in the style of Ciresan and colleagues were needed to progress on computer vision.
In November 2012, Ciresan et al.'s system also won the ICPR contest on analysis of large medical images for cancer detection, and in the following year also the MICCAI Grand Challenge on the same topic.
In 2013 and 2014, the error rate on the ImageNet task using deep learning was further reduced, following a similar trend in large-scale speech recognition.
For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as 'cat' or 'no cat' and using the analytic results to identify cats in other images.
Over time, attention focused on matching specific mental abilities, leading to deviations from biology such as backpropagation, or passing information in the reverse direction and adjusting the network to reflect that information.
Neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
Despite this number being several order of magnitude less than the number of neurons on a human brain, these networks can perform many tasks at a level beyond that of humans (e.g., recognizing faces, playing 'Go'
The goal is that eventually, the network will be trained to decompose an image into features, identify trends that exist across all samples and classify new images by their similarities without requiring human input.
The extra layers enable composition of features from lower layers, potentially modeling complex data with fewer units than a similarly performing shallow network.
The training process can be guaranteed to converge in one step with a new batch of data, and the computational complexity of the training algorithm is linear with respect to the number of neurons involved.
that involve multi-second intervals containing speech events separated by thousands of discrete time steps, where one time step corresponds to about 10 ms.
All major commercial speech recognition systems (e.g., Microsoft Cortana, Xbox, Skype Translator, Amazon Alexa, Google Now, Apple Siri, Baidu and iFlyTek voice search, and a range of Nuance speech products, etc.) are based on deep learning.
DNNs have proven themselves capable, for example, of a) identifying the style period of a given painting, b) 'capturing' the style of a given painting and applying it in a visually pleasing manner to an arbitrary photograph, and c) generating striking imagery based on random visual input fields.
Word embedding, such as word2vec, can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset;
Finding the appropriate mobile audience for mobile advertising is always challenging, since many data points must be considered and assimilated before a target segment can be created and used in ad serving by any ad server.
'Deep anti-money laundering detection system can spot and recognize relationships and similarities between data and, further down the road, learn to detect anomalies or classify and predict specific events'.
Deep learning is closely related to a class of theories of brain development (specifically, neocortical development) proposed by cognitive neuroscientists in the early 1990s.
These developmental models share the property that various proposed learning dynamics in the brain (e.g., a wave of nerve growth factor) support the self-organization somewhat analogous to the neural networks utilized in deep learning models.
Like the neocortex, neural networks employ a hierarchy of layered filters in which each layer considers information from a prior layer (or the operating environment), and then passes its output (and possibly the original input), to other layers.
Other researchers have argued that unsupervised forms of deep learning, such as those based on hierarchical generative models and deep belief networks, may be closer to biological reality.
researchers at The University of Texas at Austin (UT) developed a machine learning framework called Training an Agent Manually via Evaluative Reinforcement, or TAMER, which proposed new methods for robots or computer programs to learn how to perform tasks by interacting with a human instructor.
Such techniques lack ways of representing causal relationships (...) have no obvious ways of performing logical inferences, and they are also still a long way from integrating abstract knowledge, such as information about what objects are, what they are for, and how they are typically used.
systems, like Watson (...) use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning.'
As an alternative to this emphasis on the limits of deep learning, one author speculated that it might be possible to train a machine vision stack to perform the sophisticated task of discriminating between 'old master' and amateur figure drawings, and hypothesized that such a sensitivity might represent the rudiments of a non-trivial machine empathy.
In further reference to the idea that artistic sensitivity might inhere within relatively low levels of the cognitive hierarchy, a published series of graphic representations of the internal states of deep (20-30 layers) neural networks attempting to discern within essentially random data the images on which they were trained
Learning a grammar (visual or linguistic) from training data would be equivalent to restricting the system to commonsense reasoning that operates on concepts in terms of grammatical production rules and is a basic goal of both human language acquisition
Such a manipulation is termed an “adversarial attack.” In 2016 researchers used one ANN to doctor images in trial and error fashion, identify another's focal points and thereby generate images that deceived it.
Another group showed that certain psychedelic spectacles could fool a facial recognition system into thinking ordinary people were celebrities, potentially allowing one person to impersonate another.
ANNs can however be further trained to detect attempts at deception, potentially leading attackers and defenders into an arms race similar to the kind that already defines the malware defense industry.
ANNs have been trained to defeat ANN-based anti-malware software by repeatedly attacking a defense with malware that was continually altered by a genetic algorithm until it tricked the anti-malware while retaining its ability to damage the target.
What is representation learning in deep learning?
Your system may work something like this: Input - An image Representation - No of corners in the image (you might use tools like openCV) Model - Gets an input representation or feature (e.g.
You would realise that designing features gets not just difficult, time consuming and requires a deep domain expertise as you start working with real world use-cases.
It is observed that designing features is a complex process and the way to solve that is how our brain is able to design these features.
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In addition to an informed, working definition of machine learning (ML), we aim to provide a succinct overview of the fundamentals of machine learning, the challenges and limitations of getting machine to ‘think’, some of the issues being tackled today in deep learning (the ‘frontier’
We combed the Internet to find five practical definitions from reputable sources: We sent these definitions to experts whom we’ve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own.
Dr. Danko Nikolic, CSC and Max-Planck Institute: (edit of number 2 above): “Machine learning is the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own.” Dr. Roman Yampolskiy, University of Louisville: Machine Learning is the science of getting computers to learn as well as humans do or better.
Merging chrominance and luminance using Convolutional Neural Networks There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task you’re trying to accomplish and the type and amount of data that you have available.
Teams competing for the 2009 Netflix Price found that they got their best results when combining their learners with other team’s learners, resulting in an improved recommendation algorithm (read Netflix’s blog for more on why they didn’t end up using this ensemble).
If you think this way, you’re bound to miss the valuable insights that machines can provide and the resulting opportunities (rethinking an entire business model, for example, as has been in industries like manufacturing and agriculture).
Machine learning is a tool that can be used to enhance humans’ abilities to solve problems and make informed inferences on a wide range of problems, from helping diagnose diseases to coming up with solutions for global climate change.
Dr. Pedro Domingo, University of Washington The two biggest, historical (and ongoing) problems in machine learning have involved overfitting (in which the model exhibits bias towards the training data and does not generalize to new data, and/or variance i.e.
Domingo (and others) emphasize the importance of keeping some of the data set separate when testing models, and only using that reserved data to test a chosen model, followed by learning learning on the whole data set.
learner) is not working, often the quicker path to success is to feed the machine more data, the availability of which is by now well-known as a primary driver of progress in machine and deep learning algorithms in recent years;
This year’s took place in June in New York City, and it brought together researchers from all over the world who are working on addressing the current challenges in deep learning: Deep-learning systems have made great gains over the past decade in domains like bject detection and recognition, text-to-speech, information retrieval and others.
- On Sunday, March 24, 2019
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