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Statistical Learning


New research uses artificial intelligence to identify epileptic seizures in real-time - Mental Daily

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New research in Scientific Reports conducted by Washington University shows how comprehending brain activity as a network rather than by electroencephalography readings, provides more accurate identification of epileptic seizures in real-time. The study, which mixes machine learning with systems theory, was steered by lead author Walter Bomela. "Our technique allows us to get raw data, process it and extract a feature that's more informative for the machine learning model to use," Bomela stated in a news release. "The major advantage of our approach is to fuse signals from 23 electrodes to one parameter that can be efficiently processed with much less computing resources." As explained by researchers, using an EEG, epileptic seizures can be observed through irregular brain activity in the form of spikes and waves during the measurement of electrical output.


A hyperdimensional computing system that performs all core computations in-memory

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Hyperdimensional computing (HDC) is an emerging computing approach inspired by patterns of neural activity in the human brain. This unique type of computing can allow artificial intelligence systems to retain memories and process new information based on data or scenarios it previously encountered. Most HDC systems developed in the past only perform well on specific tasks, such as natural language processing (NLP) or time series problems. In a paper published in Nature Electronics, researchers at IBM Research- Zurich and ETH Zurich presented a new HDC system that performs all core computations in-memory and that could be applied to a variety of tasks. "Our work was initiated by the natural fit between the two concepts of in-memory computing and hyperdimensional computing," Abu Sebastian and Abbas Rahimi, the two lead researchers behind the study, told TechXplore.


Automated histologic diagnosis of CNS tumors with machine learning

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A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body [1]. An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases [2].


Intuitively, How Do Neural Networks Work?

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In my previous article about Intuitively, how can we understand different classification algorithms, I introduced the main principles of classification algorithms. However, the toy data I used was quite simple, almost linearly separable data; in real life, the data is almost always non-linear, so we should make our algorithm able to tackle non linearly separable data. Let's compare how logistic regression behaves with almost linearly separable data and non-linearly separable data. With the two toy data below, we can see that Logistic Regression helps us find the decision boundary when the data is almost linearly separable, but when the data is not linearly separable data, Logistic Regression is not capable to find a clear decision boundary. It is understandable because Logistic Regression is only able to separate the data into two parts.


Roadmap to Natural Language Processing (NLP)

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Natural Language Processing (NLP) is the area of research in Artificial Intelligence focused on processing and using Text and Speech data to create smart machines and create insights. One of nowadays most interesting NLP application is creating machines able to discuss with humans about complex topics. IBM Project Debater represents so far one of the most successful approaches in this area. All of these preprocessing techniques can be easily applied to different types of texts using standard Python NLP libraries such as NLTK and Spacy. Additionally, in order to extrapolate the language syntax and structure of our text, we can make use of techniques such as Parts of Speech (POS) Tagging and Shallow Parsing (Figure 1).


Secure Collaborative XGBoost on Encrypted Data

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Training a machine learning model requires a large quantity of high-quality data. One way to achieve this is to combine data from many different data organizations or data owners. But data owners are often unwilling to share their data with each other due to privacy concerns, which can stem from business competition, or be a matter of regulatory compliance. The question is: how can we mitigate such privacy concerns? Secure collaborative learning enables many data owners to build robust models on their collective data, but without revealing their data to each other.


K-means Clustering from Scratch

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Though there are many library implementations of the k-means algorithm in Python, I decided to use only Numpy in order to provide an instructive approach. Numpy is a popular library in Python used for numerical computations. We first create a class called Kmeans and pass a single constructor argumentk to it. This argument is a hyperparameter. Hyperparameters are parameters that are set by the user before training the machine learning algorithm.


The Data Science Course 2020 Q2 Updated: Part 4 > Python & R

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You will learn both Python and R Programming with Data Science in this course. Python: You will first learn how to Install Anaconda and Jupyter on your desktop/laptop Python: You will understand and learn the basics of For Loops and Advanced For Loops. You will have clarity on Python generators and will master the flow of your code using "If Else" Python: You will understand Why foundations Modify Lists and Dictionaries and Functions. Learn how to analyze, retrieve and clean data with Python Python: Learn Concatenation (Combining Tables) with Python and Pandas and Manipulating Time and Date Data with Python Datetime Python: You will learn to Use Pandas with Large Data Sets, Time Series Analysis and Effective Data Visualization in Python R: You will learn the most important tools in R that will allow you to do data science R: You will have the tools to tackle a wide variety of data science challenges, using the best parts of R. R: You will learn how to Tidy the data. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored.



Introduction to Machine Learning For Beginners [A to Z] 2020

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To provide awareness of the two most integral branches (i.e. To build appropriate neural models from using state-of-the-art python framework. To build neural models from scratch, following step-by-step instructions. To build end - to - end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available. To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.