Inductive Learning

Understanding overfitting: an inaccurate meme in supervised learning


It seems like, a kind of an urban legend or a meme, a folklore is circulating in data science or allied fields with the following statement: Applying cross-validation prevents overfitting and a good out-of-sample performance, low generalisation error in unseen data, indicates not an overfit. Aim In this post, we will give an intuition on why model validation as approximating generalization error of a model fit and detection of overfitting can not be resolved simultaneously on a single model. Let's use the following functional form, from classic text of Bishop, but with an added Gaussian noise $$ f(x) sin(2\pi x) \mathcal{N}(0,0.1).$$ We generate large enough set, 100 points to avoid sample size issue discussed in Bishop's book, see Figure 2. Overtraining is not overfitting Overtraining means a model performance degrades in learning model parameters against an objective variable that effects how model is build, for example, an objective variable can be a training data size or iteration cycle in neural network.

How to Analyze Tweet Sentiments with PHP Machine Learning -- SitePoint


Machine learning is something of an umbrella term that covers many generic algorithms for different tasks, and there are two main algorithm types classified on how they learn – supervised learning and unsupervised learning. In supervised learning, we train our algorithm using labelled data in the form of an input object (vector) and a desired output value; the algorithm analyzes the training data and produces what is referred to as an inferred function which we can apply to a new, unlabelled dataset. We don't know the desired output values of the dataset and we are letting the algorithm draw inferences from datasets; unsupervised learning is especially handy when doing exploratory data analysis to find hidden patterns in the data. One of the key requirements needed to build successful machine learning projects is a decent starting dataset.

Top 3 machine learning libraries for Python


A 2016 paper, Theano: A Python framework for fast computation of mathematical expressions, provides a thorough overview of the library. In the first Open Source Yearbook, TensorFlow was picked as a project to fork in 2016. We also learned about TensorFlow-based project Magenta in an article by Josh Simmons, A tour of Google's 2016 open source releases. Simmons says Magenta is an effort to advance the state of the art in machine intelligence for music and art generation, and to build a collaborative community of artists, coders, and machine-learning researchers.

5 best machine learning libraries for Java ( NEW )


Weka is a collection of machine learning algorithms for data mining tasks. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. The list of products include: RapidMiner Studio, RapidMiner Server, RapidMiner Radoop, and RapidMiner Streams. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

Inside the 2017 Data Scientist Report


Diego leads the design and implementation of supervised learning systems at Hello Digit. He specializes in building scalable perpetual-learning pipelines leveraging human-in-the-loop techniques. Randi is a storyteller, mom, knitting enthusiast, Kellogg and Dartmouth grad, and VP of Marketing at CrowdFlower, the human-in-the-loop platform for data science and machine learning teams making AI work.

Generative Models & Variational AutoEncoder Explained – Frank's World


The ever-increasing size of modern datasets combined with the difficulty of obtaining labeled information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. VAE offers a novel way to enforce structure on the representation surface, by doing so, it opens the possibility of employing traditional semi supervised learning techniques on the structured embedding space. In this talk, Shair Harel covers how VAE imposes latent space structure constraint, and how we can use it in a semi-supervised settings.

Improving Predictions with Ensemble Model


Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.

Announcing Microsoft Machine Learning Library for Apache Spark


We're excited to announce the Microsoft Machine Learning library for Apache Spark – a library designed to make data scientists more productive on Spark, increase the rate of experimentation, and leverage cutting-edge machine learning techniques – including deep learning – on very large datasets. However, they struggle with low-level APIs, for example to index strings, assemble feature vectors and coerce data into a layout expected by machine learning algorithms. Microsoft Machine Learning for Apache Spark (MMLSpark) simplifies many of these common tasks for building models in PySpark, making you more productive and letting you focus on the data science. With MMLSpark, we provide easy-to-use Python APIs that operate on Spark DataFrames and are integrated into the SparkML pipeline model.

Physicists uncover similarities between classical and quantum machine learning


This finding helps in establishing the ultimate capabilities of quantum learning algorithms, and opens the door to applying key results in statistical learning to quantum scenarios." The scientists showed that both classical and quantum inductive supervised learning algorithms must have these two phases (a training phase and a test phase) that are completely distinct and independent. By revealing this similarity, the new results generalize some key ideas in classical statistical learning theory to quantum scenarios. "Inductive supervised quantum learning algorithms will be used to classify information stored in quantum systems in an automated and adaptable way, once trained with sample systems," Sentís said.

JPMorgan's massive guide to machine learning jobs in finance


Machine learning has various iterations, including supervised learning, unsupervised learning and deep and reinforcement learning. The purpose of deep learning is to use multi-layered neural networks to analyze a trend, while reinforcement learning encourages algorithms to explore and find the most profitable trading strategies. In a finance context, J.P. Morgan says supervised learning algorithms are provided with provided historical data and asked to find the relationship that has the best predictive power. If you're only planning to learn one coding language related to machine learning, J.P. Morgan suggests you choose R, along with the related packages below.