Inductive Learning

Pseudo-labeling a simple semi-supervised learning method - Data, what now?

In this post, I will show how a simple semi-supervised learning method called pseudo-labeling that can increase the performance of your favorite machine learning models by utilizing unlabeled data. First, train the model on labeled data, then use the trained model to predict labels on the unlabeled data, thus creating pseudo-labels. In competitions, such as ones found on Kaggle, the competitor receives the training set (labeled data) and test set (unlabeled data). Pseudo-labeling allows us to utilize unlabeled data while training machine learning models.

Artificial Intelligence - Teaching Itself - Disruption Hub

Possibly on of the most important parts of building an effective Artificial Intelligence is to feed it information from diverse data sources. Supervised learning techniques have built artificially intelligent software that can provide in depth business analytics, predict consumer behaviour, translate different languages, read emotions, drive a car and, of course, play chess. In HealthTech, health trackers and virtual doctors could account for patients' emotional state, improving customer experience. Is DeepMind's study a step towards technological singularity?

Understanding overfitting: an inaccurate meme in Machine Learning

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 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.

It's a machine learning's world! Thorat and Smilkov, both software engineers in the Big Picture team at Google revealed in a blog post announcing deeplearn.js And if nothing else, the browser is one of the world's most popular programming platforms." Web machine learning libraries are hardly a novelty but one of the biggest disadvantages is that they have been either limited by the speed of Javascript or restricted to inference. The software engineers explained that the API imitates the structure of TensorFlow and NumPy, with a delayed execution model for training (like TensorFlow), and an immediate execution model for inference (like NumPy).

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.