Instructional Theory



'One machine learning model to rule them all': Google open-sources tools for simpler AI ZDNet

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Google researchers have created what they call "one model to learn them all" for training AI models in different tasks using multiple types of training data. Also, models are often trained on tasks from the same "domain", such as translation tasks being trained with other translation tasks. The model it created is trained on a variety of tasks, including image recognition, translation tasks, image captioning, and speech recognition. It also includes a library of datasets and models drawn from recent papers by Google Brain researchers.


'One machine learning model to rule them all': Google open-sources tools for simpler AI ZDNet

#artificialintelligence

Google researchers have created what they call "one model to learn them all" for training AI models in different tasks using multiple types of training data. Also, models are often trained on tasks from the same "domain", such as translation tasks being trained with other translation tasks. The model it created is trained on a variety of tasks, including image recognition, translation tasks, image captioning, and speech recognition. It also includes a library of datasets and models drawn from recent papers by Google Brain researchers.


Google launches open source system to make training deep learning models faster and easier - TechRepublic

@machinelearnbot

Google announced a new open source system Monday that could speed the process for creating and training machine learning models within the firm's TensorFlow library. Google used existing TensorFlow tools to build T2T, and the system will work to define which pieces a user may need to build their deep learning system. It also utilizes a standard interface among all aspects of a deep learning system, including datasets, models, optimizers, and different sets of hyperparameters, the post said. In one example cited by Google, a single deep learning model was successfully able to perform three distinct tasks at once.


Automating Development and Optimization of Machine Learning Models

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Essentially, this is an emerging practice in which data scientists use machine learning tools to accelerate the process of developing, evaluating, and refining machine learning models. In addition to Google's initiative, other noteworthy automated machine learning tools include: As this technology matures and gets commercialized, some fear that it may automate data scientists out of jobs. In other words, expert human judgment will remain essential for ensuring that automation of machine learning development doesn't run off the rails. As I've stated elsewhere, manual quality assurance will always remain essential a core task for which human data scientists will be responsible, no matter how much their jobs get automated.


Boosting the accuracy of your Machine Learning models

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For example, if we had 5 decision trees that made the following class predictions for an input sample: blue, blue, red, blue and red, we would take the most frequent class and predict blue. An overall OOB MSE(mean squared error) or classification error rate can be computed. The basic idea behind boosting is converting many weak learners to form a single strong learner. Adaboost is implemented using iteratively refined sample weights while Gradient Boosting uses an internal regression model trained iteratively on the residuals.


How Facebook Uses Deep Learning Models to Engage Users

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Facebook's Andrew Tulloch says deep learning has enabled the company's news-feed ranking algorithm to capture more nuance in posts, with textual content interpreted by neural network-based natural-language processing programs. Facebook is heavily leveraging deep-learning models to further its user engagement efforts, with the company's Andrew Tulloch noting predictive analytics has become less relevant as more Facebook posts embed video and images, and the volumes of data analyzed grow exponentially. Tulloch says deep learning also has enabled Facebook's news-feed ranking algorithm to capture more nuance in posts, with textual content interpreted by neural network-based natural-language processing programs. For example, Tulloch cites the use of computer-vision, neural-network, deep-learning models to interpret the content of photos posted by users and select those to surface in the "on this day" feature, without spotlighting potentially negative memories.


How Facebook uses deep learning models to engage users

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In particular, this sort of analytics powered the ranked news feed, in which users are shown posts they're likely to find interesting, as determined by an algorithm. In addition to tackling the problem of scale, Tulloch said deep learning allowed Facebook's news-feed ranking algorithm to capture greater subtlety in users' posts. Outside of the news feed, deep learning models are helping Facebook develop products by enabling developers to understand content at a large scale. For example, computer vision neural network deep learning models are used to interpret the content of photos users have posted and decide which to surface in the "on this day" feature.


Building Trust in Machine Learning Models (using LIME in Python)

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With increased trust in predictions, organisations will deploy machine learning models more extensively within the enterprise. Both Logistic Regression & XGBoost predicts that type 2 has a higher probability. Depending on the actual value of the features for a particular record and the weights assigned to those features, the algorithm computes the class probability and then predicts the class having the highest probability. If LIME or similar algorithms can help in providing interpretable output for any type of blackbox algorithm, it will go a long way in getting the buy-in from business users to trust the output of machine learning algorithms.


Train and evaluate custom machine learning models of Watson Developer Cloud - BISILO

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Natural Language Classifier (NLC), Watson Conversation, and Visual Recognition services allow developers to train custom ML models by providing example text utterances (NLC and Conversation) and example images (VR) for a defined set of classes (or intents). Furthermore, for custom entity and relation extraction from text, IBM Watson offers Watson Knowledge Studio, a SaaS solution designed to enable Subject Matter Experts (SMEs) to train custom statistical machine learning models for extracting domain-specific entities and relations from text. To help address these questions and enable our partners and clients to exercise the full power of WDC customization capabilities, we've published WDC Jupyter notebooks that report commonly used machine learning performance metrics to judge the quality of a trained model. Specifically, the WDC Jupyter notebooks report machine learning metrics that include accuracy, precision, recall, f1-score, and confusion matrix.