Instructional Theory


The hidden horse power driving Machine Learning models

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This will typically learn in 100 epochs fairly good recommendations for movies. Companies are starting to offer hardware that can be situated close to the data production (in terms of network speed) for machine learning. It is for this reason that companies are starting to offer hardware that can be situated close to the data production (in terms of network speed) for machine learning. To get an idea of its speed, a researcher loaded up the Imagenet 2012 dataset and trained a Resnet50 machine learning model on the dataset.


Public Data Sets: Use these to train Machine Learning models on Mateverse

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To get you started with Machine Learning. The ML platform which enables you to build and train customized models without writing even a single line of code. This is the first in the series, and we are planning to make a lot more data sets public in the coming days, be it from the community or something we'll make.


Train your Deep Learning models on the Cloud

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Step 2) Choose Instance type In this step choose GPU instances. Step 3) Configure the instance Step 4) Add Storage Step 5) Add tags In the above steps, configure each of them according to your requirement. During the recent I/O '17 Google has rebranded itself as an AI first company and also unveiled their TPU cloud platform for training deep learning models. After downloading the cuDNN file, upload the file to the cloud using the interface provided in the terminal for uploading files to the instance.



Book: Evaluating Machine Learning Models

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If you're new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. With this O'Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics. Alice is a technical leader in the field of Machine Learning. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University.


A Primer on Machine Learning Models for Fraud Detection - Simility

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One area of machine learning that's getting a lot of buzz in recent years is artificial neural networks (ANNs), aka "deep learning" models, which try to simulate how layers of neurons act together in the brain to make a decision. ANN models are highly versatile and can be used to solve highly complex problems like identifying account takeover using the device's sensor data. While other techniques often require limiting the number of features, multi-layer ANNs can train on thousands of features and scale easily. Training such models requires massive amounts of data (typically, millions of labeled transactions), so deep learning models are really only practical for large companies or those that generate a lot of data points.



'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

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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.