Tensors for topic modeling and deep learning on AWS Sagemaker

Editor's Notes

• #3: Document, image, forecasting
• #4: Document, image, forecasting
• #5: Document, image, forecasting
• #6: Document, image, forecasting
• #13: End-to-End Machine Learning Platform Amazon SageMaker offers a familiar integrated development environment so that you can start processing your training dataset and developing your algorithms immediately. With one-click training, Amazon SageMaker provides a distributed training environment complete with high-performance machine learning algorithms, and built-in hyperparameter optimization for auto-tuning your models. When you’re ready to deploy, launching a secure and elastically scalable production environment is as simple as clicking a button in the Amazon SageMaker management console.   Zero Setup Amazon SageMaker provides hosted Jupyter notebooks that require no setup, so you can begin processing your training datasets and developing your algorithms immediately. With a few clicks in the Amazon SageMaker console, you can create a fully managed notebook instance, pre-loaded with useful libraries for machine learning and deep learning frameworks like TensorFlow, and Apache MXNet. You need only add your data. Flexible Model Training With native support for bring-your-own-algorithms and frameworks, model training in Amazon SageMaker is flexible. Amazon SageMaker provides native Apache MXNet and TensorFlow support, and offers a range of built-in, high performance machine learning algorithms, in addition to supporting popular open source algorithms. If you want to train against another algorithm or with an alternative deep learning framework, you simply bring your own algorithms or deep learning frameworks via a Docker container. Pay by the second With Amazon SageMaker , you pay only for what you use. Authoring, training, and hosting is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and hosting instances.
• #14: The result of this is 1)      Linear Learner - Regression 2)      Linear Learner - Classification 3)      K-means 4)      Principal Component Analysis 5)      Factorization Machines 6)      Neural Topic Modeling 7)      Latent Dirichlet Allocation 8)      XGBoost 9)      Seq2Seq 10)  Image classification (ResNet)
• #15: Highly-optimized Machine Learning Algorithms Amazon Iron Man installs high-performance, scalable machine learning algorithms optimized for speed, scale, and accuracy, to run on extremely large training datasets. Based on the type of learning that you are undertaking, you can choose from supervised algorithms, such as linear/logistic regression or classification; as well as unsupervised learning, such as with k-means clustering. Linear Classification and Regression Factorization Machines K-Means Clustering Principal Components Analysis (PCA) Latent Dirichlet Analysis (Spectral LDA) Neural Topic Modeling Time-series forecasting (DeepAR)
• #45: 44