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Deep Learning VM Images

Imagine if you could avoid the headache of setting up new libraries, configuring them, and making sure they are all compatible. In this episode of AI Adventures, Yufeng shows you how to take advantage of deep learning VM images on Google Compute Engine to make setting up new environments a piece of cake.  

Getting Started with TensorFlow.js

TensorFlow.js is an ecosystem of JavaScript based tools for training and deploying machine learning models. In this episode of AI Adventures, learn all about getting started with Tensorflow.js through tutorials like training a convolutional neural network in your browser and building a Pac-Man game that’s played with data from your webcam! This is only a beginning… stay tuned for deep dives on TensorFlow.js coming soon!  

Scaling up Keras with Estimators (AI Adventures)

When you convert a Keras model to a TensorFlow Estimator, you get the best of both worlds: easy to read Keras model syntax along with distributed training with TensorFlow. In this episode of AI Adventures, Yufeng shows you how to scale up a Keras model with estimators so that it can run larger datasets or across many machines. Plus, it makes it easy to do model serving once the training is complete!  

Getting Started with Keras (AI Adventures)

Getting started with Keras has never been easier! Not only is it built into TensorFlow, but when you combine it with Kaggle Kernels you don’t have to install anything! Plus you get to take advantage of the resources from the Kaggle community. In this episode of AI Adventures, Yufeng shows you how to get started with Keras. Take a look!  

Serving Scikit-learn Models at Scale

Scikit-learn is a great tool for building your models. When it comes time to deploy them to prediction, scale up using Google Cloud ML Engine. In this episode of AI Adventures, Yufeng shows you how to set up your own deployment pipeline with scikit-learn so you can go back to focusing on tuning your model!  

Learning Scikit-Learn (AI Adventures)

Scikit-learn is a well-documented and well-loved Python machine learning library. The library is maintained and reliable, offering a vast collection of machine learning algorithms for you to incorporate into your projects. If you haven’t tried scikit-learn, you definitely should!   In this episode if AI Adventures, Yufeng gives an overview of scikit-learn and shows an example of scikit-learn in a kaggle kernel.  

AutoML Vision – Part 1 (AI Adventures)

In this episode of AI Adventures, Yufeng Guo uses AutoML Vision to build and employ a machine learning model that recognizes different types of….chairs! In part 1, he’ll walk you through gathering the data and creating a csv file that describes the location and label for all the images in the dataset. Don’t miss part 2 to see how the model performs!  

BigQuery and Open Datasets

We all love data. But it can be hard to make practical use of large datasets. In this episode of AI Adventures, Yufeng Guo introduces BigQuery public datasets, which allow you to query huge datasets with great responsiveness without needing to worry about the storage costs. Time to break out your big data toolbox, because these queries are going to be big!  

TensorFlow Object Detection on iOS (AI Adventures)

In this interview of AI Adventures, Yufeng interviews Developer Advocate Sara Robinson to talk about a custom object detection iOS app she built to detect Taylor Swift. We’ll cover everything from training a model with transfer learning, to serving the model in the cloud, to making prediction requests to the model from an iOS device (in Swift!).  

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