Face Recognition API with OCI – Part 1

You may have noticed that I’m studying Machine Learning (including Deep Learning). In my study I like to implement codes from other developers or create some codes from scratch. If you also study about facial recognition you may have heard about Facenet. Facenet is a Tensorflow implementation for face recognition that you can integrate into your projects, and I used it to create my Face Recognition API. In this first article, you will learn how to use my facial recognition API with […]

Exposing Keras as REST API

In my last blog post about Keras, you learned how to use the Kaggle dogs-vs-cats dataset. But would you like your friends to use your model to identify dogs and cats in pictures? Yes, this blog post is about it! You’ll learn how to expose your model as a REST API in a simple way. Lets go! We’ll use the code created in “Using Kaggle datasets” by adding a modification to save the model. Then, download all the files and run the jupyter notebook to train and save your model. […]

Installing Anaconda in OCI GPU instance

Now that you know how to create an Oracle Cloud Infrastructure GPU instance, the next steps are install Anaconda and use Jupyter Notebook to develop or test your AI projects. First of all, go to your Oracle Cloud Account and add the following Ingress Rule in your Security List (Networking > Virtual Cloud Networks > Virtual Cloud Network Details > Security Lists > Security List Details). Using a Terminal, access your Ubuntu instance and download the latest version of Ananconda. […]

CPU vs GPU in Oracle Cloud

If you read my blog post called “Optimizing TensorFlow for CPU“, you learned that you can improve TensorFlow for CPU by just choosing the correct distribution, in this case the Anaconda distribution. CPU instances will do the work for simple AI projects, but if you need more computing power to reduce the execution or training time of your project, you need to use GPU instances. Since many people have asked me to run the same test using GPU instances, in this post you will see the […]

Getting Started with GPU instances in Oracle Cloud

If you are working with artificial intelligence (machine learning or deep learning) projects at some point, you will need to change your CPU instances to GPU instances speed up the training of your models. Nowadays, most cloud providers offer GPU as a service and you can use it to speed up your projects. Oracle Cloud offers the two most advanced GPU models for your choice: NVIDIA V100 and NVIDIA P100. In this blog post, you’ll learn how to request and create GPU instances in the Oracle […]

Using Kaggle datasets

Even if you are a beginner in machine learning, you’ve probably heard about Kaggle. Kaggle is an online community of data scientists and machine learners. Kaggle allows users to find and publish datasets, explore and build models, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. In short, Kaggle is the right place to learn and practice machine learning. In this blog post you will learn how to use one of the many […]

Convolutional Neural Networks with Keras

In the last blog post, using Keras, you learned how to create a simple neural network (Multilayer Perceptron) using Keras that classified as MNIST dataset images with 97% accuracy. You may think 97% is a good number, but you can still improve it. Using Convolutional Neural Networks (or Convnets), a type of deep learning model most commonly applied for the analysis of visual images, it is possible to achieve about 99% accuracy in training and validation. You can clone this project here: https://github.com/waslleysouza/keras. […]