1.【博客】10 Deep Learning projects based on Apache MXNet
Apache MXNet is an Open Source library helping developers build, train and run Deep Learning models. In previous articles, I introduced you to its API and its main features.
In this article, I will focus on 10 Open Source projects applying MXNet to various use cases:
- Deployment using Docker containers or Lambda functions,
- Face recognition & detection,
- Object detection & classification,
- Optical character recognition,
- Machine translation.
- 【博客】How to Train a Final Machine Learning Model
In this post, you discovered how to train a final machine learning model for operational use. You have overcome obstacles to finalizing your model, such as:
- Understanding the goal of resampling procedures such as train-test splits and k-fold cross validation.
- Model finalization as training a new model on all available data.
- Separating the concern of estimating performance from finalizing the model.
3.【博客】Generative Machine Learning on the Cloud
In the last year we’ve witnessed rapid advancements in hardware capabilities, continued development of user-friendly machine learninglibraries, and AI connectivity for the maker community. With this backdrop of increasingly user-friendly AI, I spent the summer working with Google’s Artists & Machine Intelligence (AMI) program on a cloud-based tool to make generative machine learning and synthetic image generation more accessible, especially to artists and designers. This post will explain some common generative model structures as well as pitfalls and resources for people interested in coding their own.
Before I get to the project, a little about me.
4.【博客】Deep Learning: Language identification using Keras & TensorFlow
Welcome to my second Data Science project. This time we will dive into the most recent & hot technology: Deep Neural Networks (DNN).
The problem I am going tackle here is the following: can we identify the language of short text (140 characters) with high accuracy using neural networks? This problem is currently solved by various software libraries, but using a set of hardcoded rules and lookup tables. We will attack this problem using Machine Learning algorithms.
This is the trainable tool that can be used to automate the categorization process of images. It expects that you provide some categorized examples (it works pretty good given just 10 examples) on which it will be trained on and then it categorize data automatically.