The goal here is similar, make the rest of the network learn a common representation, while making the normalization parameters learn language specific semantics. The One-to-Many and Many-to-One models are trained for English to French, German, Italian and Spanish Translation and Vice Versa. The Many to Many model is trained on English-French, French-English, English-German and German-English. The image stylization paper specifies how a N-style network can pick up an N 1th style through fine-tuning an existing model. Similarly, I fine-tune my Many-to-Many model to pick up Portuguese.
We will use Google Drive to save our checkpoints (a checkpoint is our last saved trained model). Once our trained model is saved we can load it whenever we want to generate both conditional and unconditional texts. Now that you have your Google Drive connected let's create a checkpoints folder: Now let's clone the GPT-2 repository that we will use, which is forked from nnsheperd's awesome repository (which is forked from OpenAI's but with the awesome addition of train.py), It also lets us avoid using bash-code. Now let's download our model of choice.
File Handling in Google Colab for Data Science In this video, I will show you how to handle files on Google Colab where you will be able to read, write, copy, move and download files (from the internet and Google Drive) using Bash and Python language. This video is part of the [Python Data Science Project] and [Data Science 101] series. If you're new here, it would mean the world to me if you would consider subscribing to this channel. Disclaimer: Chanin is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to http://www.amazon.com.
Google Colab is one of the most famous cloud services for seasoned data scientists, researchers, and software engineers. While Google Colab seems easy to start, some things are difficult to use. There are several benefits of using Colab over using your own local machines. To create a new Notebook on Colab, open https://colab.research.google.com/, Here you can click on NEW NOTEBOOK to start a new notebook and start running your code in it.
It's time to make our hands dirty with a hands-on face detection model using MediaPipe. To perform face detection you have to install MediaPipe at first in your machine. If you are a windows user then you can run the below code in your computer's command prompt. You also need to install OpenCV for webcam or image input. If you are a windows user, you can run the below code in your command prompt.