Sentiment Analysis, Model Learning. If you ever thought that machine learning is hard, check this article.
Sentiment Analysis is used to predict moody attitude of the text. Today we will talk about building neural network architectures and finding the best one in our purpose. As you probably know, there are several classes of artificial neural networks, for example the RNN and the CNN. Here we will focus on the recurrent neural network, which work the best for the text operations. I assume that you probably know, how to speed up the learning process. To do this scenario we are using a GPU computing. Unfortunately in my PC I don’t have such powerful GPU, only the 1660 Ti. It’s okay, but it has some limitation, that’s why I decided to use the colab service. For this purpose I want to train our model on the whole data. Sadly the ordinary colab have the limitation to use it for maximum 12h in a row. Taking it into account, I decided to invest in the colab pro. At least it cost only $9.99/month.
Training Neural Networks for price prediction with TensorFlow: Learn how to make your DNN more efficient in solving regression problems: a practical guide with TensorFlow and Keras.
This chapter continues the series on Bayesian deep learning. In the chapter we’ll explore alternative solutions to conventional dense neural networks.
Learn Machine Learning with Python using neural networks with this machine learning beginners course. In this tutorial we will look at taking an existing sol...
Learn how to easily build, train and validate a Recurrent Neural Network. I’ll assume you have a basic understanding of what I’m going to talk in the next lines. I’ll be stacking layers of concepts as I move forward, keeping a very low-level language — don’t worry if you fell a little lost between lines, later I will probably clarify your doubts.