After much hype, Google finally released TensorFlow 2.0 which is the latest version of Google's flagship deep learning platform. A lot of long-awaited features have been introduced in TensorFlow 2.0. This article very briefly covers how you can develop simple classification and regression models using TensorFlow 2.0.
Tag: machine learning
In machine learning, the performance of a model only benefits from more features up until a certain point. The more features are fed into a model, the more the dimensionality of the data increases. As the dimensionality increases, overfitting becomes more likely.
There are multiple techniques that can be
Time series data, as the name suggests is a type of data that changes with time. For instance, the temperature in a 24-hour time period, the price of various products in a month, the stock prices of a particular company in a year. Advanced deep learning models such as Long
PyTorch and TensorFlow libraries are two of the most commonly used Python libraries for deep learning. PyTorch is developed by Facebook, while TensorFlow is a Google project. In this article, you will see how the PyTorch library can be used to solve classification problems.
Classification problems belong to the category
Nowadays, we have huge amounts of data in almost every application we use - listening to music on Spotify, browsing friend's images on Instagram, or maybe watching an new trailer on YouTube. There is always data being transmitted from the servers to you.
This wouldn't be a problem for
This is the 21st article in my series of articles on Python for NLP. In the previous article, I explained how to use Facebook's FastText library for finding semantic similarity and to perform text classification. In this article, you will see how to generate text via deep learning technique in
This is the second and final part of the two-part series of articles on solving sequence problems with LSTMs. In the part 1 of the series, I explained how to solve one-to-one and many-to-one sequence problems using LSTM. In this part, you will see how to solve one-to-many and many-to-many