Last time, I implemented an organism that selected the maximum intensity around it and moved on. By intensity, I mean a circular gradient; the organism's task is to reach the maximum point in the shortest way possible. There was only one center of intensity, and consequently, there was only one path of upward movement along the increasing gradient.
In the realm of sentiment analysis, there are two primary approaches, supervised and unsupervised learning. Supervised learning means you need a labeled dataset to train a model, while unsupervised learning does not depend on labeled data.
The Google-developed open-source software package TensorFlow is used to create and train machine learning models. Because it functions particularly well with large-scale neural networks, it has become one of the most extensively used machine learning frameworks.
I set a research goal for myself: to understand the most optimal decision-making algorithms at each level of task-setting, and to move from simpler solutions to more and more complex ones as the external environment becomes more complicated.
I have worked on a number of NLP projects and after collecting the data the biggest challenge is the pre-processing. Since the text data available on the internet is often highly unstructured, contains unwanted symbols, repeated characters, contains different forms of the same root word, etc.
I’ve worked as a web developer for a while, and most times, I’m amazed at how web apps such as Facebook and Instagram are able to detect and recognize objects in images. I reckon a machine learning model like MobileNet makes this possible, but how do you deploy this model to the web?
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