In this chapter, we took a look at how to integrate fastText word vectors into either linear machine learning models or deep learning models created in Keras, TensorFlow, and PyTorch. You also saw how word vectors can be easily assimilated into existing neural architectures that you might be using in your business application. If you are initializing the embeddings from random values, I would highly recommend that you try to initialize them using fastText values, and then see whether there are performance improvements in your model.
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Joydeep Bhattacharjee is a Principal Engineer who works for Nineleaps Technology Solutions. After graduating from National Institute of Technology at Silchar, he started working in the software industry, where he stumbled upon Python. Through Python, he stumbled upon machine learning. Now he primarily develops intelligent systems that can parse and process data to solve challenging problems at work. He believes in sharing knowledge and loves mentoring in machine learning. He also maintains a machine learning blog on Medium.
Read more about Joydeep Bhattacharjee
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Joydeep Bhattacharjee is a Principal Engineer who works for Nineleaps Technology Solutions. After graduating from National Institute of Technology at Silchar, he started working in the software industry, where he stumbled upon Python. Through Python, he stumbled upon machine learning. Now he primarily develops intelligent systems that can parse and process data to solve challenging problems at work. He believes in sharing knowledge and loves mentoring in machine learning. He also maintains a machine learning blog on Medium.
Read more about Joydeep Bhattacharjee