100 Best Tensorflow NLP Videos

Notes:

TensorFlow is a powerful open-source software library for machine learning that can be used for natural language processing (NLP) tasks such as tokenization, text classification, and language modeling. It can be used to build and train deep neural networks for NLP tasks using techniques such as word embedding, language modeling, and part-of-speech tagging. Additionally, pre-trained models from TensorFlow Hub can be used for transfer learning in NLP, making it easy to implement end-to-end NLP applications. TensorFlow can also be used in conjunction with other libraries such as Keras and gensim to improve NLP performance.

TensorFlow is a powerful open-source software library for machine learning developed by Google, and Keras is a high-level neural networks API written in Python and capable of running on top of TensorFlow. TensorFlow can be used as the backend for Keras, which means that when you use the functions and classes provided by the Keras library, they will be executed using TensorFlow functions under the hood. This allows developers to use the high-level, user-friendly API provided by Keras while still taking advantage of the low-level functionality and flexibility offered by TensorFlow. Additionally, Keras provides a simplified interface for working with neural networks and other machine learning models, making it a popular choice for deep learning tasks such as natural language processing (NLP).

PyTorch and TensorFlow are both popular open-source deep learning frameworks, but they have some key differences.

PyTorch has a simpler, more intuitive interface and a more dynamic computational graph, which makes it easier to debug and experiment with models.
TensorFlow has a more robust ecosystem and a wider range of tools and libraries for deploying models to production, such as TensorFlow Serving and TensorFlow Lite.
TensorFlow also has more support for mobile and web deployment, while PyTorch is mainly used for research and development.
PyTorch has a more “Pythonic” feel, while TensorFlow is more verbose and can be more difficult to read and write.

Transfer learning is a machine learning technique where a model trained on one task is reused as the starting point for a model on a second related task. The goal of transfer learning is to transfer knowledge learned from one task to improve learning in a second task. This can be done by using the learned features from a pre-trained model as a starting point for training a new model, or by fine-tuning a pre-trained model on a new dataset. This can be a useful technique when there is a shortage of labeled data for a particular task, and can lead to faster training times and improved performance.

Resources:

packtpub.com .. online library and learning platform for professional developers
tfhub.dev .. searchable repository of trained machine learning models

Wikipedia: