Learn how to deal with analyzing, processing text and build models that can understand the human language in Python using TensorFlow and many other frameworks.
Learn how you can perform named entity recognition using HuggingFace Transformers and spaCy libraries in Python.
Exploring the fake news dataset, performing data analysis such as word clouds and ngrams, and fine-tuning BERT transformer to build a fake news detector in Python using transformers library.
Explore different pre-trained transformer models in transformers library to paraphrase sentences in Python.
Learn how you can generate any type of text with GPT-2 and GPT-J transformer models with the help of Huggingface transformers library in Python.
Learn how to perform automatic speech recognition (ASR) using wav2vec2 transformer with the help of Huggingface transformers library in Python
Learn how to use Huggingface transformer models to perform machine translation on various languages using transformers and PyTorch libraries in Python.
Learn how you can pretrain BERT and other transformers on the Masked Language Modeling (MLM) task on your custom dataset using Huggingface Transformers library in Python
Learn how to use Huggingface transformers library to generate conversational responses with the pretrained DialoGPT model in Python.
Learn how to use HuggingFace transformers library to fine tune BERT and other transformer models for text classification task in Python.
Learn how to use Huggingface transformers and PyTorch libraries to summarize long text, using pipeline API and T5 transformer model in Python.
Learn how you can easily perform sentiment analysis on text in Python using vaderSentiment library.
Learn how to make a language translator and detector using Googletrans library (Google Translation API) for translating more than 100 languages with Python.
Building deep learning models (using embedding and recurrent layers) for different text classification problems such as sentiment analysis or 20 news group classification using Tensorflow and Keras in Python
Building a deep learning model to generate human readable text using Recurrent Neural Networks (RNNs) and LSTM with TensorFlow and Keras frameworks in Python.