Welcome to our comprehensive Natural Language Processing Bootcamp! This course is a part of our Deep Learning Bootcamp running for the year 2024. This intensive program is designed to take you from the fundamentals to advanced techniques in natural language processing, equipping you with the skills to tackle real-world language processing tasks.
Our bootcamp is structured into four key sections, each building upon the previous to create a robust understanding of natural language processing techniques:
The Natural Language Toolkit (NLTK) is a powerful Python library used for text processing and working with human language data. It provides easy-to-use tools for tasks like tokenization, stemming, lemmatization, and stop-word removal, helping developers and researchers handle linguistic data efficiently. NLTK is widely used for sentiment analysis, part-of-speech tagging, and creating language models. It is a great starting point for anyone working with natural language processing (NLP). To begin, please navigate to the link mentioned below. Happy Learning!
Section 2: PyTorch and Text Classification with Neural Networks
We'll start by introducing PyTorch, a powerful deep-learning framework. You'll learn how to manipulate tensors, build neural networks, and implement your first text classification model using Artificial Neural Networks (ANNs). Please navigate to the link mentioned below to begin.
Recurrent Neural Networks (RNNs) are designed to handle sequential data by maintaining connections between previous and current information, making them ideal for tasks like speech recognition and language processing. However, RNNs often face challenges when it comes to retaining information over long sequences due to issues like vanishing gradients. To overcome this, Long Short-Term Memory (LSTM) networks were introduced. LSTMs incorporate memory cells and gates that regulate the flow of information, enabling the network to remember important details over longer periods. This improvement allows LSTMs to perform better in tasks requiring long-term contextual understanding.
Transfer learning and fine-tuning are key techniques in modern NLP, particularly when working with large language models (LLMs) on the Hugging Face platform. Transfer learning allows models like BERT, GPT, and RoBERTa to be pre-trained on massive text corpora, capturing general language patterns. Fine-tuning then adapts these pre-trained models to specific tasks, such as text classification, named entity recognition, or summarization, by training on smaller, task-specific datasets. Hugging Face’s easy-to-use tools and model hub streamline the process, enabling users to fine-tune powerful LLMs with minimal resources and apply them to specialized use cases efficiently.