Welcome to my new series on the Evolution of Text Processing, also known as Natural Language Processing (NLP). 🌟
In today's computational landscape, GEN AI is a hot topic, particularly with models like ChatGPT, BERT, Gemini, Gemma, and others. Yet, this advancement is not sudden but the outcome of a long and gradual evolution in text processing that has developed over many decades.
From understanding simple commands to generating human-like text, NLP has transformed the way machines interact with human language. But how did we get here? This series will take you on a journey through the fascinating history of NLP, exploring key milestones and technological advancements that have shaped this field. What to Expect:
- Rule-Based Systems: The early days of NLP, where handcrafted rules and lexicons were the norm.
- Statistical Methods: The shift towards probabilistic models and statistical techniques.
- Machine Learning: The introduction of algorithms that enabled more sophisticated text analysis.
- Deep Learning: How neural networks and word embeddings revolutionized NLP.
- Encoder-Decoder Architectures: The framework that brought breakthroughs in tasks like machine translation.
- Generative Adversarial Networks (GANs): Innovative approaches to text generation.-
- Modern Models (GPT, BERT, T5): The rise of transformer-based models that set new benchmarks.
- Reinforcement Learning from Human Feedback (RLHF): Enhancing models with human feedback.
- Direct Preference Optimization (DPO): A new paradigm for aligning models with human preferences.
- Future Trends: What’s next for NLP and the challenges ahead.
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