![]() BERT – Bidirectional Encoder Representations from Transformers: a modification of the Transformer architecture by preserving the encoders and discarding the decoders, it relies on masking of words which would then need to be predicted accurately as the training metric.A key concept behind this method is discriminative fine-tuning, where the different layers of the network are trained at different rates. ULM-Fit – Universal Language Model Fine-tuning: a method for fine-tuning any neural-network-based language model for any task, demonstrated in the context of text classification.The cleverly named “attention is all you Need” paper that introduced the attention mechanism also enabled the creation of powerful deep learning language models, like: The transformer model improves this more, by defining a self-attention layer for both the encoder and decoder. This improved sequence-to-sequence model performance by letting the model focus on parts of the input sequence that were the most relevant for the output. Later it was discovered that long input sequences were harder to deal with, which led us to the attention technique. ![]() In 2014, sequence-to-sequence models were developed and achieved a significant improvement in difficult tasks, such as machine translation and automatic summarization. These neural-network-based techniques vectorize words, sentences, and documents in such a way, that the distance between vectors in the generated vector space represents the difference in meaning between the corresponding entities. In 2013, we got the word2vec model and its variants. These techniques achieve state-of-the-art results for the hardest NLP tasks like machine translation. In the past decade (after 2010), neural networks and deep learning have been rocking the world of NLP. In the late 1980s, singular value decomposition (SVD) was applied to the vector space model, leading to latent semantic analysis-an unsupervised technique for determining the relationship between words in a language. The 1970s saw the development of a number of chatbot concepts based on sophisticated sets of hand-crafted rules for processing input information. A major historical NLP landmark was the Georgetown Experiment in 1954, where a set of around 60 Russian sentences were translated into English. NLP was born in the middle of the 20th century. This is just a bit of background about Natural Language Processing, but you can skip on to the projects if you’re not interested. In this article, I’ll help you practice NLP by suggesting 10 great projects you can start working on right now-plus, each of these projects will be a great addition to your resume! Read moreĮxplore more Natural Language Processing articles. ![]() With well-known frameworks like PyTorch and TensorFlow, you just launch a Python notebook and you can be working on state-of-the-art deep learning models within minutes. Whether you’re a developer or data scientist curious about NLP, why not just jump in the deep end of the pool, and learn by doing it? From conversational agents (Amazon Alexa) to sentiment analysis (Hubspot’s customer feedback analysis feature), language recognition and translation (Google Translate), spelling correction (Grammarly), and much more. Already, NLP projects and applications are visible all around us in our daily life. Natural Language Processing (NLP) is a very exciting field.
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