Technical requirements
To successfully navigate through this chapter, certain technical prerequisites are necessary, as follows:
- Programming knowledge: A strong understanding of Python is essential, as it’s the primary language used for most DL and NLP libraries.
- Machine learning fundamentals: A good grasp of basic ML concepts such as training/testing data, overfitting, underfitting, accuracy, precision, recall, and F1 score will be valuable.
- DL basics: Familiarity with DL concepts and architectures, including neural networks, backpropagation, activation functions, and loss functions, will be essential. Knowledge of RNNs and CNNs would be advantageous but not strictly necessary as we will focus more on transformer architectures.
- NLP basics: Some understanding of basic NLP concepts such as tokenization, stemming, lemmatization, and word embeddings (such as Word2Vec or GloVe) would be beneficial.
- Libraries and frameworks: Experience with libraries such as TensorFlow and PyTorch for building and training neural models is crucial. Familiarity with NLP libraries such as NLTK or SpaCy can also be beneficial. For working with BERT specifically, knowledge of the
transformerslibrary from Hugging Face would be very helpful. - Hardware requirements: DL models, especially transformer-based models such as BERT, are computationally intensive and typically require a modern graphics processing unit (GPU) to train in a reasonable amount of time. Access to a high-performance computer or cloud-based solutions with GPU capabilities is highly recommended.
- Mathematics: A good understanding of linear algebra, calculus, and probability is helpful for understanding the inner workings of these models, but most of the chapter can be understood without in-depth mathematical knowledge.
These prerequisites are intended to equip you with the necessary background to understand and implement the concepts discussed in the chapter. With these in place, you should be well-prepared to delve into the fascinating world of DL for text classification using BERT.
