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Why keep learning?​

In the rapidly advancing world of AI and data science, continuous learning is essential. Did you know that the demand for AI and ML skills has surged by 60% in the last few years? Organisations are looking for experts who can not only apply ML techniques but also understand the mathematical foundations behind them to make well-informed decisions.

Staying ahead in this field means deepening your understanding of these concepts and continuously refining your skills. Don’t let the rapid pace of innovation leave you behind — continue learning and keep expanding your expertise in ML.

Dive deeper: Additional learning materials​

If you're interested, use the following resource to continue exploring topics related to this unit.

Transcript: Additional Mathematical Principles​

Neural Networks and Deep Learning Deep learning models, such as neural networks, use complex mathematical operations to learn patterns in large datasets. Here's an overview of the key concepts and techniques used in these fields:

Mathematical Foundations in NLP and LLMs

Linear Algebra:

  • Word Embeddings: Word embeddings like Word2Vec and GloVe are foundational in NLP for representing words in continuous vector space. The word embedding space is typically represented by a matrix where each row corresponds to a word, and the columns represent different dimensions of meaning.
  • Word2Vec: Uses neural networks to learn vector representations of words by training on large corpora of text. CBOW and Skip-gram are the primary architectures.
  • GloVe: Creates word vectors by factorizing a word co-occurrence matrix.
  • Matrix Factorization: Techniques like SVD are used to decompose large word-context matrices into lower-dimensional matrices that reveal latent semantic structures.
  • Practical Example: king - man + woman = queen (vectors capture semantic relationships).Probability Theory:
  • Language Modeling: Predicting the probability of the next word or sequence of words. This is crucial for speech recognition, text generation, and machine translation. Common approaches include n-gram models and more.