Master LLM: Large Language Models with Transformers

Explore how lowercasing standardizes text in NLP preprocessing, reducing vocabulary size and improving model generalization. The lecture demonstrates applying the dot lower method in a notebook and discusses text consistency.

Explore tokenization as a crucial NLP pre-processing step that breaks text into word, subword, character, or sentence units and demonstrates practical coding with NLTK to prepare data for models.

Explore how punctuation shapes meaning in NLP, from tokenization to when to remove punctuation for text classification or keep it for chatbots, with code examples.

Explore chat words, abbreviations, slang and emoticons, and learn to map them to full forms with a reusable dictionary, tokenization, and context-aware handling in NLP preprocessing.

Handle emojis in natural language processing by removing them or replacing them with textual descriptions using the emoji package in Python.

Explore stemming as a text normalization technique in NLP, reducing words like run, runs, running to a single root using the Porter stemmer, to improve efficiency and reduce dimensionality.

Explore lemmatization with WordNet Lemmatizer in NLTK, comparing it to stemming to obtain base words that are meaningful, reduce dimensionality, and enhance NLP accuracy.

Explore stopwords in NLP as background noise and when removing them aids text classification, information retrieval, and text summarization, while not removing them for language modeling, NER, or generation.

Explore spelling correction as a key NLP pre-processing step, addressing typographical and cognitive errors with Python-based tools to improve text data quality and model performance.

Master the bag of words text representation by converting text into a numerical word frequency vector, building a vocabulary and applying it to sentiment analysis, document classification, and information retrieval.

Explore how n-grams extend bag-of-words by capturing contextual information with unigram, bigram, and trigram representations and their vectorization using CountVectorizer.

Master one hot encoding, a text representation that converts each unique word into a binary vector whose length equals the number of unique words in the corpus.

Master tf-idf, the term frequency–inverse document frequency, to gauge word importance in a document relative to a corpus for information retrieval and text mining, implemented with Python and scikit-learn.

Learn word2vec, a neural-network approach that generates dense, context-aware word embeddings and captures semantic similarities beyond bag-of-words, tf-idf, and n-grams, via cbow and skip-gram models.

Learn how the continuous bag of words model in word2vec predicts a target word from its context using a neural network, builds one-hot vectors, and derives word embeddings.

Train a cbow word2vec model using gensim on the ABC dataset, set vector size 100, window 3, min_count 3, using cbow (s=0), and save/load the model for later use.

Explore the skip-gram model in word2vec, its hidden-layer architecture that predicts context words from a target word to generate embeddings, and its advantage with rare words compared to cbow.

Learn to use pre-trained word embeddings from open source models like word2vec to boost NLP performance, save training time, and explore vector similarity with gensim.

Explore how the transformer enables self-attention, an encoder-decoder architecture, and positional encoding to overcome rnn and lstm limitations, enabling parallelization and handling long-range dependencies in nlp.

Explore how self-attention in transformers computes query, key, and value vectors to produce contextual embeddings via softmax-weighted sums, enabling dynamic word meaning.

Explore multi-head attention, extending self-attention with multiple parallel heads to capture diverse relationships in sequences. Concatenate outputs and apply a linear transform for final transformer representations.

Learn how positional encoding injects position information into word embeddings to give transformers a sense of order, using sinusoidal functions and additive inputs in encoder and decoder.

Master natural language processing with transformers by understanding the encoder architecture: multi-head attention, residual connections, layer normalization, and feedforward networks across stacked encoder layers.

Analyze the transformer decoder, its six stacked blocks, training versus inference behavior, masked attention, and teacher forcing that uses encoder outputs to generate Spanish sequence.

Explore the transformer decoder architecture, including masked multi-head attention, encoder-decoder attention, and feedforward networks, and learn how decoding proceeds during training and inference for sequence translation.

Explore the Hugging Face ecosystem, including the transformer library, model hub, datasets, and tokenizers, and learn to set up sentiment analysis in Google Colab with a simple pipeline.

Explore bert, a bidirectional encoder from the transformer, pre-trained on 3.3 billion words with two-task training—masked language modeling and next sentence prediction—to enable fine-tuning for natural language processing tasks.

Fine-tune Distilbert for sentiment analysis on IMDb data using Hugging Face. Train on Google Colab with GPU and evaluate with a classification report.

Explore T5, the text-to-text transfer transformer, with its encoder-decoder architecture, unified translation, summarization, and question answering, and pre-training on C4 with span masking guiding fine-tuning.

Fine-tune a small T5 model to translate English to German using the Hugging Face library, with a WMT 14 dataset and 1% sampling on Colab GPU.

Explore transformer models like Bert and T5 to tackle text classification, translation, and text generation, and apply attention mechanisms using Hugging Face and PyTorch.