Learn BERT - essential NLP algorithm by Google

Explore what BERT is, a bidirectional encoder-based language model built on transformers. Learn how pretraining on large unlabeled data and fine-tuning enable flexible word and sentence representations.

Learn how word embedding converts text into vector representations, replaces one-hot encoding, and trains context-aware embeddings that reveal semantic relationships, such as king minus man plus woman equals queen.

Explore BERT as a pre trained language model that uses transfer learning and fine tuning on large corpora to generate contextual word vectors for tasks like classification and question answering.

Trace the sequence-to-sequence setup with an encoder and decoder built from recurrent neural networks, and see how attention uses a context vector to relate encoder states to decoder steps.

Explore how the transformer uses self-attention and an encoder–decoder architecture to encode and decode sequences, processing whole sentences at once for global context.

Unpack the attention mechanism in transformers, including scaled dot-product attention with q, k, v, softmax weighting, lookahead masking, and its use in encoder-decoder and self-attention.

Explore the architecture of BERT, a transformer-based encoder stack with tokenization, embeddings, and two-sentence inputs, delivering a classification vector from the classification token and word-level outputs through pretraining tasks.

Explore BERT pre-training with masked language modeling and next sentence prediction, enabling bidirectional context and sentence-level understanding through context-rich representations.

This lecture explains convolutional neural networks for natural language processing using embeddings. It shows how filters detect local features on a sentence matrix and use max activations to summarize them.

Build a cnn-based sentiment analyzer for tweets using the sentiment140 dataset. Train on the train set to classify text as positive or negative.

Connect to the server, import dependencies and libraries, set GPU accelerator in Google Colab, and pip install TensorFlow 2.x, then load Dancefloor Hub models for CNN training.

Mount your google drive in the session, load the sentiment data with latin-1 encoding and defined column names, then drop unused columns to prepare the text input.

Learn data cleaning and pre-processing for NLP by removing mentions and links, decoding XML with BeautifulSoup, applying regex for text normalization, and preparing labeled sentiment data for BERT workflows.

Explore the tokenization phase for tweets using a tokenization tool, load the vocabulary, configure lowercase, and convert tokenized text into integer IDs for model input.

Create efficient NLP training data by batching sentences by length, padding, shuffling, and sorting via length, then using a from_generator dataset with proper shapes and a train-test split.

build a deep convolutional neural network for text classification using an embedding layer, three filter sizes (2, 3, 4), max pooling, and dense layers with dropout.

Set up training for a cnn nlp model, define hyperparameters, compile with adam and cross-entropy, and use checkpoints plus a custom callback to save after each epoch.

Evaluate the model on test sets and report accuracy and loss. Highlight the training-to-test accuracy gap and discuss overfitting and data processing choices.

Learn how to plug a pretrained BERT embedding layer into a CNN classifier by using three inputs: token ids, attention masks, and segment ids with [CLS] and [SEP].

Discover how to use a BERT embedding layer within a CNN, including three input types (IDs, mask, segments), to produce per-token vectors and a sentence vector for classification.

Apply BERT to the SQuAD question answering task by using word-level representations to predict the start and end positions of the answer within a paragraph.

Connect to the server to set up a TPU-accelerated BERT training environment, install TensorFlow nightly and Google's Birds package, and configure a modified Adam optimizer.

Learn how to preprocess data for BERT using Google tools, converting JSON to TF records, generating input metadata, and building SQuAD datasets with batch size and max sequence length.

Explore building the squad layer for BERT by using a dense layer with outputs per token to predict start and end scores, then reshape into start and end score lists.

Build and fine-tune a bert-based squad model by configuring a trainable squad layer, processing inputs into sequence outputs, and producing start and end logits for a standard question answering task.

Configure hyperparameters and a Google optimizer to train a BERT-based QA model, define start and end position losses with cross entropy, and implement a training loop with checkpoints.

Learn how to build a custom TensorFlow training loop with epochs and batches, use a gradient tape to compute gradients, apply an optimizer, track loss, and save checkpoints.

Prepare evaluation data for a squad model by generating the prediction file and converting exemplars to features for the tf record used by evaluation scripts.

Learn how to prepare BERT NLP evaluation predictions by creating named result tuples with unique ids, start logits, and end logits, and streaming them into the predictions file for evaluation.

Learn how to evaluate a fine-tuned BERT model on a SQuAD-style task, run evaluation scripts to compare predictions with gold data, and interpret accuracy scores.