Exam AWS Certified Machine Learning - Specialty topic 1 question 176 discussion

seq2seq and object2vec take care of more than just the words. Any response with blazingText is wrong because blazingText just uses a cbow (continuous bag of words), working only on individual words

It should be B an D. The objective is to create a latent space/word embedding that puts similar words closer to each other for other purposes. Thus, we should use Sagemaker Blazing Text in unsupervised mode (Word2Vec mode). cbow, skip-grams, and batch skip-grams are the 3 algorithms for this. However, since we do not need to do the later part of E, E is not correct. The ans should be B and D.

Using Amazon SageMaker Object2Vec (C) provides an end-to-end solution for learning embeddings with contextual and sequential relationships, while BlazingText in Skip-gram mode combined with a custom RNN (E) allows for greater flexibility in capturing sequence-level dependencies. Both approaches can produce high-quality embedding vectors that meet the requirements. Why not A : The seq2seq algorithm is designed for sequence-to-sequence tasks like translation or summarization. While it generates embeddings internally, it is not designed to provide a general-purpose feature space for downstream predictive models.

Best choices

To extract embedding vectors: Blazingtext Word2vec and Object2vec (B, C). Seq to seq: generate one sequence from another (A is out) Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode does not capture word embeddings (D is out)

To capture word context and sequential QA information, the embedding vectors need to consider both the order and the meaning of the words in the text. Option B, Amazon SageMaker BlazingText algorithm in Skip-gram mode, is a valid option because it can learn word embeddings that capture the semantic similarity and syntactic relations between words based on their co-occurrence in a window of words. Skip-gram mode can also handle rare words better than continuous bag-of-words (CBOW) mode1. Option E, combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN), is another valid option because it can leverage the advantages of Skip-gram mode and also use an RNN to model the sequential nature of the text. An RNN can capture the temporal dependencies and long-term dependencies between words, which are important for QA tasks2

Considering the requirements, the two options that can produce the required embedding vectors that capture word context and sequential QA information are: C. Amazon SageMaker Object2Vec algorithm: Because it can learn to capture relationships in pairs of text, which could include the sequential nature of questions and answers. E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN): This combination provides both context-aware word embeddings and the ability to capture sequential dependencies in text data.

C because Object2Vec is a neural network-based algorithm that can learn embeddings for a wide range of data types and tasks. E because If you want to capture word context and sequential information, especially in the context of natural language processing (NLP), it is advisable to use models that are specifically designed for sequence modeling, such as recurrent neural networks (RNNs) or more advanced models like long short-term memory networks (LSTMs) or transformers.

A. Amazon SageMaker seq2seq algorithm: Sequence-to-sequence (seq2seq) models are designed to convert sequences from one domain to sequences in another domain, often used in tasks like machine translation. They are capable of understanding the context and the sequence in which words appear, making them suitable for differentiating between questions and answers in a text. E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN): This combination is promising. BlazingText in Skip-gram mode captures word context, and the recurrent neural network (RNN) is excellent for capturing sequential data, such as the flow in conversations or text. This combination should be effective at understanding both the context of individual words and the sequence of questions and answers.

One problem of Object2Vec is it takes two objects as input during training and loss minimizes the difference between embeddings of these two objects. I don't think we have some labels to pass to Object2Vec We might think that we have a QA which we can pass as two objects. But in the question we want embeddings to distinguish between Q & A, but this Object2Vec minimizes the difference. So I wouldn't tell it is for sure C https://aws.amazon.com/blogs/machine-learning/introduction-to-amazon-sagemaker-object2vec/

A . Seq2seq (sequence-to-sequence) models are designed to handle sequences. They are particularly well-suited for tasks like translating sentences from one language to another, but they can also be used for other tasks that involve sequences, such as converting questions to answers. Given that the embedding space must differentiate between questions and answers, a seq2seq model would be a good choice. E . BlazingText in Skip-gram mode can capture word context effectively. However, on its own, it might not capture the sequential information between questions and answers. By combining it with a custom RNN, the sequential nature of the sentences, especially in a QA setting, can be captured. RNNs are designed to work with sequences and can remember past information, making them suitable for this task.

A. NO - seq2seq not for word embeddings B. YES - BlazingText in Skip-gram works and can capture Q&A C. NO - object2vec not for word embeddings D. YES - BlazingText in CBOW works and can capture Q&A E. NO - no need for RNN

Answers should be B and C See the following for BlazingText with Skip-gram: https://arxiv.org/pdf/1604.04661.pdf (search skip-gram) Linked from this page https://docs.aws.amazon.com/sagemaker/latest/dg/blazingtext.html For Object2Vec see this page https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec.html

C is confirmed but confused between A or E then lean to E

confusing

Seq2Seq will not generate an embedding vector, so A it's wrong from my POV. I go with B - C

IMO E for sure then either B or C Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN) is a more sophisticated approach that can be used to capture sequential QA information. This is because RNNs are able to learn long-term dependencies between words.