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Unit 4

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Terminology related to deep learning in the context of NLP, including word embeddings and recurrent neural networks

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Learn More\n Live Demo
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Description Unit 4

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\n Learn about the structure of the unit, the skills you will develop and the materials we will use by exploring the following resource.\n

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Click to read
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Idiom of the day

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* Push the envelope

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Meaning: To go beyond the current limits or boundaries, often used in technological advancements.
Example: “The research team aimed to push the envelope of AI capabilities with their groundbreaking algorithm.”

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Click to read
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Key vocabulary 

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Let's learn vocabulary before reading #1.

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Key vocabulary
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\n \"Mobirise\n \n
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Reading comprehension activity

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\"What Is Deep Learning? A Guide to Deep Learning Use Cases, Applications, and Benefits\" 

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Go to reading #1
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Activity about previous reading

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Let's answer the questions in the following activity.

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Go to activity #1
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\n Resultados\n

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\n Los editores y los anunciantes pueden decidir cómo presentar su contenido y qué proveedores de tecnología utilizar, todo mientras mantienen y mejoran los indicadores clave de rendimiento.\n

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\n Learn More\n
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Pre-reading #2: Socialize keywords

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\n Dale forma a tu futuro proyecto web con un diseño nítido y funciones codificadas depuradas.\n

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Learn More\n Live Demo
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Part 1

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1. What is augmented reality and how will it shape our shopping experience by 2030?

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1. Word Embeddings: it refers to the representation of words as real-valued vectors. These vectors capture semantic and syntactic information, allowing computers to understand and process textual content more effectively.\n
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2. NLP (Natural Language Processing): NLP is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves the development of algorithms and models to enable machines to understand, interpret, and generate human-like text. 

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Part 2

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2. How will AI-powered personal assistants enhance our daily lives by 2030?

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3. Vectorization: is the process of converting words or documents into numeric vectors. In the context of word embeddings, it involves representing words as real-valued vectors in a lower-dimensional space.\n
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4. TF-IDF (Term Frequency-Inverse Document Frequency): TF-IDF is a machine learning algorithm used for word embedding in text. It comprises two metrics, term frequency (TF) and inverse document frequency (IDF), to measure the importance of words in a document or corpus.

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Part 3

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3. What is the metaverse and how is it expected to become a big deal in our daily lives in 2030?

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5. Bag of Words (BOW): is a word embedding technique where each value in the vector represents the count of words in a document or sentence. It is a method of extracting features from text, disregarding word order.\n
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6. Word2Vec: is a word embedding technique developed by Google. It uses shallow neural networks to learn word representations based on the distributional hypothesis, capturing semantic similarities between words. 

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Part 4

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\n Effective Engagement\n

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7. GloVe (Global Vectors for Word Representation): GloVe is a word embedding method that focuses on global context to create word representations. It uses a co-occurrence matrix derived from a large corpus to capture semantic relationships between words.\n
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8. BERT (Bidirectional Encoder Representations from Transformers): BERT is a state-of-the-art natural language processing model based on transformers. It utilizes bidirectional attention mechanisms to generate high-quality word embeddings that are contextualized, making it effective for various NLP tasks.

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Compromisos

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\n Effective Engagement\n

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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Corporis dignissimos ad minima recusandae dicta perspiciatis cumque? Ut excepturi aliquid temporibus consectetur dolore ab facilis ipsa veritatis fuga nam, tempora, voluptatum officiis. Cupiditate eligendi, cum, fugiat esse ut nam, ex eius iste sunt consequuntur quia omnis. Itaque dolorum ullam quod at!

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Compromisos

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\n Effective Engagement\n

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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Molestiae ipsam consequuntur, impedit fuga ad veritatis itaque facere dignissimos iste autem perferendis minus adipisci iure fugit id deserunt quisquam incidunt, unde ea? Harum eos, deleniti laborum dicta tenetur. Esse itaque neque repellat facilis nostrum, illum veritatis recusandae autem quam expedita quia.

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Reading: A Guide on Word Embeddings in NLP

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Pay close attention to the following reading and then gap fill activity.

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Go to reading #2 Go to activity #2
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\"Word Embeddings in NLP and its Applications\"

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\n Páginas Móviles Aceleradas por Google\n

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\n Let's pay attention to the following resources on the future of AI.

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Vocabulary activity\n Go to reading #3 Activity #3
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\n \"Mobirise\n \n
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