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AI ML Model Engineering with Word Embeddings

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Leibnitz’ Dream and the Calculus of Thought:

https://www.linkedin.com/search/results/content/?keywords=peter%20sigurdson%20word%20embeddings&sid=M2s&update=urn%3Ali%3Afs_updateV2%3A(urn%3Ali%3Aactivity%3A7103011870537617408%2CBLENDED_SEARCH_FEED%2CEMPTY%2CDEFAULT%2Cfalse)⁠

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(Your Assignment on Week 7 will be to make your own word embedding.)

Lecture on Word Embeddings in Building the AI/ML Model

Introduction:

Word Embeddings are a type of word representation {encoded in the AI Model in the format of a numerical data structure} that enables words to be represented as vectors in a continuous vector space.

The position of a word within the vector space is learned from text and is based on the words that surround the word when it is used.

{Think about the text training corpus you are doing the Baysian training on.}

Word embeddings are a fundamental aspect of natural language processing (NLP) in AI/ML models.

How do word embeddings work in the architecture of the AI MODEL

Word embeddings play a critical role in the architecture of many AI models, particularly those dealing with natural language processing (NLP).

Understanding the place and function of word embeddings in these architectures is key to grasping how these models are able to process and understand text data.

Below, the working of word embeddings in the architecture of the AI model is outlined:

1. Input Layer: Preprocessing and Transformation of the training text:

Tokenization:

Text data is tokenized, breaking it down into smaller pieces (words, subwords, or characters). This creates the TOKENS in your AI MODEL.

Encoding:

Each token is then encoded as a unique integer.

Embedding:

The integers are passed to the embedding layer as indices.

The embedding layer contains a table of vectors, and each index corresponds to a vector.

Each index is mapped to a dense vector (embedding) that the model will learn during training.

The output is a dense vector for each word, which will be used for further processing.

#We will R programming to put our hands on these mathematical concepts.

Example:

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Text: "I love machine learning."

Tokens: ["I", "love", "machine", "learning"]

Encoded Tokens: [1, 2, 3, 4]

Embeddings: [[0.1, 0.3], [0.4, 0.2], [0.5, 0.7], [0.8, 0.6]]

2. Hidden Layer(s): Processing and Learning

Sequential Processing:

The dense vectors (embeddings) are passed through one or more hidden layers of the neural network.

Learning Contextual Representations:

The network learns the optimal representations by adjusting the vectors to reduce the prediction error for the given task.

It captures semantic information, relationships, and context among words.

Example:

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Embeddings: [[0.1, 0.3], [0.4, 0.2], [0.5, 0.7], [0.8, 0.6]]

Processed (adjusted) Embeddings: [[0.2, 0.4], [0.5, 0.3], [0.6, 0.8], [0.9, 0.7]]

3. Output Layer: Task-Specific Outputs

Task-Specific Transformation:

The learned representations are used to make predictions or decisions based on the task.

Examples of Tasks:

Text Classification: Assigning a category to the text.

Sentiment Analysis: Determining the sentiment of the text.

Named Entity Recognition: Identifying entities (names, places) in the text.

Example:

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Processed Embeddings: [[0.2, 0.4], [0.5, 0.3], [0.6, 0.8], [0.9, 0.7]]

(Processed) Output (Sentiment Analysis): Positive

4. Backpropagation: Refining Embeddings

Error Calculation and Propagation:

The error between the predicted and actual output is calculated.

This error is propagated backward through the network.

Updating Embeddings:

The vectors in the embedding layer are updated based on the error, refining the word representations for better predictions in subsequent iterations.

Overview:

Initialization:

Embeddings are usually initialized randomly and learned during training.

Learning and Optimization:

Through various epochs (cycle of training), the model continuously learns and adjusts the embeddings to minimize the loss function.

Final Embeddings:

The final embeddings capture rich semantic and contextual information, which enhances the model’s capability to understand and process text.

In conclusion, word embeddings are fundamental to the architecture of AI models dealing with text, enabling the models to understand and process language in a way that’s meaningful and useful for a wide range of tasks.

Outline:

I. Understanding Word Embeddings:

A. Definition and Importance:

Word Embeddings: Continuous vector representations of words.

Importance: Capture semantic relationships between words.

B. Benefits in AI/ML Models:

Improved model performance in NLP tasks.

Capture contextual and emotional nuance information effectively.

II. Origin of Word Embeddings:

A. Historical Context:

Transition from bag-of-words (BoW) to more sophisticated representations.

B. Inspiration:

Aim to capture semantic context and relationships among words.

III. Creating Word Embeddings:

A. Algorithms:

Word2Vec:

Uses neural networks to learn word representations.

Context prediction.

GloVe (Global Vectors for Word Representation):

Based on word co-occurrence statistics.

Captures both global statistics and local context.

FastText:

Considers subword information.

B. Training Process:

Large text corpus (think about the Guttenburg Corpus) used for training.

Context words used to predict target words (or vice versa). next token generation

IV. Engineering Word Embeddings into the AI Model:

A. Integration with AI/ML Models:

Used as input layers for neural networks.

Provide dense, continuous, and fixed-size vectors. (R language examples of how this works).

B. Applications:

Text classification, sentiment analysis, machine translation, and more.

This works for the case of the TEXT AI model. (NOT other formats of AI models such as image generation, Video, business process, math and physics formulas: these have their own DSL domain language models).

C. Challenges & Solutions:

Challenge of out-of-vocabulary words.

Solutions like subword embeddings. (hyphenated words).

V. Practical Example:

A. Creating Word Embeddings using Gensim:

Demonstration of creating word embeddings using the Gensim library.

B. Integrating into AI Model:

Example of using the embeddings as input for a neural network model. (Your Assignment).

Conclusion:

Word embeddings are paramount for handling text in AI/ML models effectively.

They provide a way for models to understand semantic relationships between words, making them crucial for various NLP tasks.

Understanding the creation and integration of word embeddings into AI models is essential for anyone working in the field of machine learning and wanting to build AI ML products. This understanding enables the development of more sophisticated, efficient, and effective AI/ML models, contributing to building AI ML MODELS for your employers. The basis of this will be the kind of training in data, and the kinds of interactions the MODEL will have with users.

Below is a basic example using Python's Keras library to create an embedding layer and a simple neural network model.

This example is at a high level and is more illustrative than functional for specific tasks, to give an understanding of how embeddings work and how they can be integrated into a model.

Note: Before running the code, ensure to install necessary libraries by running pip install tensorflow.

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# Importing necessary libraries

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Embedding, Flatten, Dense

# Sample data (usually it would be more complex and larger)

sentences = [

"I love machine learning",

"I love coding in Python",

"I enjoy learning new things"

]

# Processing data: In real-world tasks, you would use more sophisticated preprocessing

words = set(word for sentence in sentences for word in sentence.split())

word_to_index = {word: index for index, word in enumerate(words)}

# Parameters

vocab_size = len(words) # Total unique words in the dataset

embedding_dim = 5 # The dimension of word embeddings

max_length = 5 # Maximum length of sentences/sequences

# Creating a Sequential model

model = Sequential([

Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length), # Embedding layer

Flatten(), # Flattening the 3D tensor output from the Embedding layer

Dense(16, activation='relu'), # Dense layer with 16 neurons and ReLU activation function

Dense(1, activation='sigmoid') # Output layer with 1 neuron and sigmoid activation function (binary classification)

])

# Compiling the model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Displaying the model summary

model.summary()

# In real-world tasks, you would fit the model with data by using the model.fit() function

Explanation:

Data Preparation:

Sentences are manually processed into a set of unique words and mapped to indices. In actual tasks, more sophisticated tokenization and preprocessing would be used.

Embedding Layer:

The Embedding layer is the first layer in the model, which transforms each word index into a dense vector of fixed size (embedding_dim), learned during model training.

Model Architecture:

After the Embedding layer, the output is flattened, followed by two dense layers.

The final layer is a single neuron with a sigmoid activation function, suitable for binary classification tasks.

Compilation:

The model is compiled with the Adam optimizer and binary crossentropy loss function, suitable for binary classification tasks.

In real-world tasks, the model would be trained with labeled data by using the model.fit() function, and the embeddings would learn to represent words in a way that is useful for the specific task (e.g., text classification).

Making Word Embeddings Relatable

When discussing the utility of word embeddings, it's crucial to provide relatable and understandable examples to help students grasp the concept. Let's dive into the world of word embeddings and explore the problems they can solve that other methods can't effectively handle.

Understanding the Problem:

The Context Dilemma:

Imagine reading a book, but every word is isolated, and you don’t understand the connection between the words. Traditional methods might struggle with understanding the context and relationships between words.

Handling Ambiguity:

Consider the word “bank.” It can mean a financial institution or the side of a river. Without understanding the context, it's hard to ascertain the correct meaning.

How Word Embeddings Help:

Capturing Semantic Meaning:

Word embeddings can capture the context and semantic meaning of words. They place words in a multi-dimensional space where the "distance" and "direction" between words convey meaning.

For example, in this space, "man" might be closer to "boy" and farther from "car".

Dealing with Synonyms and Antonyms:

Traditional methods might treat words with similar meanings as entirely different entities. Word embeddings, however, can understand the similarity between words like “huge” and “large” or the opposite meaning of “good” and “bad”.

Contextual Ambiguity Resolution:

Word embeddings can help in understanding the context, enabling the model to differentiate the meaning of “bank” in “river bank” versus “savings bank”.

Real-World Scenarios:

Improving Search Engine Results:

Suppose you search for "large feline." Traditional methods might not associate it with "big cats," but word embeddings can, offering better search results.

Machine Translation:

When translating a sentence from one language to another, understanding the context is crucial to get the translation right. Word embeddings enhance machine translation by grasping the semantic context.

Sentiment Analysis:

For a business analyzing customer reviews, understanding that “not bad” is closer in meaning to “good” is important. Word embeddings can capture such nuances.

Conclusion:

In essence, word embeddings bridge the gap between human language complexity and machine understanding, providing a richer, more nuanced representation of text that enables machines to grasp context, semantics, and sentiment more effectively than traditional methods. By understanding the relationships and nuances of words, word embeddings empower AI models to perform a multitude of tasks with a higher degree of accuracy and efficiency, from search and translation to sentiment analysis and beyond.

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