Published:
Matrix multiplication can be quite confusing, especially when using the versatile np.dot() function in NumPy. In this blog, we’ll dive into the three main types of matrix multiplication: vector-vector, vector-matrix, and matrix-matrix operations. We’ll clarify how these operations work and provide examples to enhance your understanding.
Published:
In Natural Language Processing (NLP), a word embedding is a representation of a word in a continuous vector space. This representation is typically a real-valued vector that encodes the meaning of the word, such that words closer together in the vector space are expected to have similar meanings.
Published:
In Recurrent Neural Networks (RNNs), one of the major challenges is the vanishing gradient problem. To address this, we use more advanced architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). Today, we’ll focus on LSTM networks.
Published:
In a Recurrent Neural Network (RNN), the process starts with forward propagation, followed by backward propagation (Backpropagation Through Time, or BPTT). During backward propagation, the network’s weights are updated over time to minimize the loss function. However, simple RNNs encounter significant challenges known as the vanishing gradient problem and the exploding gradient problem.
Published:
Published:
Published:
Semantic information refers to the meaning and relationship between words in a sentence or document. Traditional methods like Bag of Words (BoW) and TF-IDF focus solely on the frequency of words and ignore the context in which they appear. This means they don’t capture the meaning of the words or how they relate to each other.
Published:
Term Frequency (TF) is a measure of how frequently a word appears in a sentence, normalized by the total number of words in that sentence. It is calculated as:
Published:
The Bag of Words (BoW) model is a fundamental technique in Natural Language Processing (NLP) used to extract features from text data. It helps in representing text in a numerical form, which is essential for many machine learning algorithms. In this post, we’ll explore how the Bag of Words model works, how to implement it, and some of its limitations.
Published:
In Natural Language Processing (NLP), reducing words to their root form is an essential step for various tasks like text analysis and classification. Two common techniques for this are Stemming and Lemmatization. Though they serve a similar purpose, they differ in their approach and results. In this post, we’ll explore both techniques and discuss when to use each one.
Published:
When dealing with text data, tokenization is a crucial step. It involves breaking down a text into smaller components, such as words or sentences, to prepare it for further analysis. In this post, we’ll explore how to handle tokenization using the Natural Language Toolkit (NLTK), an open-source library that simplifies various NLP tasks.
Published:
Natural Language Processing (NLP) is a rapidly evolving field with a broad spectrum of techniques and technologies. Whether you’re a beginner or looking to deepen your knowledge, this roadmap will guide you through essential stages of NLP. Here’s a structured path to mastering NLP: