# @awomanindatascience on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/C3EOtwWP4X1
- **Gondola URL:** https://gondola.cc/posts/61230168-awomanindatascience-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/ac0c4a588c.jpg
- **Posted:** 2024-02-08T00:23:48.000+00:00
- **Account Owner:** Chinar Arora (@awomanindatascience) — https://gondola.cc/awomanindatascience

## Caption

⚡️NLP is a field of artificial intelligence that focuses on the interaction between computers and human language. It involves the development of algorithms and models to enable machines to understand, interpret, and generate human language.
⚡️For Day 12 - Day 22 of my #100daysofcodechallenge I applied the concepts from Stanfords online course CS224N NLP with Deep learning in Python. My key learnings:
⚡️Word vectors represent words as continuous-valued vectors in a multi-dimensional space, capturing semantic relationships between words based on their context. Also known as word embeddings, these vectors enable mathematical operations on words.
Similar words have closer vector representations, aiding in semantic understanding.
⚡️Word2Vec is an algorithm that learns word embeddings by training neural networks to predict words based on their context, resulting in dense vector representations.
Consists of Skip-gram and Continuous Bag of Words (CBOW) architectures.
Efficiently captures semantic relationships and word similarities.
Popular for initializing embeddings in various NLP tasks.
⚡️Neural network classifiers use artificial neural networks to classify and process sequential data, making them effective for tasks like sentiment analysis and part-of-speech tagging in NLP.
⚡️Stochastic gradient descent : SGD is an optimization algorithm that updates model parameters iteratively by minimizing the loss function, making it widely used in training neural networks.
⚡️Backpropagation is the process of updating neural network weights by propagating errors backward through the network, enabling effective learning and optimization.
Utilizes the chain rule of calculus to calculate gradients with respect to the weights.
Collaborates with optimization algorithms like SGD for efficient model learning.
⚡️Dependency parsing involves analyzing the grammatical structure of a sentence by identifying syntactic relationships between words, essential for understanding sentence meaning.

#naturallanguageprocessing #machinelearning #deeplearning #100daysofcodechallenge #womenwhocode #womenindatascience #100daysofcode #datascience #microsoft #learning

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## Tags

womenindatascience, learning, microsoft, deeplearning, machinelearning, 100daysofcodechallenge, womenwhocode, naturallanguageprocessing, datascience, 100daysofcode

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