CJP’s Five-Point Agenda for 2029
We call upon leaders of all opposition parties, their supporters, and social activists to stand behind our Five-Point Agenda.
It doesn’t matter to us which party you belong to (except for BJP), if you want to save democracy, support #CJP2029
Day 32 of #100DaysOfCode
Revisited RNN forward and backward propagation in depth today.
Was struggling with the concepts, so took time to break them down step-by-step and truly understand how data and gradients flow through time.
#LearningInPublic
Day 31 of #100DaysOfCode
Continued exploring Recurrent Neural Networks (RNNs)
Learned about Backpropagation Through Time (BPTT) and how errors are propagated across multiple time steps to update weights.
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Day 30 of #100DaysOfCode
Dived deeper into Simple RNNs.
Learned forward propagation through time and understood the architecture step-by-step
Getting more clarity on how sequential data flows.
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Day 29 of #100DaysOfCode
Learned the foundations of NLP in deep learning:
• ANN for tabular data
• CNN for image classification & object detection
• Sequential data concepts
Also explored ANN & RNN architectures and how sequence data is handled.
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Day 28 of #100DaysofCode
Completed NLP for ML
Learned about Word2Vec (CBOW & Skip-gram), and how word vectors are created and filtered also explored cosine similarity and average Word2Vec.
Got hands-on with the Gensim library to implement it in practice.
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Day 27 of #100DaysOfCode
Continued with NLP started word embeddings & learned how words can be represented as meaningful vectors instead of just counts. Also introduced to Word2Vec. Alongside, kept progressing with Java basics
Next: diving deeper into Word2Vec
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Day 26 of #100DaysOfCode
Continued with NLP learned TF & IDF, their advantages, disadvantages, and implementation.
Understanding how important words are weighted in text
Also started with Java basics (not explored much yet).
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Day 25 of #100DaysOfCode
Continued with NLP fundamentals
Covered Bag of Words, N-grams, TF and IDF
Learned how text is converted into numerical features and how word importance is calculated
Building strong foundations before moving to advanced models.
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Day 24 of #100DaysOfCode
Dived deeper into NLP and implemented Bag of Words from scratch.
✔️ Text preprocessing
✔️ Binary & Non-binary BoW
✔️ Converted text into numerical arrays
Step by step turning text into data.
Next: TF-IDF
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Day 23 of #100DaysOfCode
Restarted after a long distraction phase.
Revised Python basics, OOP, and everything I had done before.
Back on track with NLP completed tokenization, stemming, lemmatization, stopwords, NER, and POS.
Text preprocessing ✅
Now moving to vectorization.
Just launched my VS Code extension on the Marketplace! 🎉
Built it to improve the developer experience.
Would love your feedback download, try it, and drop a rating ⭐
#VSCode#BuildInPublic#Developers
Day 22 of #100DaysOfCode
Restarting after a break.
Lower back pain (deadlift mistake maybe).
Had to slow down.
Back to basics : text processing & stopwords in NLTK
No more casual effort.
Time to take this seriously.
Quiet comeback.
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Day 21 of #100DaysOfCode
Learned Stemming in NLP today. Explored PorterStemmer, RegexpStemmer & SnowballStemmer. Understanding how words are reduced to root forms for better text processing.
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Day 20 of #100DaysOfCode
Learned more about tokenization in NLP sent_tokenize, word_tokenize, wordpunct_tokenize, TreebankWordTokenizer.
Each splits text a little differently, especially with punctuation.
Going slow as have to manage college academics also.
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Day 19 of #100DaysOfCode
Was off for a few days due to some personal work.
Started diving deeper into NLP learned about tokenization, corpus, and sentence processing using NLTK. Understanding how text gets broken down before models actually “understand” it.
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Day 18 Of #100DaysOfCode
Energy was low today because of college. Learned more about practical use cases of NLP and started with tokenization and basic terminologies like corpus, stopwords, and n-grams.
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Day 17 of #100DaysOfCode
Today was low energy.
Didn’t build much.
Just reviewed an NLP roadmap and started learning Java for college.
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