Day 27 of #100DaysOfDataScience ✅.Unlocking meaning with Word Embeddings! 🧠.❌ BoW & TF-IDF lack semantics.🧭 Word2Vec (CBOW vs Skip-gram).🌐 GloVe embeddings.📏 Cosine similarity, word analogies.🧪 Visualized with t-SNE, tested pre-trained vectors via spaCy/gensim.#NLP #AI.

Good morning, beautiful souls! ☀️.Wishing you a day filled with peace in your heart, clarity in your mind, and kindness in your actions. May divine energy guide your path. Om Namah Shivaya. 🙏✨

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RT @TheAITimeline: 🚨This week's top AI/ML research papers:. - GSPO.- Diffusion Beats Autoregressive in Data-Constrained Settings.- Gemini 2….

RT @probnstat: Probability, Stochastic Processes and Optimization:

RT @dair_ai: Top AI Papers of The Week (July 21 - 27): . - MCPEval.- Subliminal Learning.- Learning without Training.- Alignment Auditing….

RT @rryssf_: You don’t need a PhD to understand Retrieval-Augmented Generation (RAG). It’s how AI stops hallucinating and starts thinking….

Day 26 of #100DaysOfDataScience ✅.Turning text into numbers for ML! 🔢🧠.🧰 Feature Extraction: BoW, TF-IDF.🔡 Vectorizers: Count & Tfidf.🧠 Intro to Word Embeddings (Word2Vec, GloVe).⚙️ ML Models: Logistic Regression, Naive Bayes, SVM.🧪 Built a spam/sentiment classifier!.#NLP.

RT @Ashafind: Dear Data Analyst/Scientist. Read these books and you'll be miles ahead in your Data journey. To get it: . 1. Follow me (s….

That's a quick dive into RNNs in Deep Learning! From understanding voice to predicting trends, they're vital for making sense of sequences. #DeepLearning #RNN #AI #MachineLearning.10/10.

So, while basic RNNs paved the way, LSTMs & GRUs are the workhorses powering many impressive AI apps today, especially with complex sequential data!.9/10.

Enter the saviors! To fix this, we have advanced RNNs:.LSTMs (Long Shirt-Term Memory): Use "gates" to control info flow, remembering important things over long sequences. GRUs (Gated Recurrent Units): A simpler, often effective version of LSTMs. 8/10.

But there's a catch! Basic RNNs struggle with long-term dependencies. They suffer from:. Vanishing Gradients : Info from early in a long sequence can "fade away.".Exploding Gradients : Gradients get too big, making training unstable. 7/10.

Key Uses:. NLP: Machine Translation, Text Generation, Sentiment Analysis. Speech Recognition: Powering Siri & Alexa. Time Series: Stock market prediction, weather forecasting. 6/10.

This "recurrent" connection means the output/state from one step feeds back as input for the next. The network constantly reminds itself of what happened before. 🔄 .5/10.

Imagine predicting the next word: "I love to eat hot ___". An RNN remembers "I love to eat hot" to guess "dogs" or "pizza", not "cloud"! 🍕🌭 .4/10.

The magic? RNNs have a "memory"! ✨.They use a "hidden state" at each step, carrying info from previous inputs. This helps them understand context and dependencies in a sequence. 3/10.

Unlike regular neural nets, RNNs shine with sequential data. Think text, speech, time series. The order of information is crucial here! 🗣️📝📈 .2/10.

Ever wonder how AI understands your voice, translates languages, or predicts your next word? Meet Recurrent Neural Networks (RNNs) – a key part of Deep Learning! .1/10.

RT @BharukaShraddha: SQL cheat sheet - Every JOIN explained. #sql #database