Self-attention in LLMs, clearly explained

#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation

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🔍 Understanding Recurrent Neural Networks (RNNs) Cheat Sheet!
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:

📘 Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.

🔧 Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.

🚀 Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.

🔗 Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems! 💡

#RNN #RecurrentNeuralNetworks #DeepLearning #NLP #LSTM #GRU #TimeSeriesForecasting #MachineLearning #NeuralNetworks #AIApplications #SequenceModeling #MLCheatSheet #PyTorch #TensorFlow #DataScience

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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub:

1️⃣ Text-Based Applications

1.1. Building a Chatbot Using HuggingFace Open Source Models

https://lnkd.in/dku3bigK

1.2. Building a Text Translation System using Meta NLLB Open-Source Model

https://lnkd.in/dgdjaFds

2️⃣ Speech-Based Applications

2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model

https://lnkd.in/dbgQgDyn

2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio

https://lnkd.in/dcbp-8fN

2.3. Building Text-to-Speech Systems Using VITS & ArTST Models

https://lnkd.in/dwFcQ_X5

3️⃣ Image-Based Applications

3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model

https://lnkd.in/dnk6epGB

3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide

https://lnkd.in/d573SvYV

3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM)

https://lnkd.in/dFavEdHS

3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio

https://lnkd.in/d9jjJu_g

4️⃣ Vision Language Applications

4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models

https://lnkd.in/dHNFaHFV

4.2. Building an Image Captioning System using Salesforce Blip Model

https://lnkd.in/dh36iDn9

4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models

https://lnkd.in/d7fsJEAF

➡️ You can find the articles and the codes for each article in this GitHub repo:

https://lnkd.in/dG5jfBwE

#HuggingFace #Kaggle #AIapplications #DeepLearning #MachineLearning #ComputerVision #NLP #SpeechRecognition #TextToSpeech #ImageProcessing #OpenSourceAI #ZeroShotLearning #Gradio

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Top 50 LLM Interview Questions!

#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels

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Introduction to Deep Learning
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.

Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.

What makes Deep Learning so powerful?

Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.

#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation

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