Self-attention in LLMs, clearly explained
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This channels is for Programmers, Coders, Software Engineers.
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Mathematics for Computer Science
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
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discrete.openmathbooks.org/pdfs/dmoi4.pdf
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
🔗 Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf
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https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/
https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/
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𝗬𝗼𝘂𝗿_𝗗𝗮𝘁𝗮_𝗦𝗰𝗶𝗲𝗻𝗰𝗲_𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄_𝗦𝘁𝘂𝗱𝘆_𝗣𝗹𝗮𝗻.pdf
7.7 MB
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Differentiate between descriptive vs inferential statistics
Learn various sampling techniques
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Work with data structures, pandas, numpy, and matplotlib
Practice writing optimized SQL queries
Master joins, filters, groupings, and window functions
3. Build real-world projects
Construct end-to-end data pipelines
Develop predictive models with machine learning
Create business-focused dashboards
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Learn to break down ambiguous business problems
Ask clarifying questions to gather requirements
Think aloud and structure your answers logically
5. Mock interviews with feedback
Use platforms like Pramp or connect with peers
Record and review your answers for improvement
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6. Revise machine learning concepts
Understand supervised vs unsupervised learning
Grasp overfitting, underfitting, and bias-variance tradeoff
Know how to evaluate models (precision, recall, F1-score, AUC, etc.)
7. Brush up on system design (if applicable)
Learn how to design scalable data pipelines
Compare real-time vs batch processing
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rnn.pdf
5.6 MB
🔍 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!💡
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!
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Forwarded from Python Data Science Jobs & Interviews
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ds full archive.pdf.pdf
55.2 MB
Best Data Science Archive Notes
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𝗠𝗮𝘀𝘁𝗲𝗿_𝗣𝘆𝗦𝗽𝗮𝗿𝗸_𝗟𝗶𝗸𝗲_𝗮_𝗣𝗿𝗼_–_𝗔𝗹𝗹_𝗶𝗻_𝗢𝗻𝗲_𝗚𝘂𝗶𝗱𝗲_𝗳𝗼𝗿_𝗗𝗮𝘁𝗮_𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀.pdf
2.6 MB
𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 – 𝗔𝗹𝗹-𝗶𝗻-𝗢𝗻𝗲 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you.
I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce
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• SparkContext vs SparkSession
• Transformations: map, flatMap, reduceByKey, groupByKey
• Optimizations – caching, persisting, skew handling, salting
• Joins – Broadcast joins, Shuffle joins
• Deployment modes – Cluster vs Client
• Real interview-ready Q&A from top use cases
• CSV, JSON, Parquet, ORC – Format comparisons
• Common commands, schema creation, data filtering, null handling
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If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you.
I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce
• Spark Architecture – Driver, Executors, DAG
• RDDs vs DataFrames vs Datasets
• SparkContext vs SparkSession
• Transformations: map, flatMap, reduceByKey, groupByKey
• Optimizations – caching, persisting, skew handling, salting
• Joins – Broadcast joins, Shuffle joins
• Deployment modes – Cluster vs Client
• Real interview-ready Q&A from top use cases
• CSV, JSON, Parquet, ORC – Format comparisons
• Common commands, schema creation, data filtering, null handling
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🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph
Link: https://realpython.com/langgraph-python/
Link: https://realpython.com/langgraph-python/
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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
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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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
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