Forwarded from Data Science Jupyter Notebooks
This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations
๐ Jupyter Notebooks with interactive code.๐ง Step-by-step tutorials on Tensors, Autograd, and Neural Networks.๐ผ Real-world mini-projects like image classification.โ Practical guides on using GPU with PyTorch.โ Beginner-friendly but also great for revision.
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Mathematics for Computer Science
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
๐ Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
๐ Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf
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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/
https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/
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7.7 MB
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rnn.pdf
5.6 MB
๐ 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:
๐ 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.
๐ง 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.
๐ Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
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๐ Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
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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:
๐ 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.
๐ง 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.
๐ 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.
๐ 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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ds full archive.pdf.pdf
55.2 MB
Best Data Science Archive Notes
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๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ถ๐ธ๐ฒ ๐ฎ ๐ฃ๐ฟ๐ผ โ ๐๐น๐น-๐ถ๐ป-๐ข๐ป๐ฒ ๐๐๐ถ๐ฑ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews โ this one is for you.
Iโm sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
๐ช๐ต๐ฎ๐'๐ ๐ถ๐ป๐๐ถ๐ฑ๐ฒ? โข Spark vs MapReduce
โข Spark Architecture โ Driver, Executors, DAG
โข RDDs vs DataFrames vs Datasets
โข SparkContext vs SparkSession
โข Transformations: map, flatMap, reduceByKey, groupByKey
โข Optimizations โ caching, persisting, skew handling, salting
โข Joins โ Broadcast joins, Shuffle joins
โข Deployment modes โ Cluster vs Client
โข Real interview-ready Q&A from top use cases
โข CSV, JSON, Parquet, ORC โ Format comparisons
โข Common commands, schema creation, data filtering, null handling
๐ช๐ต๐ผ ๐ถ๐ ๐๐ต๐ถ๐ ๐ณ๐ผ๐ฟ? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews โ this one is for you.
Iโm sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
๐ช๐ต๐ฎ๐'๐ ๐ถ๐ป๐๐ถ๐ฑ๐ฒ? โข Spark vs MapReduce
โข Spark Architecture โ Driver, Executors, DAG
โข RDDs vs DataFrames vs Datasets
โข SparkContext vs SparkSession
โข Transformations: map, flatMap, reduceByKey, groupByKey
โข Optimizations โ caching, persisting, skew handling, salting
โข Joins โ Broadcast joins, Shuffle joins
โข Deployment modes โ Cluster vs Client
โข Real interview-ready Q&A from top use cases
โข CSV, JSON, Parquet, ORC โ Format comparisons
โข Common commands, schema creation, data filtering, null handling
๐ช๐ต๐ผ ๐ถ๐ ๐๐ต๐ถ๐ ๐ณ๐ผ๐ฟ? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.
#PySpark #DataEngineering #BigData #SparkArchitecture #RDDvsDataFrame #SparkOptimization #DistributedComputing #SparkInterviewPrep #DataPipelines #ApacheSpark #MapReduce #ETL #BroadcastJoin #ClusterComputing #SparkForEngineers
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๐๐ฐ 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 โ full tutorials and code available on GitHub:
1๏ธโฃ 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
2๏ธโฃ 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
3๏ธโฃ 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
4๏ธโฃ 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
โก๏ธ You can find the articles and the codes for each article in this GitHub repo:
https://lnkd.in/dG5jfBwE
1๏ธโฃ 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
2๏ธโฃ 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
3๏ธโฃ 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
4๏ธโฃ 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
โก๏ธ 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.
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New to Pandas?
Here's a cheat sheet you can download (2025)
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Top 50 LLM Interview Questions!
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
๐๐๐๐๐๐
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
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LLM Interview Questions.pdf
71.2 KB
Top 50 LLM Interview Questions!
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