Younes Sawaftah

🚀 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐇𝐞𝐚𝐭𝐦𝐚𝐩𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 🚀 I’m excited to share a recent project where I created an 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐡𝐞𝐚𝐭𝐦𝐚𝐩 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐭𝐨 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐞 𝐩𝐨𝐥𝐲𝐠𝐨𝐧 𝐝𝐚𝐭𝐚 with 𝐠𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐜𝐨𝐥𝐨𝐫𝐢𝐧𝐠 based on various metrics. This approach can be easily adapted for different data formats, including 𝐄𝐱𝐜𝐞𝐥, 𝐊𝐌𝐋, 𝐨𝐫 𝐊𝐌𝐙 𝐟𝐢𝐥𝐞𝐬. Key Highlights: 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Combined polygon coordinates and values from separate files to create a comprehensive visualization. 𝐆𝐫𝐚𝐝𝐢𝐞𝐧𝐭 𝐂𝐨𝐥𝐨𝐫𝐢𝐧𝐠: Implemented a gradient color scale that transitions from red to green, with yellow representing mid-range values. 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐩: Utilized Folium and GeoPandas to generate an interactive map that provides insightful visual representations. 🔍 How It Works: 𝐋𝐨𝐚𝐝 𝐃𝐚𝐭𝐚: Import data from Excel files containing polygon coordinates and associated values. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧: Merge and preprocess the data to align coordinates with corresponding values. 𝐌𝐚𝐩 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧: Use Folium and GeoPandas to create and style the map based on the provided data. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Display the results with a gradient color scale to highlight variations in the data. Feel free to connect if you want to learn more about data visualization techniques or if you have similar projects in mind! #DataScience #DataVisualization #Python #Folium #GeoPandas #Heatmap

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Models for Machine Learning engineers and data scientists are fun as well as Tableau or dashboard for BI/data analysts However, a data project is not simply that: 1- the work should keep cash flowing (long term) 2- avoid business losses 3- a non foreseeable return on investment (roi) might induce jobs cut or department shut. 4- learning about business matters should be the everyone business 5- communication needs to be going back and forth between data nerds and execs. kill the project if necessary or not contributing. 6- the company should monitor the ongoing project continuously. A safer way to protect each individual contributing and fulfilling his/her role. #data #dataptoject #roi #companies #datascience #ml #dataanalytics #dataengineering

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𝗗𝗮𝘆 𝟭 𝗼𝗳 𝟭𝟬𝟬: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘃𝘀. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 Welcome to the first day of "𝟭𝟬𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝟭𝟬𝟬 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀!" Today, we'll start with a fundamental concept in data analytics: the difference between 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 and 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 statistics. 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀: Descriptive statistics are used to summarize and describe the main features of a dataset. They provide simple summaries about the sample and the measures. Common descriptive statistics include: 𝗠𝗲𝗮𝗻 (𝗔𝘃𝗲𝗿𝗮𝗴𝗲): The sum of all values divided by the number of values. 𝗠𝗲𝗱𝗶𝗮𝗻: The middle value when the data is ordered. 𝗠𝗼𝗱𝗲: The most frequently occurring value. 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗗𝗲𝘃𝗶𝗮𝘁𝗶𝗼𝗻: A measure of the amount of variation or dispersion in a set of values. 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲: The average of the squared differences from the mean. 𝗥𝗮𝗻𝗴𝗲: The difference between the highest and lowest values. 𝗣𝗲𝗿𝗰𝗲𝗻𝘁𝗶𝗹𝗲𝘀 𝗮𝗻𝗱 𝗤𝘂𝗮𝗿𝘁𝗶𝗹𝗲𝘀: Values that divide the data into equal parts. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀: Inferential statistics go beyond describing the data. They are used to make inferences and predictions about a population based on a sample of data. This involves: 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Determining if there is enough evidence to support a certain belief about a parameter. 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗮𝗹𝘀: Estimating the range within which a population parameter lies, with a certain level of confidence. 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Examining the relationship between variables and predicting future values. 𝗔𝗡𝗢𝗩𝗔 (𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲): Comparing means among three or more groups. 𝗖𝗵𝗶-𝗦𝗾𝘂𝗮𝗿𝗲 𝗧𝗲𝘀𝘁: Assessing the association between categorical variables. 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Descriptive statistics aim to describe the data, while inferential statistics aim to make predictions or inferences about a population. 𝗗𝗮𝘁𝗮 𝗨𝘀𝗲: Descriptive statistics use the data to provide a summary. Inferential statistics use the data to make generalizations beyond the data. 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: Descriptive techniques include measures of central tendency and variability. Inferential techniques include hypothesis tests, confidence intervals, and regression. Understanding these differences is crucial for effective data analysis and making informed decisions based on data. 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻! Have you used descriptive or inferential statistics in your projects? Share your experiences or ask questions in the comments below! #dataanalytics #statistics #datascience #learningjourney #100DaysOfLearning

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#Boom #My_Models have been #trained #Project_title: Sentiment Analysis for Customer Feedback: Product and Service Improvements with Precision The processing times and speeds provide insight into the efficiency of the data preprocessing stage for this datasets. Here's a breakdown of what these figures imply: #Training_Dataset (3.6 Million Reviews) - Total Time_Taken: 41 minutes and 31 seconds - Processing Speed: 1444.68 iterations per second (it/s) #Test_Dataset (400,000 Reviews) - Total Time Taken: 4 minutes and 28 seconds - Processing Speed: 1488.19 iterations per second (it/s) #Observations_and_Implications: 1. Processing Speed: The speed of processing, measured in iterations per second, is relatively consistent between the training and test datasets. The slight variation could be due to differences in system resources, data structure, or concurrent processes running during the preprocessing. 2. Scalability: The fact that the system can handle a large dataset of 3.6 million reviews within a reasonable timeframe (41 minutes) demonstrates good scalability and efficiency of the preprocessing step. This efficiency is crucial for projects involving large-scale data, where preprocessing can become a bottleneck. 3. Progress Feedback: The use of tqdm to provide progress feedback is particularly beneficial in such scenarios. It not only gives an estimate of the time required but also helps in monitoring the process for any potential issues. 4. Optimization Opportunities: While the current processing speed is efficient, there may still be room for optimization. This could involve parallel processing, more efficient algorithms for stopword removal, or hardware improvements. 5. Practical Considerations: The total processing time and speed are important metrics for planning and resource allocation, especially if preprocessing is a recurring task or if different datasets need to be processed frequently. Overall, these metrics are a positive indication of the system's capability to handle extensive preprocessing tasks, an essential step in preparing data for subsequent analysis or machine learning modeling. The code is in the comment session AMDARI, #NLP #DataScience, #PredictiveModelling #SentimentAnalysis #CustomerFeedback #ServiceImprovements Omowumi Victoria Samson

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Another Maven Analytics Python course completed! This course provided a comprehensive overview of the data analysis process in Python, leveraging the Pandas library. It covered everything from data reading and cleaning to performing multi-level analyses and generating plots, all aimed at deriving actionable insights for data-driven decision-making. I would like to express my appreciation to Maven Analytics for delivering such an outstanding course with real-time data and thought-provoking problems. Next course, #datascience in Python! And as always, keep learning, keep growing! #dataanalysis, #python, #pandas, #EWEC #keeplearningkeepgrowing

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#البيانات_الاصطناعية Lately, I've been hearing from so many developers looking for massive datasets to train their machine learning models. But you don’t need endless sources of real-world data. With **synthetic data generation**, you can create as much data as you need, with the exact characteristics you want – instantly. - **Avoid Privacy Issues**: Train on data that feels real without handling sensitive info. ### How to Get Started Many platforms now support synthetic data generation right out of the box, like **Oracle Autonomous Database** with its integrated AI models. You can define patterns and let the AI handle the heavy lifting – giving you endless data in minutes. So, instead of endlessly searching for “big data,” start thinking about **smart data** and **synthetic data**. It’s faster, safer, and built. Here’s an example of generating synthetic data in Oracle SQL Developer using a simple approach. This will create a sample dataset of customer transactions, including columns for customer ID, transaction date, amount, and location. You can customize the ranges and values to fit your specific needs. ### Step 1: Create the Table First, create a table to store the synthetic data. ```sql CREATE TABLE synthetic_transactions ( customer_id NUMBER GENERATED BY DEFAULT AS IDENTITY, transaction_date DATE, transaction_amount NUMBER(10, 2), location VARCHAR2(50) ); ``` ### Step 2: Generate Synthetic Data Using `DBMS_RANDOM` Oracle’s `DBMS_RANDOM` package allows you to generate random data directly in SQL. You can use this to populate columns with realistic values. ```sql BEGIN FOR i IN 1..1000 LOOP -- Generate 1000 rows; adjust as needed INSERT INTO synthetic_transactions ( transaction_date, transaction_amount, location ) VALUES ( SYSDATE - DBMS_RANDOM.VALUE(0, 365), -- Random date within the last year ROUND(DBMS_RANDOM.VALUE(10, 1000), 2), -- Random amount between $10 and $1000 CASE WHEN DBMS_RANDOM.VALUE(1, 100) <= 30 THEN 'New York' WHEN DBMS_RANDOM.VALUE(1, 100) <= 60 THEN 'Los Angeles' WHEN DBMS_RANDOM.VALUE(1, 100) <= 90 THEN 'Chicago' ELSE 'Houston' END -- Randomly assign location with weighted distribution ); END LOOP; COMMIT; END; ``` ### Explanation of Code - **`transaction_date`**: Generates a random date within the last year. - **`transaction_amount`**: Generates a random amount between $10 and $1000. - **`location`**: Assigns a location based on probability, with a higher chance for New York and Los Angeles. ### Step 3: Verify the Data After running the script, check the data with: ```sql SELECT * FROM synthetic_transactions FETCH FIRST 10 ROWS ONLY; ``` #AI #SyntheticData #MachineLearning #oracle #DataScience

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𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄: 👇 👉 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻 𝗔𝗜) is revolutionizing industries such as healthcare, finance, education, research, and medicine. 👉 With Gen AI, we leverage 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 (like GPT-4, Claude-3, Llama-3, etc.) to build various applications and use cases. 👉 The backbone of Gen AI is 𝗣𝘆𝘁𝗵𝗼𝗻. We use OOP principles, logging, and libraries like Flask and FastAPI in every end-to-end production-grade application. 👉 A strong theoretical background is not enough; building cool projects is essential. 👉 If you're in the AI field, dedicate 1 hour daily to sharpen your AXE (Python skills). It's an easy language compared to others. 📌 𝗠𝗼𝗿𝗮𝗹: Fundamentals make you indispensable in any organization. 𝗔𝗿𝗲 𝘆𝗼𝘂 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝘀𝗼𝘂𝗻𝗱 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻? 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗼𝗻 𝗣𝘆𝘁𝗵𝗼𝗻? 👀 #ai #python #GENAI #fundamentals

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Machine Learning engineer. #Supervise_Learning Supervised learning involves training a model on labeled data, where the desired output is already known. It can be further divided into: Classification: This task involves predicting discrete labels, such as spam detection in emails, image classification, and identity fraud detection. Regression: This task involves predicting continuous values, such as house prices, stock prices, or temperature forecasts, Weather Forecasting, Market Forecasting, Estimating Life Expectancy #Unsupervised_Learning Unsupervised learning involves training a model on data without labeled responses, primarily to find hidden patterns or intrinsic structures in the input data. It can be divided into: Clustering: Grouping similar items together, which can be used for customer segmentation, targeted marketing, and recommender systems. Dimensionality Reduction: Reducing the number of random variables under consideration by obtaining a set of principal variables. This is useful in: Big Data Visualization, Meaningful Compression, Structure Discovery, Feature Elicitation #Reinforcement_Learning Reinforcement learning involves training an agent to make a sequence of decisions by rewarding or punishing it based on its actions. It is commonly used in: Real-time Decisions, Robot Navigation, Game AI, Skill Acquisition .... etc. ||Additional Recommendations for a Machine Learning Engineer Foundations|| #Mathematics: Proficiency in linear algebra, calculus, probability, and statistics. #Programming: Strong skills in Python (and libraries like NumPy, pandas, scikit-learn) and familiarity with other languages like R if required. Data Handling Data Preprocessing Exploratory Data Analysis (EDA) #Algorithm_Knowledge: Deep understanding of various ML algorithms (decision trees, SVMs, neural networks, etc.). #Model_Evaluation: Using metrics like accuracy, precision, recall, F1-score, and AUC-ROC to evaluate model performance. ||Advanced_Topics|| #Deep_Learning: Understanding neural networks, CNN, RNN, LSTM, and using frameworks like TensorFlow or PyTorch. #Natural_Language_Processing (NLP): Techniques for text analysis, including sentiment analysis, text generation, and translation. #Computer_Vision: Techniques for image processing, including object detection and image segmentation. #Model_Deployment: Using tools like Docker, Flask, and cloud services (AWS, GCP, Azure) for deploying models. #Scalability: Ensuring models can handle large-scale data and real-time predictions. #Continuous_Learning Research and Development: Keeping up-to-date with the latest research through papers, conferences, and courses. Practical Projects: Engaging in projects, competitions (like Kaggle), and real-world applications to apply learned skills.

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🔥 Agentic RAG vs Vanilla RAG Agentic RAG enhances Retrieval-Augmented Generation (RAG) by incorporating AI agents to orchestrate the retrieval component and perform additional actions. These agents can decide which knowledge source to retrieve information from, formulate queries, and evaluate the retrieved context. Agentic RAG can be implemented using language models with function calling or agent frameworks, providing greater flexibility and power than traditional RAG. 🔗 Link to the article: https://lnkd.in/dByvSffP

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Data Storage for Data Science 1- Relational Databases: Data stored in relation to each other within tables. Examples: Microsoft SQL Server, Oracle Database, MySQL 2- Non-Relational Databases: Non-tabular storage optimized for the data type. Examples: MongoDB, Apache Cassandra, Redis 3- Data Lake: Stores large volumes of raw data (structured, semi-structured, and unstructured). Examples: Databricks, AWS S3, Azure Data Lake 4- Data Warehouse: Stores processed historical data (structured and semi-structured). Examples: AWS Redshift, Snowflake, BigQuery, Azure Synapse 5- Graph Database: A NoSQL database focused on relationships between entities. Examples: Neo4j, Amazon Neptune 6- Vector Database: Stores data as mathematical vectors to accelerate AI development. Examples: Pinecone, MongoDB, Elasticsearch, ChromaDB Guidance of Ahmed Hafez #DataScience #DatabaseManagement #BigData #AI #DataEngineering #MachineLearning #CloudComputing #RelationalDatabases #SQL #DataScience #NoSQL #DataStorage #BigData #DataLake #DataEngineering #CloudStorage #DataWarehouse #ETL #Analytics #GraphDatabase #NoSQL #DataModeling #VectorDatabase #AI #MachineLearning

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Exploring Functions vs. Generators in Python Dive into the distinctions between functions and generators in Python. Functions are blocks of reusable code that perform a specific task, whereas generators are a special type of iterable used to generate a sequence of values lazily, conserving memory. Understanding when to use each can optimize your code's efficiency and readability. Pdf credit goes to respective owner Follow Ajay Yadav for more such posts #python #functions #generators #data #analytics

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#task6 The Sparks Foundation 🚀 Iris Flower Classification using Decision Tree! 🌸🌳 🔍 Project Overview: In this project, I utilized the classic Iris flower dataset to build a Decision Tree classifier. The goal is to predict the species of Iris flowers based on their sepal and petal measurements. 💡 Key Features: - Dataset: Iris flower dataset with features like Sepal Length, Sepal Width, Petal Length, and Petal Width. - Model: Decision Tree classifier trained to classify the Iris species accurately. - Visualization: Graphical representation of the Decision Tree to understand the decision-making process. 📊 Tools and Technologies: - Python 🐍 - Pandas for data manipulation - Scikit-learn for model building - Matplotlib for visualization 📁 GitHub Repository: Check out the complete code and details here: https://lnkd.in/dPg23-g3 If you're interested in data science, machine learning, or Python programming, feel free to connect. Let's discuss and collaborate on exciting projects! #DataScience #MachineLearning #Python #DecisionTree #IrisDataset #GitHub #DataVisualization #AI #DataAnalytics

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Docker and Containerization 📝, Very Important for Data Science, Artificial intelligence AI, Data Analysis, Machine learning ML -------------------------------------------------- 1️⃣ Follow Amar Sharma for more Informative Notes. 📝 2️⃣ Like, Comment, and Repost to Engage and Show Support. 👍💬🔄 3⃣ Check my previous posts it will definitely help you 🙌 Thank you 💜 #python #pythonprogramming #pythondeveloper #pythoncoding #pythoncode #datascience #ai #ml #datascientist #mlengineer #machinelearning #artificialintelligence #aiengineer #sqldeveloper #statistics #stats #sql #mysql

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What Is Data Science A Beginner’s Guide To Data Science 🔹 Business Intelligence (BI) vs. Data Science:  - BI focuses on descriptive analytics, while Data Science delves into predictive and prescriptive analytics.  - Data Science explores complex data patterns and generates insights for informed decision-making. --- 🔹 The Lifecycle of Data Science:  - Discovery: Identifying business objectives and defining the problem statement.  - Data Preparation: Collecting, cleaning, and transforming raw data into a usable format.  - Model Planning: Selecting appropriate algorithms and techniques for analysis. --- 🔹 Case Study: Diabetes Prevention  - Illustrates the practical application of Data Science in healthcare.  - Emphasizes the importance of data collection, cleaning, and model building in solving real-world problems. --- 🔹 Essential Skills for Data Scientists:  - Proficiency in statistics and mathematics to analyze and interpret data accurately.  - Knowledge of machine learning algorithms for predictive modeling.  - Domain-specific expertise to understand and address industry-specific challenges.  - Strong coding skills in languages like Python or R for data manipulation and analysis. --- Share your thoughts and experiences with data science in the comments below! ✅ Get any Data science training videos, https://lnkd.in/gQVwVNSG ✅ Subscribe to our Youtube channel: https://lnkd.in/gD54ZjUh ✅ P.S. Want to Upskill your Data Science workforce? Check out our course catalog for corporate training, https://lnkd.in/dYipv_Qm

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Hosting Local Code Copilot Server with TabbyML & Open Source Code Models TabbyML is a code AI model serving engine that enables anyone to host their own code completion copilot server rapidly. Here's what you need to do: 1. Visit TabbyML github and download binaries for your platform 2. Run the binary and pick code and chat models from the model library 3. Model will be downloaded on your computer 4. Download the VS Code / IDEA extensions for TabbyML 5. Connect code editor plugin to your local server instance I tried 1B sized models for code and chat but the quality was really underwhelming compared to github copilot. The bigger models need a GPU server but I expect them to be far better (as shown by many benchmarks that open code models have become really good). With tools like Tabby, organizations and companies can host their own secure code copilot instances and worry less about IP theft (let's face it all of AI is built on somewhat dubious legal foundations right now, so still some time until we know what's really legal v/s what's not). Link to Tabby in the comment. #ai #programming #tech #servers #cloud #hosting #copilot #github

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This link contains Tutorials + Books + Courses + Trainings + Educational Resources in: - Data science - Python - Artificial Intelligence . - BIG DATA - Data Analytics - Google Cloud Platform - IT Training - MBA - Cybersecurity And much more > https://lnkd.in/dtdBPcxz

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