The Presidency of Religious Affairs of the Grand Mosque and the Prophet’s Mosque has activated simultaneous interpretation services at the #Two_Holy_Mosques. The move is intended to enhance the #religious and #educational_services provided to pilgrims, enabling them to benefit from guidance in their respective #languages. The initiative will enrich their spiritual and knowledge experience, as the program provides prompt awareness and guidance content, as well as responses to #inquiries, in 17 international languages through state-of-the-art technologies that rely on #fast_voice and #text_processing. Ultimately, this initiative will help facilitate #communication with pilgrims of various nationalities and cultures, in addition to conveying #Sharia and guidance messages more clearly and accurately inside the Two Holy Mosques.

LLMs Roadmap ├── 01_Foundations │ ├── Programming_Basics │ │ ├── Python_for_AI │ │ │ ├── Data_Types_Functions_Modules │ │ │ ├── Virtual_Environments_Packages │ │ │ └── Real_Project │ │ │ └── Build_a_Text_File_Analyzer │ ├── Math_for_LLMs │ │ ├── Linear_Algebra_Basics │ │ ├── Probability_and_Statistics │ │ └── Intuition_Focused_Learning │ └── Computer_Science_Fundamentals │ ├── Data_Structures │ ├── Algorithms_Basics │ └── Complexity_Thinking │ ├── 02_Machine_Learning_Core │ ├── ML_Concepts │ │ ├── Supervised_vs_Unsupervised │ │ ├── Training_vs_Inference │ │ └── Overfitting_Underfitting │ ├── Tools │ │ ├── NumPy_Pandas │ │ ├── Scikit_Learn │ │ └── Matplotlib │ └── Real_Project │ └── Build_a_Text_Classifier_for_Support_Tickets │ ├── 03_Neural_Networks │ ├── Core_Concepts │ │ ├── Neurons_Layers_Activations │ │ ├── Backpropagation │ │ └── Loss_Functions │ ├── Frameworks │ │ ├── PyTorch │ │ └── TensorFlow │ └── Real_Project │ └── Build_a_Sentiment_Analysis_Model │ ├── 04_NLP_Foundations │ ├── Text_Processing │ │ ├── Tokenization │ │ ├── Stopwords_Stemming_Lemmatization │ │ └── Vectorization │ ├── Language_Models_Before_LLMs │ │ ├── N_Grams │ │ ├── Word2Vec_GloVe │ │ └── Limitations │ └── Real_Project │ └── Build_a_Smart_Search_Engine_for_Documents │ ├── 05_Transformers_and_LLM_Core │ ├── Transformer_Architecture │ │ ├── Self_Attention │ │ ├── Multi_Head_Attention │ │ ├── Positional_Encoding │ │ └── Encoder_vs_Decoder │ ├── Popular_LLMs │ │ ├── GPT_Family │ │ ├── LLaMA │ │ ├── Mistral │ │ └── Claude │ └── Real_Project │ └── Build_a_Mini_GPT_Text_Generator │ ├── 06_LLM_Usage_and_Engineering │ ├── Prompt_Engineering │ │ ├── Zero_Shot │ │ ├── Few_Shot │ │ ├── Chain_of_Thought │ │ └── System_Prompts │ ├── LLM_APIs │ │ ├── OpenAI_API │ │ ├── HuggingFace_Inference │ └── Real_Project │ └── Build_an_AI_Study_Assistant ├── 07_Retrieval_Augmented_Generation_RAG │ ├── Core_Idea │ │ ├── Embeddings │ │ ├── Vector_Databases │ │ └── Context_Injection │ ├── Tools │ └── Real_Project │ └── Build_a_Custom_Knowledgebase_Chatbot ├── 08_Fine_Tuning_and_Optimization │ ├── Fine_Tuning │ ├── Evaluation │ └── Real_Project │ └── Fine_Tune_an_LLM_for_Customer_Support │ ├── 09_AI_Agents_and_Tools │ ├── Agent_Concepts │ ├── Frameworks │ │ ├── LangChain │ └── Real_Project │ └── Build_an_AI_Agent_that_Automates_Research ├── 10_Deployment_and_Production │ ├── Model_Serving │ │ ├── FastAPI │ │ ├── Docker │ │ └── GPU_Usage │ ├── Cloud │ │ ├── AWS_GCP_Azure │ │ ├── Serverless_AI │ │ └── Cost_Optimization │ └── Real_Project │ └── Deploy_an_AI_Chat_App_for_Real_Users ├── 11_Responsible_and_Modern_AI │ ├── Ethics │ ├── Security/ │ └── Real_Project │ └── Secure_and_Monitor_an_LLM_Based_System └── 12_Capstone_Real_World_AI_Product ├── Idea │ ├── AI_SaaS │ ├── AI_Tutor │ ├── AI_Coding_Assistant │ └── AI_Business_Analytics_Tool ├── Build │ ├── End_to_End_System │ ├── Scalable_Architecture │ └── Real_User_Feedback └── Outcome └── Production_Ready_LLM_Product Grab this Ebook resource to deepen your LLM journey: codewithdhanian.gumroad.com/…

🔬 Excited to share the publication "Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing". Welcome to access the full article freely here 👉 mdpi.com/2306-5354/10/9/1070 #explainable_artificial_intelligence #XAI #text_processing #Grad_CAM #ResNet

Mastering Text Processing in Python with Regular Expressions link.medium.com/wDQ2qUY0Uxb #pythonprogramming #text_processing #python #python_re #regularexpression

How to split a huge line of characters into several lines with equal numbers of columns? - Visit programmatic.solutions/hmvzg… for the answer. #text_processing #bioinformatics #linux #computer #coders

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counting multiple patterns in a single pass with grep? - Visit programmatic.solutions/6mhfl… for the answer. #bash #shell_script #text_processing #grep #bioinformatics

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sort -z equivalent in Solaris ( support sorting of multi-line ) - Visit programmatic.solutions/jixmy… for the solution. #text_processing #sort #unix #computerengineering #dev

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for loops to grep multiple texts from parent file to multiple files in single command - Visit programmatic.solutions/rupkh… for the solution. #shell_script #text_processing #grep #bioinformatics #unix #linux

New #CatBoost 0.20 is out! This release contains text_processing library, which provides possibilities to easily preprocess text: lowercasing, tokenization, extraction of numbers or punctuation. And super fast building of dicts and BPE. Use it with boosting and with NN libs!

Fun #drupal fix of the day. A view was erroring with text_processing not being defined. Old field was text and new is int. #whynotviewmodes

دارم حرکتی جدی شروع می کنم به سمت رایانش متنی (بخوانید: تکست پراسسینگ) #text_processing