efpr

Agradecido con Dios !!!
#BigData
#MachineLearning
#Python
#R
#Scala
#Libros

[efpr @ATuringAI73]

@KhusbooT14835 Google

[tut_ml @tut_ml]

Best ML Courses- https://t.co/Ugtu4RMMJO

@KirkDBorne
@antgrasso
@ronald_vanloon
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #AgenticAI #AIAgent

[Akshay 🚀 @akshay_pachaar]

I just built an open NotebookLM clone!

Here's what it can do for you:

- Process multi-modal data
- Scrape websites and YouTube videos
- Create a unified knowledge base
- Lets you do RAG over it
- Remember every conversation
- Generate a podcast 🎙️

The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step!

So by the end of this video, you'll learn how to:

↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs

↳ Store everything in a vector database for fast retrieval

↳ Add a memory layer that remembers conversations and preferences for a personalized experience

↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model

The podcast generation feature is my favorite part!

There's something powerful about turning written content into conversational audio that you can listen to while doing something else.

The entire code is 100% open-source. I've shared a link in the replies!
____
Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this.

Cheers! :)

[Neo Kim @systemdesignone]

10 blogs that will make you a 10x software engineer:

[Rosana Ferrero 📈📊🙌 @RosanaFerrero]

💡Lo que TODO analista de datos debería saber: conceptos, errores y modelos explicados.🤯
No basta con software o IA: comprender la estadística marca la diferencia entre resultados fiables y engañosos.

Recopilatorio de post importantes👇🧵

#DataScience #Stats #RStats #analytics

[efpr @ATuringAI73]

@Ai_here202 AI

[Swapna Kumar Panda @swapnakpanda]

The Channel: Alex The Analyst

❯ Data Analyst Bootcamp (Playlist):
https://t.co/qmzbHyby5X

❯ Data Analyst Bootcamp (Single Video):
https://t.co/rx9X2Uftls

[efpr @ATuringAI73]

@DAIEvolutionHub AI

[DataVidhya @thedatavidhya]

Best practices for Scalable Pipelines

[Tom Dörr @tom_doerr]

open-source framework for building and connecting AI agent networks

[Kirk Borne @KirkDBorne]

Simple Practical Data Cleaning #Python Code Snippets for messy data: https://t.co/d982YmvjUb
+
#DataCleaning Cookbook to prepare your data for analysis — PANDAS, NumPy, Matplotlib, scikit-learn, etc.: https://t.co/6AxvYTItfJ v/ @PacktDataML
—
#DataScience #AI #ML #DataScientist

[Ronald van Loon @Ronald_vanLoon]

How AI/ML Models are Built and Deployed in 12 Steps?
by @techyoutbe

#ArtificialIntelligence #MachineLearning #ML #Technology

cc: @ylecun @ogrisel @meisshaily

[tut_ml @tut_ml]

Best ML Courses- https://t.co/9fDSNMH4km

@KirkDBorne
@antgrasso
@ronald_vanloon
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #AgenticAI #AIAgent

[Victor @victor_explore]

Ever wondered how to actually build AI agents from scratch?
Saurav Prateek’s “AI Engineering 101” playlist is probably the best crash course out there right now — 19 videos walking through real agentic workflows using LangChain, Python, and more.

> Build Agentic Workflows (no-code + code)
> Design routing logic & prompt chaining
> Add memory + human-in-the-loop
> Integrate databases, APIs, and search
> Even create Self-RAG and Text-to-SQL Agents

No fluff. Just clean, practical engineering for real-world AI systems.
Do not sleep on this if you’re serious about agents.

Link in the comment.

[Dhanian 🗯️ @e_opore]

The Complete LLM & Generative AI Tech Stack

Foundation Models & Core Technologies
├── Large Language Models
│ ├── OpenAI: GPT-4, GPT-4o
│ ├── Anthropic: Claude 3
│ ├── Meta: Llama 2, Llama 3
│ └── Google: Gemini, PaLM 2
├── Open Source Alternatives
│ ├── Mistral AI models
│ ├── Falcon, Vicuna
│ ├── CodeLlama, StarCoder
│ └── Custom fine-tuned models
└── Model Architectures
├── Transformer-based
├── Attention mechanisms
├── Encoder-decoder models
└── Auto-regressive models

Development Frameworks & SDKs
├── Python Libraries
│ ├── LangChain, LlamaIndex
│ ├── Transformers
│ ├── PyTorch, TensorFlow
│ └── JAX, Flax
├── API Integration
│ ├── OpenAI Python SDK
│ ├── Anthropic API
│ ├── Google AI Python SDK
│ └── REST API wrappers
└── Development Tools
├── Jupyter Notebooks
├── VS Code with AI extensions
├── Google Colab, Kaggle
└── Weights & Biases

Prompt Engineering & Optimization
├── Prompt Design Patterns
│ ├── Zero-shot, Few-shot learning
│ ├── Chain-of-Thought
│ ├── ReAct
│ └── Self-consistency, Tree of Thoughts
├── Prompt Management
│ ├── Prompt templates
│ ├── Version control for prompts
│ ├── A/B testing frameworks
│ └── Prompt optimization tools
└── Advanced Techniques
├── Function calling
├── Tool use & planning
├── Memory management
└── Multi-modal prompting

RAG & Knowledge Enhancement
├── Retrieval-Augmented Generation
│ ├── Vector databases
│ ├── Document loaders & parsers
│ ├── Embedding models
│ └── Hybrid search systems
├── Vector Databases
│ ├── Pinecone, Weaviate
│ ├── Chroma, Qdrant
│ ├── Milvus, Vespa
│ └── PostgreSQL with pgvector
└── Data Processing
├── Text chunking strategies
├── Embedding generation
├── Semantic search
└── Knowledge graph integration

Application Patterns & Architectures
├── Common Patterns
│ ├── AI Agents
│ ├── Conversational AI
│ ├── Content generation systems
│ └── Code generation
├── System Design
│ ├── Chat interfaces
│ ├── Streaming responses
│ ├── Caching strategies
│ └── Rate limiting
└── Integration Patterns
├── API gateways for AI services
├── Middleware for LLM call
└── Circuit breakers

Model Management & Deployment
├── Model Serving
│ ├── Hugging Face Inference Endpoints
│ ├── AWS SageMaker, Google Vertex AI
│ ├── Azure Machine Learning
│ └── Custom model servers
├── Fine-tuning
│ ├── LoRA,
│ ├── Parameter-efficient fine-tuning
│ ├── RLHF
│ └── Custom training pipelines
└── Optimization
├── Quantization
├── Pruning
├── Model compression
└── Hardware acceleration

Monitoring & Evaluation
├── Performance Metrics
│ ├── Latency
│ ├── Cost per request tracking
│ ├── Token usage analytics
│ └── Quality metrics
├── LLM-specific Monitoring
└── Evaluation Frameworks

Security & Responsible AI
├── Safety Measures
│ ├── Content moderation
│ └── Ethical guidelines
├── Privacy
│ ├── Data anonymization
│ ├── GDPR
│ └── Audit trails
└── Risk Management
├── Bias detection
├── Fairness metrics
├── Transparency reports
└── Response plans

Emerging Technologies & Future Trends
├── Multi-modal AI
│ ├── Vision-Language models
│ ├── Audio processing
│ ├── Video understanding
│ └── Cross-modal retrieval
├── Advanced Architectures
│ ├─MoE
│ ├── Retrieval-based models
│ ├── Neuro-symbolic AI
│ └── World models
└── Production Considerations

Get my Complete LLM and Generative AI Development eBook here: https://t.co/DBXPEOcHrI

[efpr @ATuringAI73]

@CC_Sabathia Pa' lante mi llave, de la mano de Dios tendrás la victoria nuevamente, no estas solo, ánimo, Dios te bendice... amén 🙌

[Rosana Ferrero 📈📊🙌 @RosanaFerrero]

👀 ¿Buscas conjuntos de datos gratuitos para aprender/practicar/crear tu portfolio de #stats #datascience #MachineLearning?
Comparto algunos interesantes:👇🧵

(Comparte y completa la lista)

[Victor @victor_explore]

🔥FREE Official Machine Learning Course from Microsoft

- Comprehensive curriculum that will teach you Machine Learning from scratch with 12 weeks of hands-on projects, covering everything from regression to reinforcement learning.
- 26 lessons, 52 quizzes, and real-world applications using Python and R

[LangChain @LangChain]

🔄💬 Text-to-SQL Tutorial

Build a powerful natural language to SQL converter using LangChain, Ollama's DeepSeek model, and Streamlit. This tutorial shows you how to create an intuitive interface that automatically converts spoken queries into database-ready SQL.

🎥 Watch now: https://t.co/YZDrjPEcrL