DEV INVISIBLE TRADING
Canal cerrado
☎️Contact |Disclaimer: This channel owner is not a SEBI registered. I m just sharing my opinion only as an Investor cum trader.I m not responsible for any of your profit/loss with this channel's suggestion. Consult ur financial advisor before taking any
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📈 Análisis del canal de Telegram DEV INVISIBLE TRADING
El canal DEV INVISIBLE TRADING en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 19 650 suscriptores, ocupando la posición 8 793 en la categoría Criptomonedas y el puesto 24 800 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 19 650 suscriptores.
Según los últimos datos del 07 septiembre, 2025, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -507, y en las últimas 24 horas de 0, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 0 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
- Intereses temáticos: El contenido se centra en temas clave como (loss, ....., joining, karke, khud.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“☎️Contact |Disclaimer: This channel owner is not a SEBI registered. I m just sharing my opinion only as an Investor cum trader.I m not responsible for any of your profit/loss with this channel's suggestion. Consult ur financial advisor before taking...”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 11 noviembre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Criptomonedas.
19 650
Suscriptores
Sin datos24 horas
-787 días
-50730 días
Archivo de publicaciones
19 650
Data scientist 🤑💸🫰
Becoming a data scientist typically requires a combination of education, practical experience, and skill development. Here are the steps you can follow to become a data scientist:
1. Education: Obtain a bachelor's degree in a relevant field such as mathematics, statistics, computer science, or economics. It provides you with foundational knowledge in data analysis and programming.
2. Master's Degree: Consider getting a master's degree in data science or a related field. This can deepen your understanding of statistical modeling techniques, machine learning algorithms, and data visualization.
3. Gain Programming Skills: Develop expertise in programming languages commonly used in data science, such as Python or R. Learn to manipulate and analyze data using libraries like pandas, numpy, and scikit-learn.
4. Learn Statistical Analysis: Familiarize yourself with statistical concepts and techniques. Understand hypothesis testing, regression models, Bayesian inference, and experimental design.
5. Acquire Machine Learning Skills: Gain an understanding of machine learning algorithms and techniques. Learn how to apply them to solve real-world problems.
6. Industry Tools and Technologies: Familiarize yourself with common data science tools and technologies, such as SQL, Hadoop, Spark, and data visualization tools like Tableau or Power BI.
7. Build a Portfolio: Start working on data science projects to showcase your skills and knowledge. Create a portfolio that includes a variety of projects highlighting different aspects of data analysis and machine learning.
8. Networking: Attend data science meetups, conferences, and workshops to connect with professionals in the field. Engage in online communities and forums to learn from experienced data scientists and share ideas.
9. Internships and Real-world Experience: Seek internships or entry-level positions in data science or related roles. Gain practical experience working on real-world projects and contribute to solving business problems using data analysis.
10. Continuous Learning: Stay updated with the latest trends, techniques, and tools in data science. Continuous learning and self-improvement are essential to excel in this rapidly evolving field.
Remember, becoming a data scientist requires consistent effort and dedication. It is important to have a strong foundation in mathematics, statistics, and programming, as well as a keen interest in solving complex problems using data.
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How you can train Large Language Models?
Large language models (LLMs) are gaining significant popularity due to their versatility in text generation, translation, and question-answering tasks. However, training these models can be resource-intensive and time-consuming. LLMs examples include 𝐆𝐏𝐓-3 and 𝐆𝐏𝐓-4 from 𝐎𝐩𝐞𝐧𝐀𝐈, 𝐋𝐋𝐚𝐌𝐀 from 𝐌𝐞𝐭𝐚, 𝐚𝐧𝐝 𝐏𝐚𝐋𝐌2 from 𝐆𝐨𝐨𝐠𝐥𝐞.
Several LLM training frameworks have emerged to address this challenge, offering solutions to streamline and enhance the training process. Here are some of the most popular frameworks that help you to train and tuning LLMs Models:
✅ Deepspeed: An efficient deep learning optimization library that simplifies distributed training and inference, enabling easy and effective implementation.
Examples: https://www.deepspeed.ai/
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Examples: https://huggingface.co/blog/bloom-megatron-deepspeed
✅ FairScale: A PyTorch extension library designed for high-performance and large-scale training, empowering researchers and practitioners to train models more efficiently.
Example: https://fairscale.readthedocs.io/en/latest/tutorials/oss.html
✅ Megatron-LM: A research-focused framework dedicated to training transformer models at scale, facilitating ongoing exploration in the field.
Examples:https://huggingface.co/blog/megatron-training
✅ Colossal-AI: A platform that aims to make large AI models more accessible, faster, and cost-effective, contributing to democratizing AI advancements.
Examples: https://github.com/hpcaitech/ColossalAI/tree/main/examples
✅ BMTrain: An efficient training framework tailored for big models, enabling smoother and more effective training processes.
Examples: https://github.com/OpenBMB/BMTrain
✅ Mesh TensorFlow: A framework simplifying model parallelism, making it easier to leverage distributed computing resources for training large models.
Examples: https://github.com/tensorflow/mesh
✅ Max text: A performant and scalable Jax LLM framework designed to simplify the training process while maintaining high performance.
Examples: https://github.com/EleutherAI/maxtext
✅ Alpa: A system specifically developed for training and serving large-scale neural networks, offering comprehensive support for training requirements.
Examples: https://alpa.ai/opt
✅ GPT-NeoX: An implementation of model parallel autoregressive transformers on GPUs, built on the DeepSpeed library, providing enhanced training capabilities.
Examples: https://blog.eleuther.ai/announcing-20b/
If you're interested in training LLMs, I encourage you to explore these frameworks. They can significantly simplify and optimize the training process, allowing you to achieve better results efficiently.
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Bill Gates just published a 7-page letter about AI and his predictions for its future
Here is the summary:
✔️AI impact in the next 5-10 years:
-Change how people work, learn, travel, access healthcare, and communicate,
-Influence entire industries
✔️AI's potential for reducing global inequities:
-Health: save lives of children in poor countries
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-Climate change: help address issues faced by the world's poorest
✔️AI integration in the workplace:
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-Support the creation of personal agents and company-wide agents
✔️AI applications in healthcare:
-Assist healthcare workers with tasks and paperwork
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-Accelerate medical breakthroughs by analyzing complex biological data
-Incentivize companies to share AI-generated insights for agriculture in poor countries
-AI can improve seeds, advise farmers, and develop drugs for livestock
-AI-driven software will revolutionize education in the next 5-10 years
-AI can assist teachers and administrators in various ways
-Ensure AI tools are available for low-income schools and address the digital divide
-Address risks and problems with AI, including context understanding and -abstract reasoning
-Governments and the private sector should collaborate to limit AI risks
-Super intelligent AIs raise questions about goals and potential conflict with humanity
-AI advancements will lead to new hardware, software, and applications
-Balance AI's downsides with its potential to improve lives
Governments and philanthropy should ensure AI reduces inequity
AI limitations today will be overcome as the technology advances
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