Jua.ai’s Post

Google DeepMind has a fully developed neural network mathematical reasoning that we are integrating into NextGenjax, a library for thinking and development analysis https://lnkd.in/gR4WnFqm I am impressed Google DeepMind

🚀🔍✨ 🔍Compared ChatGPT 3.5 and Claude 3 Sonnet in a Machine Learning Showdown! 🏆 🔥 Taking on the challenge at the Kaggle Titanic Competition, here's what I found: 🔹 Neural Network Implementation: ChatGPT 3.5: 0.77272 Claude 3 Sonnet: 0.7177 🔹 Logistic Regression Implementation: ChatGPT 3.5: 0.68899 Claude 3 Sonnet: 0.72727 🔹 Algorithm of Choice: (Asked to use the suited Algorithm) ChatGPT 3.5 (NN): 0.72488 Claude 3 Sonnet (Logistic Regression): 0.72966 🎉 Results Speak: Claude 3 Sonnet took the lead in 2 scenarios, while ChatGPT 3.5 claimed victory in 1. To put these results into perspective, guessing all women survived and all men did not survive gives a score of 0.76555 🤔 Debate? You Bet! Does Claude's win make it better? It's open for discussion. Claude may have clinched victory, but encountered some coding hiccups along the way which had to be fixed manually or asked back to the model. 🛠️ Both models are formidable, each with its own strengths and potential use cases. 🌟 🏁 The LLM Race is Just Getting Started! 🏁 https://lnkd.in/eREPRgZw #MachineLearning #NLP #NeuralNetworks #LogisticRegression #ChatGPTvsClaude #KaggleChallenge

🚀 Introducing DBRX🚀 Exciting news from Databricks! DBRX is here, an open Large Language Model (LLM) setting new standards in AI evolution Key Features Advanced Capabilities - Access advanced features previously limited to closed models Performance - Outperforms GPT-3.5 in programming, mathematics, and retrieval-augmented generation Availability - Available as DBRX Base and DBRX Instruct via Hugging Face and Databricks Marketplace Impact DBRX's launch marks a shift where open LLMs rival closed models, democratizing advanced AI capabilities. Join us as we unlock AI's true potential with DBRX! #Databricks #DBRX #AI #LLM #ArtificialIntelligence #Innovation #DataPattern #GenAI

Helloo friends i have completed a project on House price prediction using Neural network, with features more than 70. The purpose of this excercise to build a nueral network to predict price of the respective property based on above features .The model has a r2 score of 0.84. #kaggle #kaggle_competetion #neural_network #deep_learning #regression

Thrilled to share my first blog about machine learning 🤖 ! Delving into the transformative power of algorithms, Excited to embark on this journey of discovery! #MachineLearning #FirstBlog

𝗘𝗹𝗼𝗻 𝗠𝘂𝘀𝗸’𝘀 𝘅𝗔𝗜 𝗵𝗲𝗮𝘁𝘀 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗚𝗿𝗼𝗸-𝟮 𝗺𝗶𝗻𝗶 𝗻𝗼𝘄 𝗹𝗶𝘃𝗲 𝗼𝗻 𝕏 Elon announced their latest LLM: Grok-2 and Grok-2-mini. They’ve made major improvements since the November launch of Grok-1 and are already claiming to outperform Claude Sonnet 3.5 and GPT-4 turbo. It’s impressive, considering xAI is less than a year old company. 𝘐 𝘵𝘩𝘪𝘯𝘬 𝘹𝘈𝘐 𝘩𝘢𝘴 𝘢𝘯 𝘪𝘯𝘤𝘳𝘦𝘥𝘪𝘣𝘭𝘦 𝘢𝘥𝘷𝘢𝘯𝘵𝘢𝘨𝘦 𝘸𝘩𝘦𝘯 𝘵𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘵𝘩𝘦𝘴𝘦 𝘮𝘰𝘥𝘦𝘭𝘴 𝘩𝘰𝘸 𝘵𝘰 𝘤𝘩𝘢𝘵 𝘢𝘯𝘥 𝘦𝘯𝘨𝘢𝘨𝘦. Why? It’s simple: Elon owns 𝕏, which provides a huge source of chat data, real-time information, and news. Not to mention his other companies and potential to train these models on that data. With their recent announcement of a 100,000 H100 GPU cluster and a strong team, they’re gearing up to compete closely with OpenAI, DeepMind, and Anthropic.

During the “Production-ready RAG or RAG Challenges” Papers Club session, the moderator and an IBM AI Engineer, Paula Rodríguez de V. Azor, suggested sharing valuable tools and platforms to build RAG systems. LlamaIndex, Rasa, PyTorch, Gradio, Neo4j, Weaviate, and many more were mentioned. We put a few of them into a handy list for you to use. Swipe to learn more 👉 #ragsystems #RAGplatforms #RAGtools

The Uncertain Art of Accelerating ML Models with Sylvain Gugger: Sylvain Gugger is a former math teacher who fell into machine learning via a MOOC and became an expert in the low-level performance details of neural networks. He’s now on the ML infrastructure team at Jane Street, where he helps traders speed up their models. In this episode, Sylvain and Ron go deep on learning rate schedules; the subtle performance bugs PyTorch lets you write; how to keep a hungry GPU well-fed; and lots more, including the foremost importance of reproducibility in training runs. They also discuss some of the unique challenges of doing ML in the world of trading, like the unusual size and shape of market data and the need to do inference at shockingly low latencies. You can find the transcript for this episode  on our website. Some links to topics that came up in the discussion: * “Practical Deep Learning for Coders,” a FastAI MOOC by Jeremy Howard, and the book, of which Sylvain is a co-author. * The Stanford DAWNBench competition that Sylvain participated in. * HuggingFace, and the Accelerate library that Sylvain wrote there. * Some of the languages/systems for expression ML models that were discussed: PyTorch, TensorFlow, Jax, Mojo, and Triton * CUDA graphs and streams * Hogwild concurrency #OCaml #OCamlPlanet

Exciting (if slightly scary) advances in #GenAI are on the horizon with a new research paper describing a GenAI model independently discovering algorithms that can boost GenAI performance. Find out why this matters and what it could mean for future #regulation in our latest #techinsights post by James Phoenix.

Google has introduced Gemma, a family of lightweight and open AI models developed by Google DeepMind. Comprising variants like Gemma 2B and 7B, these models are built from the same research and technology used for Gemini. Gemma is designed to support developers and researchers in building AI responsibly, offering compatibility with tools such as Colab and Kaggle notebooks, and frameworks like JAX, PyTorch, Keras 3.0, and Hugging Face Transformers. The models aim to surpass larger counterparts on key benchmarks while adhering to safety and responsibility standards. #Google #Gemma #ArtificialIntelligence #AIModels