We're excited to announce Generative AI for Software Development, a new course, now available for pre-enrollment. Generative AI is reshaping developers' workflows, and this course offers a comprehensive pathway to understand and apply generative AI technologies in real-world software development. Taught by Laurence Moroney, Chief AI Scientist at VisionWorks Studios and former AI lead at Google, you’ll learn to use LLMs to assist with the core functions of a software developer or engineer, including code generation, optimization, debugging, and documentation, and you’ll enhance your efficiency and creativity through the tools and techniques you'll explore. 🔗 Integrate generative AI tools into your workflow. 🐛 Optimize and debug code with AI. 🚀 Develop advanced software solutions using AI. Generative AI for Software Development is available for pre-enrollment on Coursera now, and you can receive a certificate upon successful completion! Pre-enroll now and be the first to join: https://hubs.la/Q02GL_tx0
DeepLearning.AI
Software Development
Palo Alto, California 1,031,860 followers
Making world-class AI education accessible to everyone
About us
DeepLearning.AI is making a world-class AI education accessible to people around the globe. DeepLearning.AI was founded by Andrew Ng, a global leader in AI.
- Website
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http://DeepLearning.AI
External link for DeepLearning.AI
- Industry
- Software Development
- Company size
- 11-50 employees
- Headquarters
- Palo Alto, California
- Type
- Privately Held
- Founded
- 2017
- Specialties
- Artificial Intelligence, Deep Learning, and Machine Learning
Products
DeepLearning.AI
Online Course Platforms
Learn the skills to start or advance your AI career | World-class education | Hands-on training | Collaborative community of peers and mentors.
Locations
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Primary
2445 Faber Pl
Palo Alto, California 94303, US
Employees at DeepLearning.AI
Updates
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Meta will withhold its future multimodal AI models from the European Union to avoid potential fines or bans related to the region’s privacy laws. While the newly released Llama 3.1, which processes text only, will be available, future multimodal models will not. Learn more about this decision in #TheBatch: https://hubs.la/Q02J6XcN0
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DeepLearning.AI reposted this
I recently embarked on a new research project in the area of federated learning and was recommended (thank you, Daniel Mauricio Jiménez Gutiérrez) two insightful short courses: - Intro to Federated Learning (C1) and - Federated Fine-tuning of LLMs with Private Data (C2). Offered by DeepLearning.AI in collaboration with Flower Labs and guided by experts Daniel J. Beutel and Nicholas Lane, these courses are designed to be completed in less than two hours each. C1 covers the basics of federated training, tuning, data privacy, and bandwidth management, while C2 focuses on applying federated learning to large language models, addressing data memorization, resource requirements, and privacy techniques like Parameter-Efficient Fine-Tuning and Differential Privacy. I am thankful for the recommendation and found the courses to be incredibly valuable, providing practical insights and essential skills for effective implementation in the rapidly evolving field of federated learning. 🔗 Link to the courses: https://lnkd.in/dRYaqKqU
Yelizaveta Falkouskaya, congratulations on completing Intro to Federated Learning!
learn.deeplearning.ai
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DeepLearning.AI reposted this
🚀 Exciting News! We've launched #federatedlearning 🎓 courses together with DeepLearning.AI and Andrew Ng 👉 Learn more and enroll for free: https://buff.ly/4df2dAy 🎓 Two courses are available: 1️⃣ Intro to Federated Learning taught by Daniel J. Beutel, co-founder and CEO of Flower Labs 2️⃣ Federated Fine-tuning of LLMs with Private Data taught by Nicholas Lane, co-founder and Chief Scientific Officer of Flower Labs 🤩 We will host an AMA session for these courses on Monday July 29th 16:00 UTC. For details about the AMA, and additional course material join the #course-deeplearning-ai channel on 🌼 Flower Slack. Simply scan the QR code in the video or click here: https://buff.ly/3YONpTy then join the course channel! We have designed these courses to be very accessible. The will suit anyone with a basic knowledge of python and machine learning, an appreciation of LLM concepts, and an interest in building AI models on private or distributed data using the Flower framework. 🍿👩🎓 Enjoy!
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OpenAI unveiled GPT-4o Mini, a compact yet powerful multimodal generative model designed to outperform its peers at a lower cost. This model supports text and image inputs with text outputs, with plans to include video and audio functionalities soon. GPT-4o Mini is available for API access at significantly reduced rates compared to larger models, making it an attractive option for developers needing to process extensive data efficiently. Learn more in #TheBatch: https://hubs.la/Q02H-5-l0
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This week, featured in The Batch: ➡️ All about GPT-4o Mini ➡️ Meta limits models in EU ➡️ Why VCs are stockpiling GPUs Plus, Andrew Ng shares his thoughts on why AI startups may want to begin by imagining a concrete product to test rather than a general problem to solve. Read #TheBatch: https://hubs.la/Q02HTGv00
OpenAI Shrinks GPT-4o, Meta Withholds Models From Europe, and more
deeplearning.ai
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DeepLearning.AI reposted this
Learn to train an LLM with distributed data while ensuring privacy using federated learning in a new two-part short course, Intro to Federated Learning and Federated Fine-tuning of LLMs with Private Data, created with Flower Labs and taught by Daniel J. Beutel and Nicholas Lane. Federated learning allows a single model to be trained across multiple devices, such as phones, or multiple organizations, such as hospitals, without the need to share data to a central server. This two-part course gives you an introduction to federated learning, and then teaches you how to fine-tune your large language model with distributed data using Flower Lab’s open source federated learning framework. You’ll learn: - How to use federated learning to train a variety of models, ranging from speech and vision models to LLMs, across distributed data while offering data privacy options to users and organizations. - Privacy Enhancing Technologies like differential privacy (DP), which obscures individual data by adding calibrated noise to query results. - Two variants of differential privacy - Central and Local - and how to choose depending on your use case. - How to measure and decrease bandwidth usage to make federated learning more practical and efficient with techniques like using pre-trained models and Parameter-Efficient Fine-Tuning - How federated LLM fine-tuning reduces the risk of leaking training data. Sign up here! https://lnkd.in/gajf4wSE
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Announcing a two-part Federated Learning course series, in collaboration with Flower Labs! Federated learning allows models to be trained across multiple devices or organizations without sharing data, and in this course series, instructed by Flower Labs’ Daniel J. Beutel and Nicholas Lane, you’ll learn to use the Flower framework to build federated learning systems and fine-tune LLMs with private data. 1️⃣ The first course, Intro to Federated Learning, covers the basics of federated training, tuning, data privacy, and bandwidth management. 2️⃣ The second course, Federated Fine-tuning of LLMs with Private Data, focuses on applying federated learning to LLMs, including data memorization and resource requirements, with a focus on efficiency and privacy techniques like PEFT and DP. Start with the course that matches your knowledge level in federated learning and start today!
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Researchers at the University of Oxford developed a method to identify hallucinations in large language model outputs. Their approach estimates the likelihood of hallucinations by calculating the degree of uncertainty based on the distribution of generated meanings rather than word sequences. This method outperformed traditional entropy and P(True) methods, achieving an average AUROC of .790 across multiple datasets and models. Read our summary of the paper in #TheBatch: https://hubs.la/Q02HKD990
Oxford Scientists Propose Effective Method to Detect AI Hallucinations
deeplearning.ai
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Artificial Analysis launched the Text to Image Arena leaderboard, where the public judges head-to-head contests between top text-to-image AI models. Models are ranked using Elo ratings, and users can see personalized leaderboards based on their voting history. Learn which models are leading the arena in #TheBatch: https://hubs.la/Q02HKGbb0
Text-to-Image Generators Face Off in Arena Leaderboard by Artificial Analysis
deeplearning.ai