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Deep Learning News
Medium
6d
372
Image Credit: Medium
Experience of “Deep Learning Specialization” by Andrew Ng
- The 'Deep Learning Specialization' by Andrew Ng is a widely known and highly regarded course on Coursera.
- The specialization consists of 5 courses covering various topics such as neural networks, deep learning optimization, structuring machine learning projects, convolutional neural networks, and sequence models.
- The courses are designed to be completed in 3-5 months, but the duration can vary based on individual pace and commitment.
- Each course includes quizzes and programming assignments, and upon completion, course certificates are provided.
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Nvidia
6d
57
Image Credit: Nvidia
Dial It In: Data Centers Need New Metric for Energy Efficiency
- The formula for energy efficiency is simple: work done divided by energy used. Applying it to data centers calls for unpacking some details.
- Today’s most widely used gauge — power usage effectiveness (PUE) — compares the total energy a facility consumes to the amount its computing infrastructure uses.
- PUE served data centers well during the rise of cloud computing, and it will continue to be useful. But it’s insufficient in today’s generative AI era, when workloads and the systems running them have changed dramatically.
- Modern data center metrics should focus on energy, what the engineering community knows as kilowatt-hours or joules.
- The key is how much useful work they do with this energy.
- Ideally, any new benchmarks should measure advances in accelerated computing.
- Experts see the need for a new energy-efficiency metric, too.
- Jonathan Koomey, a researcher and author on computer efficiency and sustainability, agreed.
- “To make good decisions about efficiency, data center operators need a suite of benchmarks that measure the energy implications of today’s most widely used AI workloads,” said Koomey.
- More can and must be done to extend efficiency advances in the age of generative AI. Metrics of energy consumed doing useful work on today’s top applications can take supercomputing and data centers to a new level of energy efficiency.
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Medium
6d
265
Kili Paul Biography in Hindi/His Net Worth/Family/Career/Followers & Subscribers
- Kili Paul is a Tanzanian farmer, social media influencer, and dancer.
- He gained fame through his captivating performances on social media platforms.
- Kili Paul has a net worth of ₹20 to ₹25 lakhs.
- He has over 2 million subscribers on Instagram and 2 million followers on TikTok.
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Medium
6d
95
Elon Musk Biography in Hindi, Innovations,Age, Spouse, Children,NetWorth
- Elon Musk was born on June 28, 1971, in Pretoria, South Africa. His father's name is Errol Musk, and his mother's name is Maye Musk.
- Elon Musk pursued his education in the direction of space exploration. He completed his education from 'Pretoria Boys High School' and earned a bachelor's degree in physics from the University of Pretoria. He later went to the United States to pursue a PhD in energy physics at Stanford University.
- Elon Musk co-founded his first venture, Zip2 Corporation, in 1995. After it was acquired by Compaq for $307 million in 1999, Musk received $22 million for his share.
- Elon Musk founded SpaceX in 2002, an aerospace manufacturer and space transport services company. He also joined Tesla Motors, Inc. (now Tesla, Inc.) in 2004 as chairman of the board.
- Elon Musk's net worth is estimated to be over $150 billion. He is famous for his role as the CEO of SpaceX and Tesla, Inc., and his ambitious vision of colonizing Mars and transitioning the world to sustainable energy.
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Medium
7d
299
Gentle Parenting Myself As an Adult
- The author came across the concept of gentle parenting through social media.
- Although the author doesn't have children, they became interested in learning more about gentle parenting.
- Realizing the importance of leading by example, the author decided to apply gentle parenting techniques to themselves as an adult.
- The author plans to share more about their journey of gentle parenting themselves in the rest of the article.
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Medium
7d
8
Updates on Retrieval Augmented Generation part8(Machine Learning 2024)
- Retrieval-augmented generation (RAG) augments large language models (LLM) by retrieving relevant knowledge, showing promising potential in mitigating LLM hallucinations and enhancing response quality.
- Existing RAG systems are inadequate in answering multi-hop queries that require retrieving and reasoning over multiple pieces of supporting evidence.
- A novel dataset called MultiHop-RAG has been developed, consisting of a knowledge base, multi-hop queries, ground-truth answers, and supporting evidence.
- Experiments comparing different embedding models and state-of-the-art LLMs reveal that existing RAG methods perform unsatisfactorily in retrieving and answering multi-hop queries.
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Medium
7d
116
Updates on Retrieval Augmented Generation part7(Machine Learning 2024)
- Large Language Models (LLMs) have shown exceptional capabilities in natural language understanding and generation tasks.
- To address the personalization issue in dialogue systems involving multiple sources, a novel approach called Prompt-RAG (Prompt-based Retrieval-Augmented Generation) is proposed.
- Prompt-RAG enhances the performance of generative LLMs in niche domains without relying on embedding vectors.
- Evaluation results demonstrate that Prompt-RAG outperforms existing models in terms of relevance and informativeness in a Question-Answering chatbot application.
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Medium
7d
120
Updates on Retrieval Augmented Generation part2(Machine Learning 2024)
- Large language models (LLMs) with residual augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past.
- The proposed LLM-based system enables data authentication, user query routing, data retrieval, and custom prompting for question answering capabilities from large and varying data tables.
- The system is tuned for Enterprise-level data products and provides real-time responses in under 10 seconds.
- A five metric scoring module is proposed to detect and report hallucinations in LLM responses, achieving >90% confidence scores in sustainability, financial health, and social media domains.
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Medium
7d
357
Updates on Retrieval Augmented Generation part1(Machine Learning 2024)
- Evaluating open-ended written examination responses from students is a time-intensive task for educators.
- Large Language Models (LLMs) like ChatGPT-3.5, ChatGPT-4, Claude-3, and Mistral-Large show promise in assessing students' answers.
- There are notable variations in consistency and grading outcomes among the LLMs studied.
- Further research is needed to assess the accuracy and cost-effectiveness of using LLMs for educational assessments.
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Medium
7d
328
Latest Updates on 3D Gaussian Splatting part10(Machine Learning 2024)
- Conventional colonoscopy techniques face limitations in colorectal cancer diagnostics.
- A method called 'Gaussian Pancakes' is introduced to improve 3D reconstructions of the colonic surface.
- By combining 3D Gaussian Splatting with a RNNSLAM system, the method achieves more accurate alignment and smoother reconstructions.
- Evaluation results show improved view synthesis quality and faster rendering times, making it suitable for real-time applications.
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Medium
7d
303
Latest Updates on 3D Gaussian Splatting part8(Machine Learning 2024)
- 3D Gaussian Splatting is a popular method for neural rendering, but current methods have limitations in generalization and quality.
- This research proposes rethinking 3D Gaussians as random samples drawn from a probability distribution, using Markov Chain Monte Carlo (MCMC) samples.
- The proposed method shows similarities between 3D Gaussian updates and Stochastic Langevin Gradient Descent (SGLD) updates.
- The method offers improved rendering quality, control over the number of Gaussians, and robustness to initialization.
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Medium
7d
228
Image Credit: Medium
Latest Updates on 3D Gaussian Splatting part1(Machine Learning 2024)
- GS-LRM is a scalable large reconstruction model that predicts high-quality 3D Gaussian primitives from 2-4 sparse images.
- GS-LRM handles scenes with large variations in scale and complexity, outperforming state-of-the-art baselines in object and scene captures.
- SAGS is a structure-aware Gaussian Splatting method that encodes the geometry of the scene, resulting in improved rendering performance and reduced storage requirements.
- SAGS showcases a compact representation of the scene with up to 24x size reduction without the need for compression strategies, mitigating floating artifacts and irregular distortions.
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Medium
7d
287
Image Credit: Medium
RULER: Is your LLM using all his avaliable context?
- The paper titled RULER: What’s the Real Context Size of Your Long-Context Language Models? explores the context sizes of long-context language models.
- The study tests a wide range of context window sizes (4K - 128K) and provides insights into the performance of different models.
- Gemini 1.5 Pro and possibly Claude 3 show good performance on bigger contexts, while GPT-4 experiences a ~20% loss when using all available context.
- LLama 3, Mistral, and Film models are promising in terms of open-source development, but there are limitations in larger context sizes.
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Medium
7d
203
New methods with Collaborative Filtering part9(Machine Learning 2024)
- Contrastive learning-based recommendation algorithms have advanced self-supervised recommendation.
- The proposed framework leverages positive and negative augmentation to improve self-supervisory signal.
- The learning method maximizes likelihood estimation with latent variables representing user interest centers.
- The method achieves significant improvements over the BPR optimization objective while maintaining comparable runtime.
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Medium
7d
54
New methods with Collaborative Filtering part8(Machine Learning 2024)
- Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to enhance Recommender Systems (RSs).
- Existing work predominantly utilizes LLMs to generate knowledge-rich texts or utilizes LLM-derived embeddings as features to improve RSs.
- In this paper, the authors propose the Large Language Models enhanced Collaborative Filtering (LLM-CF) framework, which distils the world knowledge and reasoning capabilities of LLMs into collaborative filtering.
- Comprehensive experiments on three real-world datasets demonstrate that LLM-CF significantly enhances several backbone recommendation models and consistently outperforms competitive baselines, showcasing its effectiveness in distilling the world knowledge and reasoning capabilities of LLM into collaborative filtering.
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