mohamed shaban’s Post

I have some interesting experimental prompt frameworks coming. I will provide templates for these publicly. These prompts will focus on reasoning and thinking with Claude 3.5 Sonnet.

📢 New Research Alert! Check out this insightful blog post on "Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting." The post discusses the effectiveness of feature-based offline algorithm selection and the importance of constructing well-designed algorithm portfolios. Find the full article here: https://bit.ly/4dGfH9A #AlgorithmSelection #Optimization #ResearchInsights

Approximate Nearest Neighbor (ANN) algorithms allow vector databases to quickly find similar items in large datasets! Unlike exact search methods (kNN), ANN algorithms trade off some accuracy for significant speed improvements, powering features like recommendation systems, image recognition, and semantic search engines. Read more about Weaviate’s custom-built ANN algorithm: https://lnkd.in/g39efvbh

New on our blog: An Introduction to #Observability for #LLM-based applications using #OpenTelemetry

You should read this great article I just wrote: Unlocking the Power of Your Supercomputer Mind: What One Can Do, One Must Do https://lnkd.in/eGeV6Dp4

We have been using Algorithms knowingly or unknowingly. Would you like to brush up your knowledge on Algorithms within next two minutes ? Then this is the post.

🌟 New Research Announcement! 🌟 Exciting news on the convergence properties of convex message passing algorithms in graphical models. This latest blog post presents a novel proof technique to prove convergence to a fixed point, with the added achievement of precision $\varepsilon\>0$ in $\mathcal\{O\}\(1/\varepsilon\)$ iterations. Read the full article here: https://bit.ly/3ICai5k \#SocialMediaMarketing \#ResearchUpdate \#ConvexAlgorithms \#GraphicalModels \#NewResearch \#ConvergenceProperties

Leave no context behind: Efficient infinite context transformers with infini-attention This is a new research paper by google which proposes a novel approach to scale the transformer-based LLMs to process infinitely long input sequences. They're calling this new attention technique as infini-attention. Currently transformers have a limited size context window. So what they do is they integrated a memory module into the old-vanilla attention mechanism and builds both local and global attention in the same transformer block. You can see in the image below infini-Transformer has entire context history whereas the Transformer-XL discards old contexts. The attention uses KV states to focus on important pieces of the sequence and discards them once they have been used to update the attention matrix. The infini-attention stores these old KV states in the compressive memory and retrieves them while processing next sequences. So basically they reuse these old KV states to maintain entire contextual history. This effective memory system can help LLMs unlock capabilities never seen before. You can read the paper here: https://lnkd.in/dA3p2Qef Yannic has a nice paper read video on this https://lnkd.in/dAhHyjRi

What if I told you that achieving state-of-the-art image recognition with CNNs doesn’t have to come at the expense of excessive model complexity? Balancing model complexity while maintaining performance is within reach by applying these proven strategies: 1. Leverage efficient architectures like VGNetG, MobileNets, and EfficientNets that excel in accuracy with fewer parameters. 2. Employ pruning strategies, using L2 regularization and dropout layers to minimize overfitting and keep models lightweight. 3. Enhance your datasets through data augmentation techniques such as zooming, flipping, and rotation for richer training data. 4. Optimize hyperparameters meticulously, focusing on learning rates, epochs, and optimizers like Adam and SGD to tailor performance. 5. Implement robust training algorithms like Batch Normalization and attention mechanisms to sharpen focus on essential features. Ready to elevate your CNN's performance while ensuring efficiency? Reply with “YES” and I’ll provide insights on integrating these strategies into your workflow for exceptional results.

169. Majority Element 🧮 - Learned to identify the majority element in an array using efficient algorithms.