Puffrace @puffrace

@DanielLutaaya @MKarekye 😆 🤣 😂

@SpireJim @Kanunguprince You are “somebody” in this country imagine what is happening to ordinary citizens , especially when they are biased

@ug_xi69950 @kirya_ug Lol i think most Ugandans suspending thier thinking when it becomes spiritual

@SpireJim But why can't we just request and push for the dismal of the paranoid minister

@emmanuel_dombo Are u comparing ur pastors to Jesus ??? #blasphemy. Flip an egg , the same Bible talks about false prophets . We can do the cherry picking the entire conversation. We want evidence, these men are callous fraudsters that ride on ir gullible naive ways

@mukulaa There are crucial factors u deliberately ignore and we all know they smoothen that process. So let's all pretend we don't know them and just whine ..

@PoliceUg @mkituuma @cobbo3 @NBSBreakingNews @ntvuganda to the least of things u can show us how the men in uniform in the barracks have embraced this shift..🏃‍♂️

@DanielLutaaya @cobbo3 @rkabushenga And u forgot the shitty poor journalism that has failed us

@AAntony94 @SpireJim Simple answers are never adequate , but I think this is about the VALUE we attach to CULTURE . This to very basic a symbol of culture and what is man without an identity or culture , even Jacob worked for Racheal. So are u xtian?

@AAntony94 @SpireJim Which language ? Why do u think u understood it ? Simple answers are never adequate

@DailyMonitor Poor journalism at its best, where is the side of the person being accused ? My guts tell me this soldier encroached on the victim's land. The act of wearing that uniform when retired, what message is he sending ? #PoorJournalism we deserve better

@DavidKaahw92816 @sejudav But the boldness in rendering the rest of us as idiots . Is just incredibly top roof. We all know the system doesnt work

@sejudav Do the plates even delivery the security layer they promised ? Its absurd and painful , how an entire country is hoodwinked in plain sight. Were the changes necesaary ? The british should come and take back thier colony

@KeturahMidi @irumanyika Remove king , I think he was the only Oyo we knew

Learn Probability- mltut.com/best-resources…

The absolutely beautiful free hands-on book "Physics-Based Deep Learning Book" by Thuerey et al. (2026) is now in @ChapterPal's collection. The textbook is an applied guide to physics-based deep learning, also known as scientific machine learning, which blends numerical physical simulations with modern artificial intelligence architectures. Designed for researchers, engineers, and students with a working knowledge of deep learning fundamentals and basic partial differential equations, the book focuses on practical implementations using deep learning frameworks such as PyTorch, JAX, and the differentiable simulation library ΦFlow. The textbook, at 350 pages, has 163 illustrations and 39 hands-on coding exercises. Read the book with an AI tutor: chapterpal.com/book/4d08ce3b-… All books on ChapterPal are free to read with a free account. Table of contents: Chapter 1: Introduction - Welcome ... * Coming up * Comments and suggestions * Thanks! * Citation * Time to get started - A Teaser Example * Differentiable physics * Finding the inverse function of a parabola * A differentiable physics approach - A Probabilistic Generative AI Approach * Discussion * Next steps - Overview * Motivation * Categorization * Looking ahead * Implementations - Models and Equations * Deep learning and neural networks * Partial differential equations as physical models * Some example PDEs * Preliminaries * Newton's method * Approximating the Hessian * Broyden's method * BFGS * Gauss-Newton * Gradient Descent Chapter 2: Neural Surrogates and Operators - Supervised Training * Problem setting Chapter 3: Physical Losses - Physical Loss Terms * Using physical models Chapter 4: Differentiable Physics - Introduction to Differentiable Physics * Differentiable operators * Jacobians * Learning via DP operators * A practical example * Backpropagation through solver steps * Alternatives: noise * Complex examples - Reducing Numerical Errors with Neural Operators * Problem formulation * Getting started with the implementation * Simulation setup * Network and transfer functions * Training setup * Interleaving simulation and NN * Test evaluation * Next steps - Solving Inverse Problems with NNs * Formulation * Control of incompressible fluids * Data generation * Supervised initialization * CFE pretraining with differentiable physics * End-to-end training with differentiable physics * Next steps - Discussion of Differentiable Physics * Integration * Reducing data shift via interaction * Generalization Chapter 5: Probabilistic Learning - Introduction to Probabilistic Learning * Uncertainty * Forward or Backward? * Simulation-based Inference - Learning a Probability Distribution * Fundamentals: A Training Objective * From Unconditional to Conditional * Learning Distributions with Normalizing Flows * Practical Example: Learning Gaussians * A Simple Normalizing Flow based on Affine Couplings * Neural ODEs: Making Normalizing Flows Continuous * Summary of Normalizing Flows - Score Matching * Gaussian Toy Dataset with Analytic Scores * Learning the Score * Langevin Dynamics * Full Denoising Algorithm * Training with DDPM - Flow Matching * Learning Flows with Velocities * Mappings and Conditioning * Score Matching with Differentiable Physics * Summary of Physics-based Diffusion Models - Probabilistic Inverse Problem with Differentiable Simulations * Toy Problem setup * Conditioning * Implementation * Backbone Network Definition * Variance Schedule * Diffusion Model Definition * Training * Test Dataset * Test Inference * Accuracy of the Prediction * Summarizing Time Predictions with Diffusion Models - Unconditional Stability * Main Considerations for an Evaluation * Comparing Architectures * Stability Criteria * Batch Size vs Rollout * Summary - Graph-based Diffusion Models * Diffusion Graph Net (DGN) * Diffusion on Graphs * Diffusion in Latent Space * Turbulent Flows around Wings in 3D * Distributional accuracy * Computational Performance - Distributional Accuracy of Diffusion Graph Nets * Implementation * Sample-wise Accuracy * Evaluating Distributional Accuracy - Discussion of Probabilistic Learning Chapter 6: Reinforcement Learning - Introduction to Reinforcement Learning Chapter 7: Improved Gradients - Scale-Invariance and Inversion * The crux of the matter * Traditional optimization methods * Quasi-Newton methods * Inverse simulators * NN training * Loss functions * Iterations and time dependence * SIP training in action * Discussion of SIP Training - Learning to Invert Heat Conduction with Scale-invariant Updates * Problem Statement * Implementation * Data generation * Differentiable physics and gradient descent * Stable SIP gradients * Neural network and loss function * Training * Evaluation * Next steps - Half-Inverse Gradients * Derivation Chapter 8: Fast Forward Topics - Additional Topics - Model Reduction and Time Series * Reduced order models * Time series * End-to-end training * Source code - Unstructured Meshes and Meshless Methods * Types of computational meshes * Unstructured meshes and graph neural networks * Meshless and particle-based methods * Continuous convolutions * Learning the dynamics of liquids * Source code - Generative Adversarial Networks * Maximum likelihood estimation Chapter 9: Outlook - Outlook * Some specific directions * Closing remarks References Notation and Abbreviations - Math notation: - Summary of the most important abbreviations:

@Chiomabtq The post seems designed to attract uninformed hatred under the guise of ignorance. If your intentions are genuinely clean, why not post crews from other airlines as well, so that people can make a fair and informed comparison?

@kirenzi_samson @FrankTumwebazek @addy_dian @AndrewMwenda 😆 🤣 now u need to finish the last part....uuhm all ears

@FrankTumwebazek @AndrewMwenda Now that is a voice of an elder. Salute Honorable