AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

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The AWS Certified Machine Learning Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.

Download AWS machine Learning Specialty Exam Prep App on iOs

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AWS MLS-C01 Machine Learning Specialty Exam Prep PRO

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AWS machine learning certification prep
AWS machine learning certification prep

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

The App provides hundreds of quizzes and practice exam about:

– Machine Learning Operation on AWS

– Modelling

– Data Engineering

– Computer Vision,

– Exploratory Data Analysis,

– ML implementation & Operations

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision, Python, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.

Domain 1: Data Engineering

Create data repositories for machine learning.

Identify data sources (e.g., content and location, primary sources such as user data)

Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)

Identify and implement a data ingestion solution.

Data job styles/types (batch load, streaming)

Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modeling.

Perform feature engineering.

Analyze and visualize data for machine learning.

Domain 3: Modeling

Frame business problems as machine learning problems.

Select the appropriate model(s) for a given machine learning problem.

Train machine learning models.

Perform hyperparameter optimization.

Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

Build machine learning solutions for performance, availability, scalability, resiliency, and fault


Recommend and implement the appropriate machine learning services and features for a given


Apply basic AWS security practices to machine learning solutions.

Deploy and operationalize machine learning solutions.

Machine Learning Services covered:

Amazon Comprehend

AWS Deep Learning AMIs (DLAMI)

AWS DeepLens

Amazon Forecast

Amazon Fraud Detector

Amazon Lex

Amazon Polly

Amazon Rekognition

Amazon SageMaker

Amazon Textract

Amazon Transcribe

Amazon Translate

Other Services and topics covered are:



Data analysis/visualization

Model training

Model deployment/inference


AWS ML application services

Language relevant to ML (for example, Python, Java, Scala, R, SQL)

Notebooks and integrated development environments (IDEs),

S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena

Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service , Amazon Redshift

Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.

Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.

Download AWS machine Learning Specialty Exam Prep App on iOs

Download AWS Machine Learning Specialty Exam Prep App on Android/Web/Amazon

  • [D] What Are the Fundamental Drawbacks of Mamba Compared to Transformers?
    by /u/Alarmed-Profile5736 (Machine Learning) on February 24, 2024 at 7:11 am

    Hello! I've been pondering this question for some time. To clarify, I'm not referring to aspects like "it hasn't been tested extensively," "its scalability is uncertain," or "there's a lack of industry infrastructure." Instead, I'm interested in understanding the core differences between the transformer and Mamba architectures, specifically how these differences may place Mamba at a disadvantage compared to Transformers. Best regards! submitted by /u/Alarmed-Profile5736 [link] [comments]

  • [R] Dataset Requirements
    by /u/Ok-Art5200 (Machine Learning) on February 24, 2024 at 6:34 am

    Need help to find out OMOP(Observational Medical Outcome Partnership) free dataset for an academic project. submitted by /u/Ok-Art5200 [link] [comments]

  • Can anyone help i wanna become machine learning engineer [discussion]
    by /u/SatisfactionSea7994 (Machine Learning) on February 24, 2024 at 6:03 am

    I am 2 year IT student really wanna become a machine learning engineer but due to vast availability of contents i get frustrated and skip all the time and by doing that I’m not going anywhere that would be se helpful if anyone can help🥺😊 . Any free course or any path i can follow? And do i need a good DSA knowledge to become a machine leaning engineer? Or data scientist? submitted by /u/SatisfactionSea7994 [link] [comments]

  • [P] Box Detection in Warehouse using Vision Based ML Engineering (Yolov8, FastAPI, Docker)
    by /u/Snoo_72181 (Machine Learning) on February 24, 2024 at 2:13 am

    I'm thrilled to showcase my first end-to-end ML engineering project for Image Object Detection that I recently developed, leveraging MLOps principles to streamline the deployment process and ensure scalability and reliability. The project focuses on boxed container detection in warehouse conveyor belts. Here's a brief overview of the key components and methodologies: 🔍 Data Preparation and Model Training - I began by downloading the dataset from Roboflow and trained the YOLOv8 model on the train set using transfer learning. Through rigorous validation and testing, the model achieved an accuracy (mean Average Precision or mAP) of over 90%. 🛠️ Software Engineering with FAST API - Next, I transitioned to software engineering aspects, where I developed a FastAPI web application to serve the trained model to end users. The application includes a user-friendly interface for uploading conveyor belt images and receiving predictions with labeled boxes. Web UI image attached below. 📦 Deployment with Docker Containerization - To facilitate easy deployment and accessibility, I containerized the FastAPI application using Docker. This allows end users to seamlessly install and run the application without worrying about dependencies or environment setup. For more details into how this project was developed, I urge you to check out the flowchart below : As next steps, I would be working on adding some unit and integration tests, as well as implementing monitoring and automated feedback mechanisms to trigger model training, so as to avoid data and model drift. Docker Image with instruction on how to run the object detection web app - Code Repository for this project - submitted by /u/Snoo_72181 [link] [comments]

  • [D] When writing ML software - how do you use TDD?
    by /u/Due-Function4447 (Machine Learning) on February 24, 2024 at 2:05 am

    Please let me know if there is a better sub for this. Test-driven-development. I’ve been working on ML software for a while now and I feel like i have spurts of wanting to get better at following TDD and trying to apply that to more nuanced ML use cases. One thing i’ve noticed over the years is requirements and design can be hazy for our work - a lot of what I do at least starts off with the simplest design and then we rely on iteration and a robust evaluation framework to justify if certain improvements will be implemented (why implement anything if it doesn’t improve performance). In these types of prototyping scenarios, TDD can be a huge time killer and a bit useless until you nail down your design. Still, it’s pretty great when requirements are clear, so i’m trying to get better at including it in my arsenal. What are your thoughts on TDD and how/when do you use it? submitted by /u/Due-Function4447 [link] [comments]

  • [D] Exploring Ideas: Advancements in End-to-End Multi-Task Text-to-Speech
    by /u/ReinforcedKnowledge (Machine Learning) on February 24, 2024 at 12:22 am

    Hi! I got curious about speaker diarization these days and looked into what people are using like combinations of whisper with pyannote etc. And since I'm not in research I would to hear from you what are some ideas that people are exploring in end-to-end multi-task text-to-speech. I see a lot of work in multi-lingual, low-resource text-to-speech, but not that much about multi-task that goes beyond multi-language translation. I tried to extend Whisper to perform speaker diarization but it didn't work well. Especially since there is no way to keep the speaker identification from one segment to another (Whisper only works on 30s audio). So I was thinking, if you want to extend Whisper to new tasks, you are not only limited by tasks that should be contained in 30s audio clips, but also by the fact that fine-tuning a model by introducing a new special token for this specific task makes the fine-tuning harder. So I was wondering, are there any promising end-to-end multi-task text-to-speech research ideas? submitted by /u/ReinforcedKnowledge [link] [comments]

  • [P] Advice regarding MoE and Mamba implementations
    by /u/PaleAle34 (Machine Learning) on February 23, 2024 at 10:13 pm

    Hi everyone, I'm diving into my Master's Thesis and need some guidance. The core of my work is to linearize a complex function that's riddled with memory effects. While the Transformer architecture has been explored in literature, I'm considering taking a fresh angle with either a Mamba architecture or spicing up the Transformer with a MoE (Mixture of Experts) approach. Moe-Mamba is also on the table. The thing is: it's the first time I'm actually working with these architectures, so I don't really know where to start in order to implement them in real code. Where should I learn about these architectures more? Can you also suggest some code implementations (I don't think there are libraries yet) for these architectures? PS: I know I still have to study a lot about these topics so don't judge my stupid questions pls, that is why I'm asking for advice, I want to learn! 🙂 submitted by /u/PaleAle34 [link] [comments]

  • [D] ai explainability tools
    by /u/_meatMuffin (Machine Learning) on February 23, 2024 at 8:15 pm

    I'm working on training a computer vision model for detecting custom objects. I've been looking for tools to help understand AI models, and haven't come across much. Google has a paid toolset: And this one is free: What other tools are people using? submitted by /u/_meatMuffin [link] [comments]

  • [D] Modern Dimensionality Reduction
    by /u/MuscleML (Machine Learning) on February 23, 2024 at 8:08 pm

    Hey All, I’m familiar with the more classical techniques of dimensionality reduction like SVD, PCA, and factor analysis. But are there any modern techniques or maybe some tricks that people have learned over the years that they would like to share. For context, this would be for tabular data. Thanks! submitted by /u/MuscleML [link] [comments]

  • [P] Gemma 7B with Tensor RT (>500k tok/s batch-8) tutorial
    by /u/paulcjh (Machine Learning) on February 23, 2024 at 6:46 pm

    Hey all - we just put out a guide for running Gemma 7B with Tensor RT. You can get some much better performance out of it with Tensor RT. ​ Check it out: ​ Hope it's useful! submitted by /u/paulcjh [link] [comments]

  • [D] [P] intent-pilot: A Desktop Operating Agent
    by /u/Outlandish_MurMan (Machine Learning) on February 23, 2024 at 6:31 pm

    library: pip install intent-pilot Repo link: Hey! We built a Desktop agent which can perform end-end automation. The core idea is based Set-of-Mark (SoM) + GPT-4v for localization. This library is along the same lines of self-operating-computer or open-interpreter but we felt our object detection is better for the UI domain. Also, we improved the UI experience by providing notifications across platforms and also fixed the keyboard layout issue - For example, Pyautogui messes up special characters in German keyboard. Let me know what you guys think. I built it in a week and my colleague helped at the end. So, your feedback will be appreciated. Our object detection model is behind an API but we have released a global key (available in the repo). Have a nice weekend! Note: This is like giving access to a baby. It surprises you mostly but can also shock you. I suggest you close important tabs before it clicks on the wrong thing 😉 submitted by /u/Outlandish_MurMan [link] [comments]

  • [D] System Design Interview - Design Chatbot or Search Engine like Perplexity.
    by /u/Grouchy-Ad6094 (Machine Learning) on February 23, 2024 at 6:21 pm

    Hi Folks, I have a system design interview coming up for ML role at a FAANG. I have started playing with Gen AI only recently and read up on foundational concepts - LLM, model selection, RAGs, fine tuning, etc. But want to get a solid understanding of overall system specifically for gen AI powered apps. Curious if anyone can point to resources o r explain end-to-end system design for a typical Chatbot or a search engine (like Perplexity). submitted by /u/Grouchy-Ad6094 [link] [comments]

  • [D] Approximating known distributions (up to a normalization factor) by a decoder-only part of a VAE.
    by /u/Invariant_apple (Machine Learning) on February 23, 2024 at 5:28 pm

    Hi all, Please feel free to delete if this is a beginner question. I'm reading a bunch of papers on how to learn and sample distributions using neural networks and have some questions. Everything described below is a summary of a couple of papers I read where people tried to do this thing, but I'd like to keep the post self-contained. ------------------------------------------------------------------------------------------------------------------------------ Introduction: I have the following question. Imagine you have a distribution P(x)=F(x)/N, where we know F(x) and can evaluate it at will, but we don't know the normalization factor N. The question is -- how can we learn generate samples for the distribution P(x), with x being elements of some high dimensional space? One option would be to do Markov chain Monte-Carlo, but I am interested in another direction. You will immediately recognize similarities to variational inference and VAE's but please bear with me. Setup: What we could do is propose a decoder network but without an encoder with which we will try to optimize a model distribution M_v(x). We start to sample a z from M(z) where M(z) is known and is for example a simple Gaussian. Next, z is an input to a neural network NN(z)=v that produces the parameters of the model distribution. So important to note here that the decoder network does not produce the actual elements x but produces weights for a model distribution. For example, if M_v(x) is a Gaussian mixture in the components of x and the parameters v are then the necessary means, variances and mixture weights. The goal: Learn appropriate weights in the network such that the graphical model: " M_v(x) =sample M(z) -> get params v -> sample x from M_v(x) " approximates sampling the distribution P(x) that we wanted to learn. Method: We start by writing the KL divergence between the two distributions as KL(M_v(x)| F(x)/N)= E_{M_v} [ log(M_v(x)) - log ( F(x) ) ] + log(N). To optimize our decoder network we essentially put a variational inequality on log(N) as follows: log(N) < E_{M_v} [ log(M_v(x)) - log ( F(x) ) ] (Expression 1) The only tunable parameters in our setup are the weights of the neural network that produces NN(z)=v , and so the goal is to tune the weights in such a way that the RHS is minimized. Questions: 1) This looks very similar to variational inference, but the main difference is that now we actually know the target distribution F(x) (up to normalization) and try to learn variational approximations to it. Whereas in most tutorials and explanations on variational inference you don't know the distribution F(x) but have some data {x} that is distributed according to it, and hence you also need an encoder network. The first question is therefore: does this "decoder-only" VAE to approximate known target distributions have a name? 2) So I understand the setup and the theory, but I'm not sure how to actually evaluate the RHS of Expression 1. Let's say that M_v(x) is a Gaussian mixture. In that case it's impossible to compute at least one of the two terms analytically. So how do you actually do your backprop in PyTorch in this case? Do you actually have to sample the distribution M_v(x) for real, generate some samples {x} and then use the generated samples to approximate E_{M_v} [ log(M_v(x)) - log ( F(x) ) ] ? submitted by /u/Invariant_apple [link] [comments]

  • [R] Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping - Meta 2024 - Searchformer - Significantly outperforms baselines that predict the optimal plan directly with a 5-10× smaller model size and a 10× smaller training dataset!
    by /u/Singularian2501 (Machine Learning) on February 23, 2024 at 5:17 pm

    Paper: Abstract: While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard A∗ search. Searchformer is an encoder-decoder Transformer model trained to predict the search dynamics of A∗. This model is then fine-tuned via expert iterations to perform fewer search steps than A∗ search while still generating an optimal plan. In our training method, A∗'s search dynamics are expressed as a token sequence outlining when task states are added and removed into the search tree during symbolic planning. In our ablation studies on maze navigation, we find that Searchformer significantly outperforms baselines that predict the optimal plan directly with a 5-10× smaller model size and a 10× smaller training dataset. We also demonstrate how Searchformer scales to larger and more complex decision making tasks like Sokoban with improved percentage of solved tasks and shortened search dynamics. submitted by /u/Singularian2501 [link] [comments]

  • [D] My thoughts on model merging
    by /u/bhavya6187 (Machine Learning) on February 23, 2024 at 4:45 pm

    Hey folks, I've been fascinated by model merging and have been playing with it recently. I wanted to understand how it works and these are my findings. My understanding of how model merging works: Task Vectors - The core idea in model merging is derived from the concept of task vectors. The main idea here is that once you have finetuned a model on a specific task, if you subtract the weights from the base model, it gives you a "vector" which captures the modifications needed for the task. Why Model Merging Works - The intuition here is that if you have different models that are good at different things, you can combine different task vectors (such as taking an average in different ways) to produce a new model that is good at both tasks. For example, if model A is good at math, and model B is good at programming, you can merge the models to product a model that is good at both. Many Approaches to Merge Models - All model merging approaches work by combining the task vectors in different ways. Some approaches include Linear Interpolation (LERP), Spherical Linear Interpolation (SLERP), TIES, and DARE. More Art then Science - Like everything in LLM world, these approaches are a bit like black box. While they have some intuitive reasoning, it seems like this is also more of an art then exact science. I am curious what you guys think of this? How have your experiences been working with merged models? Is merging mostly trial and error, and are there some recommended best practices? I have put together a step by step guide along with relevant code and tools I used to do the merge in a blog post here. submitted by /u/bhavya6187 [link] [comments]

  • [R] OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement - 2024 - HumanEval of 92.7! GPT-4 CodeInterpreter has only 88.0!
    by /u/Singularian2501 (Machine Learning) on February 23, 2024 at 4:45 pm

    Paper: Github: Abstract: The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter. To address this, we introduce OpenCodeInterpreter, a family of open-source code systems designed for generating, executing, and iteratively refining code. Supported by Code-Feedback, a dataset featuring 68K multi-turn interactions, OpenCodeInterpreter integrates execution and human feedback for dynamic code refinement. Our comprehensive evaluation of OpenCodeInterpreter across key benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus reveals its exceptional performance. Notably, OpenCodeInterpreter-33B achieves an accuracy of 83.2 (76.4) on the average (and plus versions) of HumanEval and MBPP, closely rivaling GPT-4's 84.2 (76.2) and further elevates to 91.6 (84.6) with synthesized human feedback from GPT-4. OpenCodeInterpreter brings the gap between open-source code generation models and proprietary systems like GPT-4 Code Interpreter. submitted by /u/Singularian2501 [link] [comments]

  • [D] ICLR Plot Twists
    by /u/hzmehrdad (Machine Learning) on February 23, 2024 at 4:37 pm

    Saw a few ICLR results that seem like a surprise to the community: Mamba ➡️ Reject V-JEPA ➡️ Reject MetaGPT ➡️ Accept (Oral) as discussed here What other accepts/rejects have raised a few eyebrows? submitted by /u/hzmehrdad [link] [comments]

  • [R] LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens - Microsoft 2024
    by /u/Singularian2501 (Machine Learning) on February 23, 2024 at 4:23 pm

    Paper: Abstract: Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by new token positions, current extended context windows are limited to around 128k tokens. This paper introduces LongRoPE that, for the first time, extends the context window of pre-trained LLMs to an impressive 2048k tokens, with up to only 1k fine-tuning steps at within 256k training lengths, while maintaining performance at the original short context window. This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance. Extensive experiments on LLaMA2 and Mistral across various tasks demonstrate the effectiveness of our method. Models extended via LongRoPE retain the original architecture with minor modifications to the positional embedding, and can reuse most pre-existing optimizations. submitted by /u/Singularian2501 [link] [comments]

  • [D] Mamba: The Easy Way
    by /u/jackcook (Machine Learning) on February 23, 2024 at 4:09 pm

    Mamba looks like an exciting new language model architecture, and it took me awhile to understand the paper in full! The model employs a lot of tough concepts (S4, GPU memory, parallel scans, etc.), so I've written a blogpost about my understanding of Mamba's big ideas and contributions, with an eye toward making it as beginner-friendly as possible. Link: I hope this is helpful, and I'd love to discuss any additional questions or points of clarification. Let me know what you think! submitted by /u/jackcook [link] [comments]

  • [R] LLMs seem to be by default value maximizers and have a value bias in their responses
    by /u/Cool_Abbreviations_9 (Machine Learning) on February 23, 2024 at 3:09 pm

    Title : Exploring Value Biases: How LLMs Deviate Towards the Ideal Abstract: Large-Language-Models (LLMs) are deployed in a wide range of applications, and their response has an increasing social impact. Understanding the non-deliberate(ive) mechanism of LLMs in giving responses is essential in explaining their performance and discerning their biases in real-world applications. This is analogous to human studies, where such inadvertent responses are referred to as sampling. We study this sampling of LLMs in light of value bias and show that the sampling of LLMs tends to favour high-value options. Value bias corresponds to this shift of response from the most likely towards an ideal value represented in the LLM. In fact, this effect can be reproduced even with new entities learnt via in-context prompting. We show that this bias manifests in unexpected places and has implications on relevant application scenarios, like choosing exemplars. The results show that value bias is strong in LLMs across different categories, similar to the results found in human studies. submitted by /u/Cool_Abbreviations_9 [link] [comments]

  • [R] "Generative Models: What do they know? Do they know things? Let's find out!". Quote from paper: "Our findings reveal that all types of the generative models we study contain rich information about scene intrinsics [normals, depth, albedo, and shading] that can be easily extracted using LoRA."
    by /u/Wiskkey (Machine Learning) on February 23, 2024 at 2:51 pm

    Paper. Project website. I am not affiliated with the authors. Abstract: Generative models have been shown to be capable of synthesizing highly detailed and realistic images. It is natural to suspect that they implicitly learn to model some image intrinsics such as surface normals, depth, or shadows. In this paper, we present compelling evidence that generative models indeed internally produce high-quality scene intrinsic maps. We introduce Intrinsic LoRA (I LoRA), a universal, plug-and-play approach that transforms any generative model into a scene intrinsic predictor, capable of extracting intrinsic scene maps directly from the original generator network without needing additional decoders or fully fine-tuning the original network. Our method employs a Low-Rank Adaptation (LoRA) of key feature maps, with newly learned parameters that make up less than 0.6% of the total parameters in the generative model. Optimized with a small set of labeled images, our model-agnostic approach adapts to various generative architectures, including Diffusion models, GANs, and Autoregressive models. We show that the scene intrinsic maps produced by our method compare well with, and in some cases surpass those generated by leading supervised techniques. A figure from the paper: Quotes from the paper: In this paper, our goal is to understand the underlying knowledge present in all types of generative models. We employ Low-Rank Adaptation (LoRA) as a unified approach to extract scene intrinsic maps — namely, normals, depth, albedo, and shading — from different types of generative models. Our method, which we have named as INTRINSIC LORA (I-LORA), is general and applicable to diffusion-based models, StyleGAN-based models, and autoregressive generative models. Importantly, the additional weight parameters introduced by LoRA constitute less than 0.6% of the total weights of the pretrained generative model, serving as a form of feature modulation that enables easier extraction of latent scene intrinsics. By altering these minimal parameters and using as few as 250 labeled images, we successfully extract these scene intrinsics. Why is this an important question? Our motivation is three-fold. First, it is scientifically interesting to understand whether the increasingly realistic generations of large-scale text-to-image models are correlated with a better understanding of the physical world, emerging purely from applying a generative objective on a large scale. Second, rooted in the saying "vision is inverse graphics" – if these models capture scene intrinsics when generating images, we may want to leverage them for (real) image understanding. Finally, analysis of what current models do or do not capture may lead to further improvements in their quality. ​ For surface normals, the images highlight the models’ ability to infer surface orientations and contours. The depth maps display the perceived distances within the images, with warmer colors indicating closer objects and cooler colors representing further ones. Albedo maps isolate the intrinsic colors of the subjects, removing the influence of lighting and shadow. Finally, the shading maps capture the interplay of light and surface, showing how light affects the appearance of different facial features. ​ We find consistent, compelling evidence that generative models implicitly learn physical scene intrinsics, allowing tiny LoRA adaptors to extract this information with minimal fine-tuning on labeled data. More powerful generative models produce more accurate scene intrinsics, strengthening our hypothesis that learning this information is a natural byproduct of learning to generate images well. Finally, across various generative models and the self-supervised DINOv2, scene intrinsics exist in their encodings resonating with fundamental "scene characteristics" as defined by Barrow and Tenenbaum. Twitter thread about paper from one of the authors. From paper StyleGAN knows Normal, Depth, Albedo, and More (newer version PDF) (Twitter thread about paper): Barrow and Tenenbaum, in an immensely influential paper of 1978, defined the term "intrinsic image" as "characteristics – such as range, orientation, reflectance and incident illumination – of the surface element visible at each point of the image". Maps of such properties as (at least) depth, normal, albedo, and shading form different types of intrinsic images. The importance of the idea is recognized in computer vision – where one attempts to recover intrinsics from images – and in computer graphics – where these and other properties are used to generate images using models rooted in physics. The 1978 paper mentioned in the previous paragraph: Recovering intrinsic scene characteristics: Abstract We suggest that an appropriate role of early visual processing is to describe a scene in terms of intrinsic (veridical) characteristics – such as range, orientation, reflectance, and incident illumination – of the surface element visible at each point in the image. Support for this idea comes from three sources: the obvious utility of intrinsic characteristics for higher-level scene analysis; the apparent ability of humans, to determine these characteristics, regardless of viewing conditions or familiarity with the scene, and a theoretical argument, that such a description is obtainable, by a non-cognitive and non-purposive process, at least, for simple scene domains. The central problem in recovering intrinsic scene characteristics is that the information is confounded in the original light-intensity image: a single intensity value encodes all of the characteristics of the corresponding scene point. Recovery depends on exploiting constraints, derived from assumptions about the nature of the scene and the physics of the imaging process. Language model GPT-4 Turbo explained normals, depth, albedo, and shading as follows: Normals: Imagine you have a smooth rubber ball with little arrows sticking out of it, pointing directly away from the surface. Each one of these little arrows is called a “normal.” In the world of 3D graphics and images, normals are used to describe how surfaces are oriented in relation to a light source. Knowing which way these arrows (normals) point tells the computer how light should hit objects and how it will make them look—whether shiny, flat, bumpy, etc. Depth: When you look at a scene, things that are close to you seem larger and more detailed, and things far away seem smaller and less clear. Depth is all about how far away objects are from the viewpoint (like from a camera or your eyes). When computers understand depth, they can create a 3D effect, make things look more realistic, and know which objects are in front of or behind others. Albedo: Have you ever painted a room in your house? Before the colorful paint goes on, there’s a base coat, usually white or gray. This base coat is sort of what albedo is about. It’s the basic, true color of a surface without any tricks of light or shadow messing with it. When looking at an apple, you know it’s red, right? That red color, regardless of whether you’re looking at it in bright sunshine or under a dim light, is the apple’s albedo. Shading: Think about drawing a picture of a ball and then coloring it in to make it look real. You would darken one side to show that it’s farther from the light, and lighten the other side where the light shines on it. This play with light and dark, with different tones, is what gives the ball a rounded, 3-dimensional look on the paper. Shading in images helps show how light and shadows fall on the surfaces of objects, giving them depth and shape so they don’t look flat. So, in the paper, the challenge they were addressing was how to get a computer to figure out these aspects—normals, depth, albedo, and shading—from a 2D image, which would help it understand a scene in 3D, much like the way we see the world with our own eyes. submitted by /u/Wiskkey [link] [comments]

  • [D] Lessons (tips) for writing a compelling conference paper
    by /u/PaganPasta (Machine Learning) on February 23, 2024 at 11:10 am

    Hi, With ECCV deadline around the corner, I wanted to get some insights from more experienced members here about the lessons, tips and tricks they follow/learnt over the course of their career while writing a conference paper. These can be anything of sorts like "pretty pictures", tone of the text, phrasing of objectives, certain rules they follow for the write-up, or high level questions they think the reviewers ask while reading their work etc. Hopefully, this exchange will also benefit others. submitted by /u/PaganPasta [link] [comments]

  • [D] Are IPUs still a thing?
    by /u/handwerner142 (Machine Learning) on February 23, 2024 at 9:33 am

    I was initially thrilled by IPUs as they seemed to be a serious alternative to GPUs (and TPUs). But Graphcore, the company making the IPUs, seems to be in a very bad situation now. And I don't see so many improvements in terms of software compatibility on IPUs. For example, the HF Optimum Graphcore lib has not been updated for 3 months: ... submitted by /u/handwerner142 [link] [comments]

  • [D] Why is everybody surprised that Mamba got rejected from ICLR? Am I missing something?
    by /u/Seankala (Machine Learning) on February 23, 2024 at 5:40 am

    I'm not just trying to be contrarian either. I keep hearing this on Reddit, at work, on different online forums, etc. I also was surprised when I first heard the news but after reading the paper I wasn't particularly surprised. Their hardware tweaks were interesting but other than that it seems like it was a simple adaptation of a previous paper. The benchmark experiments were not as extensive as I initially believed due to everybody talking about how revolutionary it is. Reading the paper just left me with a ton of questions along the lines of "What about performance on X task or Y benchmark?" I'm not trying to shame the authors, but it didn't really feel like a "conventional" paper in the machine learning field either. There have been plenty of great papers released that weren't exactly fit for a conference publication, and I don't think that just because something is being talked about a lot on Twitter or LinkedIn it means it deserves to be published at a venue. I'm genuinely wondering if I'm underestimating it because I didn't understand it properly and am open to any opinions. submitted by /u/Seankala [link] [comments]

  • [D] MetaGPT grossly misreported baseline numbers and got an ICLR Oral!
    by /u/Signal-Aardvark-4179 (Machine Learning) on February 22, 2024 at 5:06 pm

    OpenReview: I was looking at ICLR reviews and was surprised to see MetaGPT being submitted to ICLR. The acceptance decision states that they were awarded an Oral (highest level at ICLR). Looking at the paper, they report these comparisons with HumanEval: ​ Method Pass@1 MetaGPT 85.9 GPT-4 67.0 GPT-3.5-Turbo (in the response) 48.1 However the real GPT-4 and GPT-3.5-Turbo numbers on this benchmark are much much higher (see EvalPlus leaderboard: The results from the EvalPlus leaderboard have been reproduced numerous times, so there is no doubt about those. The numbers the MetaGPT authors used were pulled from the old technical report, and are not accurate anymore. They must know this, everyone does, there is no doubt about it. Here are the real comparisons using the numbers from EvalPlus: Method Pass@1 MetaGPT 85.9 GPT-4 88.4 GPT-3.5-Turbo 76.8 The GPT-3.5-Turbo performance is GROSSLY missreported. Never seen anything like this before. There is no way they legitimately got that number with GPT-3.5-Turbo. So, basically, their whole "agent company simulation" deal that makes you spend $10 in OpenAI credits is worse than just asking the LLM once... And they got an oral... We are screwed. submitted by /u/Signal-Aardvark-4179 [link] [comments]

  • Streamline diarization using AI as an assistive technology: ZOO Digital’s story
    by Ying Hou (AWS Machine Learning Blog) on February 20, 2024 at 6:18 pm

    ZOO Digital provides end-to-end localization and media services to adapt original TV and movie content to different languages, regions, and cultures. It makes globalization easier for the world’s best content creators. Trusted by the biggest names in entertainment, ZOO Digital delivers high-quality localization and media services at scale, including dubbing, subtitling, scripting, and compliance. Typical

  • Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints
    by Ram Vegiraju (AWS Machine Learning Blog) on February 19, 2024 at 6:13 pm

    Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single endpoint. Previously, MMEs pre-determinedly allocated CPU computing power to models statically regardless the model traffic load, using Multi Model Server (MMS) as its model server. In this post, we discuss a

  • Use Amazon Titan models for image generation, editing, and searching
    by Rohit Mittal (AWS Machine Learning Blog) on February 19, 2024 at 5:53 pm

    Amazon Bedrock provides a broad range of high-performing foundation models from Amazon and other leading AI companies, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, searching, chat, reasoning and acting agents, and more. The new Amazon Titan Image Generator model allows content

  • Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock
    by Manish Chugh (AWS Machine Learning Blog) on February 19, 2024 at 4:43 pm

    Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Their popularity stems from the ability to respond to customer inquiries in real time and handle multiple queries simultaneously in different languages. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly

  • Code Llama 70B is now available in Amazon SageMaker JumpStart
    by Kyle Ulrich (AWS Machine Learning Blog) on February 16, 2024 at 4:32 pm

    Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. Code Llama is a state-of-the-art large language model (LLM) capable of generating code and natural language about code from both code and natural language prompts.

  • Detect anomalies in manufacturing data using Amazon SageMaker Canvas
    by Helge Aufderheide (AWS Machine Learning Blog) on February 15, 2024 at 3:54 pm

    With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. Industrial companies increasingly look at data analytics and data-driven decision-making to increase resource efficiency across their entire portfolio, from operations to performing

  • Enhance Amazon Connect and Lex with generative AI capabilities
    by Hamza Nadeem (AWS Machine Learning Blog) on February 14, 2024 at 5:43 pm

    Effective self-service options are becoming increasingly critical for contact centers, but implementing them well presents unique challenges. Amazon Lex provides your Amazon Connect contact center with chatbot functionalities such as automatic speech recognition (ASR) and natural language understanding (NLU) capabilities through voice and text channels. The bot takes natural language speech or text input, recognizes

  • Skeleton-based pose annotation labeling using Amazon SageMaker Ground Truth
    by Arthur Putnam (AWS Machine Learning Blog) on February 14, 2024 at 5:29 pm

    Pose estimation is a computer vision technique that detects a set of points on objects (such as people or vehicles) within images or videos. Pose estimation has real-world applications in sports, robotics, security, augmented reality, media and entertainment, medical applications, and more. Pose estimation models are trained on images or videos that are annotated with

  • Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock
    by Ravikiran Rao (AWS Machine Learning Blog) on February 14, 2024 at 4:56 pm

    With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like responses. This is fueling innovation across

  • How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker
    by Santosh Waddi (AWS Machine Learning Blog) on February 13, 2024 at 5:44 pm

    This post is co-written with Santosh Waddi and Nanda Kishore Thatikonda from BigBasket. BigBasket is India’s largest online food and grocery store. They operate in multiple ecommerce channels such as quick commerce, slotted delivery, and daily subscriptions. You can also buy from their physical stores and vending machines. They offer a large assortment of over

  • Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access
    by Ioan Catana (AWS Machine Learning Blog) on February 13, 2024 at 5:36 pm

    Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used

  • How modernized its ML experimentation framework with Amazon SageMaker
    by Laurens van der Maas (AWS Machine Learning Blog) on February 12, 2024 at 6:54 pm

    This post is co-written with Kostia Kofman and Jenny Tokar from As a global leader in the online travel industry, is always seeking innovative ways to enhance its services and provide customers with tailored and seamless experiences. The Ranking team at plays a pivotal role in ensuring that the search and recommendation

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on February 11, 2024 at 4:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

  • Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock
    by Hasan Poonawala (AWS Machine Learning Blog) on February 9, 2024 at 8:47 pm

    In this post, we show you how to build an internal SaaS layer to access foundation models with Amazon Bedrock in a multi-tenant (team) architecture. We specifically focus on usage and cost tracking per tenant and also controls such as usage throttling per tenant. We describe how the solution and Amazon Bedrock consumption plans map to the general SaaS journey framework. The code for the solution and an AWS Cloud Development Kit (AWS CDK) template is available in the GitHub repository.

  • Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock
    by Kyle Blocksom (AWS Machine Learning Blog) on February 8, 2024 at 5:37 pm

    Generative AI agents are a versatile and powerful tool for large enterprises. They can enhance operational efficiency, customer service, and decision-making while reducing costs and enabling innovation. These agents excel at automating a wide range of routine and repetitive tasks, such as data entry, customer support inquiries, and content generation. Moreover, they can orchestrate complex,

  • Automate mortgage document fraud detection using an ML model and business-defined rules with Amazon Fraud Detector: Part 3
    by Anup Ravindranath (AWS Machine Learning Blog) on February 7, 2024 at 10:03 pm

    In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. In the second post, we discussed an approach to develop a deep learning-based computer vision model

  • Accenture creates a regulatory document authoring solution using AWS generative AI services
    by Ilan Geller (AWS Machine Learning Blog) on February 6, 2024 at 10:34 pm

    This post is co-written with Ilan Geller, Shuyu Yang and Richa Gupta from Accenture. Bringing innovative new pharmaceuticals drugs to market is a long and stringent process. Companies face complex regulations and extensive approval requirements from governing bodies like the US Food and Drug Administration (FDA). A key part of the submission process is authoring

  • Integrate QnABot on AWS with ServiceNow
    by Sujatha Dantuluri (AWS Machine Learning Blog) on February 6, 2024 at 6:03 pm

    Do your employees wait for hours on the telephone to open an IT ticket? Do they wait for an agent to triage an issue, which sometimes only requires restarting the computer? Providing excellent IT support is crucial for any organization, but legacy systems have relied heavily on human agents being available to intake reports and

  • Deploy large language models for a healthtech use case on Amazon SageMaker
    by Zack Peterson (AWS Machine Learning Blog) on February 6, 2024 at 5:56 pm

    In this post, we show how to develop an ML-driven solution using Amazon SageMaker for detecting adverse events using the publicly available Adverse Drug Reaction Dataset on Hugging Face. In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried.

  • Announcing support for Llama 2 and Mistral models and streaming responses in Amazon SageMaker Canvas
    by Davide Gallitelli (AWS Machine Learning Blog) on February 5, 2024 at 7:46 pm

    Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service for building and deploying machine learning (ML) models without the need to write any code. Ready-to-use Foundation Models (FMs) available in SageMaker Canvas enable customers to use generative AI for tasks such as content generation and summarization. We are thrilled to announce the latest

  • How is limiting risks of disease spillover from animals to humans using Amazon SageMaker geospatial capabilities
    by Ajay K Gupta (AWS Machine Learning Blog) on February 5, 2024 at 7:33 pm

    This is a guest post co-authored by Ajay K Gupta, Jean Felipe Teotonio and Paul A Churchyard from is a geospatial health risk analytics firm whose vision is that global health challenges are solvable through human ingenuity and the focused and accurate application of data analytics. In this post, we present one approach

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