Azure AI Fundamentals AI-900 Exam Preparation

Azure AI Fundamentals AI-900 Exam Prep PRO

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Azure AI 900 is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

This Azure AI Fundamentals AI-900 Exam Preparation App provides Basics and Advanced Machine Learning Quizzes and Practice Exams on Azure, Azure Machine Learning Job Interviews Questions and Answers, Machine Learning Cheat Sheets.

Download Azure AI 900 on iOs

Download Azure AI 900 on Windows10/11

Azure AI Fundamentals Exam Prep

Azure AI Fundamentals AI-900 Exam Preparation App Features:

– Azure AI-900 Questions and Detailed Answers and References

– Machine Learning Basics Questions and Answers

– Machine Learning Advanced Questions and Answers

– NLP and Computer Vision Questions and Answers

– Scorecard

– Countdown timer

– Machine Learning Cheat Sheets

– Machine Learning Interview Questions and Answers

– Machine Learning Latest News

Azure AI 900 – Machine Learning

This Azure AI Fundamentals AI-900 Exam Prep App covers:

  • ML implementation and Operations,
  • Describe Artificial Intelligence workloads and considerations,
  • Describe fundamental principles of machine learning on Azure,
  • Describe features of computer vision workloads on Azure,
  • Describe features of Natural Language Processing (NLP) workloads on Azure ,
  • Describe features of conversational AI workloads on Azure,
  • QnA Maker service, Language Understanding service (LUIS), Speech service, Translator Text service, Form Recognizer service, Face service, Custom Vision service, Computer Vision service, facial detection, facial recognition, and facial analysis solutions, optical character recognition solutions, object detection solutions, image classification solutions, azure Machine Learning designer, automated ML UI, conversational AI workloads, anomaly detection workloads, forecasting workloads identify features of anomaly detection work, Kafka, SQl, NoSQL, 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.
  • This App can help you:
  • – Identify features of common AI workloads
  • – identify prediction/forecasting workloads
  • – identify features of anomaly detection workloads
  • – identify computer vision workloads
  • – identify natural language processing or knowledge mining workloads
  • – identify conversational AI workloads
  • – Identify guiding principles for responsible AI
  • – describe considerations for fairness in an AI solution
  • – describe considerations for reliability and safety in an AI solution
  • – describe considerations for privacy and security in an AI solution
  • – describe considerations for inclusiveness in an AI solution
  • – describe considerations for transparency in an AI solution
  • – describe considerations for accountability in an AI solution
  • – Identify common types of computer vision solution:
  • – Identify Azure tools and services for computer vision tasks
  • – identify features and uses for key phrase extraction
  • – identify features and uses for entity recognition
  • – identify features and uses for sentiment analysis
  • – identify features and uses for language modeling
  • – identify features and uses for speech recognition and synthesis
  • – identify features and uses for translation
  • – identify capabilities of the Text Analytics service
  • – identify capabilities of the Language Understanding service (LUIS)
  • – etc.

Download Azure AI 900 on iOs

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Azure AI Fundamentals Breaking News – Azure AI Fundamentals Certifications Testimonials

  • Introducing the Azure Developer CLI (azd) | Azure Friday
    by /u/robcaron (Microsoft Azure) on October 3, 2022 at 12:35 am

    submitted by /u/robcaron [link] [comments]

  • Azure Policy to Audit Application Gateway SSL Policy?
    by /u/Daventhor (Microsoft Azure) on October 2, 2022 at 11:47 pm

    There are several pre-built policies for application gateways but mostly dealing with firewall configuration. Most of the examples for minimum TLS version deal with storage accounts and apps. I'm just trying to have a policy in place that checks all the application gateways and ensures they're set to a specific prebuilt sslpolicy (20170401S). Has anyone done something similar to this? submitted by /u/Daventhor [link] [comments]

  • Resource Tagging
    by /u/J_hova1974 (Microsoft Azure) on October 2, 2022 at 11:32 pm

    Is there a way to tag existing resources with a new tag? I have an RG and resources in it, and want to tag them as non-prod. Is there a way to tag them without recreating them, retroactively tagging items? Creating an incremental ARM template to update the resources tag or is there something in Git that can assist with this? submitted by /u/J_hova1974 [link] [comments]

  • Create work item on check-in. Azure DevOps TFVC.
    by /u/TieMountain1117 (Microsoft Azure) on October 2, 2022 at 7:49 pm

    I have a situation where we have some colleagues who make small bug fixes in various release branches that they work out of. What I would like to do is auto-create a work item and assign it to the dev team to let us know that it needs to be reviewed and merged. Right now we’re left with manually checking changeset history, which isn’t really tenable. I know switching to git and using pull requests would resolve this issue, but migrating to git is currently out of the question (I’m working on it!). My initial thought was to use a build pipeline. I have a powershell script that can generate the work item based on info from the pipeline execution. However, these changes are always SQL-based and don’t actually require a new build so using a build pipeline seems like overkill. I feel like I’m missing something very obvious, whether it’s about pipelines or considering another option. Does anyone here have any suggestions? submitted by /u/TieMountain1117 [link] [comments]

  • Expired trial subscription and multiple tenants
    by /u/logician3000 (Microsoft Azure) on October 2, 2022 at 7:48 pm

    Help me please with this. I have an expired trial subscription, which is linked with Default Directory. Also I have another tenant with my main Azure AD. Microsoft is sending emails that they will delete "everything", because my subscription expired. I can't upgrade to pay-as-you-go, even with multiple correct credit card attached it says I'm ineligible. Will this promised deletion be limited to Default Directory tenant, or to my main AD as well (which has no subscriptions whatsoever)? submitted by /u/logician3000 [link] [comments]

  • Autogenerating SSL certs with custom domain for container app
    by /u/HermanCainsGhost (Microsoft Azure) on October 2, 2022 at 6:09 pm

    I am relatively new to Azure and I am trying to move a client's site over there. I want to have an autoupdating cert for a container app. I see where it allows me to upload a certificate for a custom domain, but nowhere do I see a way to have Azure manage this process/constantly update the certs. Is there anyway to do this in Azure? I know AWS has their certificate manager, and I've used Digital Ocean and they use Let's Encrypt to do it. How would I setup a domain pointing to an app container with an SSL cert that always updates on Azure? submitted by /u/HermanCainsGhost [link] [comments]

  • logs for password writeback
    by /u/bizcbtr (Microsoft Azure) on October 2, 2022 at 4:13 pm

    Is there any logs to check who made changes to password writeback. I just saw its disable when i check it. submitted by /u/bizcbtr [link] [comments]

    by /u/IllustratorAccording (Microsoft Azure) on October 2, 2022 at 1:16 pm

    dears, thank you for supporting me throw my journey to azure. I have question can i add sensitivity label on PDF file ? (the file is already pdf) without convert it. submitted by /u/IllustratorAccording [link] [comments]

  • Which Azure Certifications for beginners?
    by /u/Born_Trifle_2017 (Microsoft Azure Certifications) on October 2, 2022 at 9:30 am

    Hi Guys, im a total noob in Azure. However, i did some microsoft courses and earnt a free examination. I was wondering if Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution is worth it. I am willing to study and not sure if this is a good start for beginners submitted by /u/Born_Trifle_2017 [link] [comments]

  • WAF - Reliability - What is self healing?
    by /u/1whatabeautifulday (Microsoft Azure) on October 2, 2022 at 6:05 am

    Hi, I was reading on WAF and reliability. In the documentation it states that workloads should be designed for "self healing". What is meant with this? Thank you, submitted by /u/1whatabeautifulday [link] [comments]

  • Azure Architecture Scalability
    by /u/Any-Midnight-8611 (Microsoft Azure) on October 2, 2022 at 5:51 am

    Hi everyone, I have to scale an architecture whose first half is a couple of sensors and various IoT devices. The second half is the cloud, wherein I get data from the first half, and after that, I perform two actions mainly: Pass the data to Azure IOT and event hub spark and perform real-time data processing. After that, I store that data in Azure Blob storage After that, this data is accessed through the node. Js to show on UI. 2) I Also pass that data to some windows scheduler where it performs specific actions mentioned in the data. I am supposed to scale this architecture in any way possible. Any suggestions or feedback for this are welcomed. Ps. I am new to Azure, and this is an old architecture that my company uses and cannot be changed completely. ​ I appreciate your time and consideration. submitted by /u/Any-Midnight-8611 [link] [comments]

  • Is managed disk backup taken by default even If I chose No infrastructure redundancy required?
    by /u/zxallen_ (Microsoft Azure) on October 2, 2022 at 5:45 am

    In different articles I have seen managed disk is having three redundant copies. Is the three copies mentioned in regard to three Availability zones. If I chose No redundancy and the disk fails(data lost), will I need to manually maintain backup otherwise data is lost? submitted by /u/zxallen_ [link] [comments]

  • Automating Stopping VMs
    by /u/zero_opacity (Microsoft Azure) on October 2, 2022 at 2:37 am

    I’m looking for the best way to stop VMs for my subscription at a designated time each day. I’ve seen some older posts and videos. I’ve explored automation accounts, etc… but there seem to be a lot of layers. I also looked at some of the run books in the marketplace. Anyone have any pointers/suggestions on how to implement something like this? submitted by /u/zero_opacity [link] [comments]

  • Building distributed applications have never been easier with Dapr Pub/Sub APIs and Azure Container Apps...
    by /u/Tjoudeh (Microsoft Azure) on October 2, 2022 at 1:13 am

    If you implemented the Pub/Sub pattern before, you already noticed that there is a lot of plumbing needed on the publisher and subscriber components in order to publish and consume messages, as well each message broker has it is own SDK and implementation, so you need to write your code in an abstracted way to hide the specific implementation details for each message broker SDK and make it easier for publisher and consumers to re-use this. What Dapr offers here is a building block that significantly simplifies implementing pub/sub functionality. To know more you can continue reading on this link submitted by /u/Tjoudeh [link] [comments]

  • Best study material for AZ-800 and AZ-801
    by /u/Key_Lengthiness5555 (Microsoft Azure Certifications) on October 2, 2022 at 12:10 am

    Hi Guys, What’s the best study material for AZ-800 and AZ-801. I know MS Learn is there and it’s a good resource. However I am more interested in video learning methods as that’s how I learn. Also how much time would you recommend to crack both exams in terms of study hours/weeks think of me as a complete beginner in server field. My other certs include AZ-900, MS-900, PL-900, SC-900, AI-900, DP-900, MB-910, MB-920, AZ-104, MD-100 & MD-101. Don’t think there will be over lap with any of these but I am on a timeline to crack AZ-800 and 801 in two months time. Happy to buy a paid course as company will reimburse so that’s fine. Thank you for your time. submitted by /u/Key_Lengthiness5555 [link] [comments]

  • script to find all unused resource in my subscription
    by /u/No-Ankit (Microsoft Azure) on October 1, 2022 at 8:33 pm

    I am looking to find all the unused resources in my subscription so I am wondering if I get all the list of resources which has not be modified or updated since 2 months say.. does anyone of you have any kind of script which can do this job for me?? Please share if you have one...Thanks in Advance submitted by /u/No-Ankit [link] [comments]

  • PubSub web service and PowerApps
    by /u/ThreadedJam (Microsoft Azure) on October 1, 2022 at 7:12 pm

    Hi, I have a PowerApp canvas app and I would like to be able to subscribe to notifications from an Azure PubSub web service and then receive notifications when an event is published. Has anyone done something similar? Cheers. submitted by /u/ThreadedJam [link] [comments]

  • Passed AZ-900 w/ horrible OnVue experience
    by /u/likeeatingpizza (Microsoft Azure Certifications) on October 1, 2022 at 6:38 pm

    First post here but grateful for the many tips I got from this sub in the past month or so while I was preparing for the exam, they def helped Anyway, passed AZ900 with 750 or smth (already forgot the actual score) just today. Found it harder than expected, but I work in Help Desk and have 0 exposure to Azure. Started studying from MS Learn over the summer, then played around with the Azure Portal Sandbox a bit and watched the Savil 3h long video lesson. Happy that I passed but damn what an absolute nightmare of an experience the OneVue at home exam was. It was the 4th time I had to reschedule the exam. First two tries were from 2 different desktop PCs and proctor couldn't never see my video feed (on my side webcam was working fine). Went back and forth between home DSL and hotspot, and they kept putting me at the back of a 60+ people waiting queue. Sat in my room for up ~2h occasionally being told by a clueless indian to "exit and reopen the software" before giving up for the day. Next try was from a W10 old laptop and finally the proctor "released" my exam, only for the OnVue software to start crashing every time before loading the questions (later found bunch of TestTakerSBBrowser.exe errors in the Event Viewer) so that was another night wasted. Plus the same access code would not work on a different computer. For today I had even bought a different webcam from amazon... I use my desktop PC and same ordeal from the proctor about my video being off/black/frozen/ you name it... Then I try again from the (same) old laptop and everything magically just... worked. Had to re-do all check-in photos but the exam launched. Rants aside, I have two questions. Does that preliminary survey about your level of knowledge on the different topics (Could principles, azure resources, Az security, etc...) affect which questions you get on the exam? And does the exam score mean anything in a work environment? Do people put it on the CV, or do companies ask for it and maybe eventually turn you down if too low? submitted by /u/likeeatingpizza [link] [comments]

  • Meraki VPN and Azure MFA
    by /u/ButterflyWide7220 (Microsoft Azure) on October 1, 2022 at 6:25 pm

    Any experiences with Meraki and Azure MFA? I am looking into the NPS extension, but is there also a different way like with an Azure Enterprise app to achieve this? submitted by /u/ButterflyWide7220 [link] [comments]

  • Azure Terraform Customer Meetups at HashiConf 2022 [In-Person] Oct 4th
    by /u/satyavel (Microsoft Azure) on October 1, 2022 at 6:14 pm

    HashiConf 2022 is being held in-person in Los Angeles from October 4th to 6th. If you are attending in-person and would like to meet with the Azure Terraform Engineering team to share your experiences using Terraform on Azure, or would like to hear about some future investments you can sign up at submitted by /u/satyavel [link] [comments]

Download Azure AI 900 on iOs

Download Azure AI 900 on Windows10/11

We know you like your hobbies and especially coding, We do too, but you should find time to build the skills that’ll drive your career into Six Figures. Cloud skills and certifications can be just the thing you need to make the move into cloud or to level up and advance your career. 85% of hiring managers say cloud certifications make a candidate more attractive. Start your cloud journey with these excellent books below:

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

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

AWS MLS-C01 Machine Learning Specialty Exam Prep PRO
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

  • [P] DreamBooth training and inference using huggingface diffusers with stable diffusion + Gradio Web UI in colab
    by /u/Illustrious_Row_9971 (Machine Learning) on October 3, 2022 at 2:17 am

    submitted by /u/Illustrious_Row_9971 [link] [comments]

  • [D] - Has there been any research in the following variations to Attention Layer architecture?
    by /u/029187 (Machine Learning) on October 3, 2022 at 1:55 am

    Hi all, sorry for spamming posts today. For attention layer architecture, I had a few questions about potential variations: The attention layer shares the K, V, and Q weights for each input token. Has it ever been tried to have more than 1 K, V, and Q matrix per attention layer? K, Q, and V seem to usually be linear layers, which effectively makes them a matrix multiplication of the inputs (plus any positional encoding). Why stop at 1 linear layer? Has anyone done research into replacing the K, V, and Q matrices with non-linear functions such as DNNs? The "Attention is all you need" mentions the words are already encoded before they are passed into a transformer. Has there been any research on what the optimal encoding schemes are? How much does pre-encoding even help? Has 1-hot encoding been tried? It looks like in the transformer, the output is a softmax probability of every possible token, so the network output must already be pretty large. How does concatenation based positional encoding work as opposed to the sinusoidal addition used in "Attention is all you need". The sinusoidal strikes me as odd because it is messing with the embedded word vectors. submitted by /u/029187 [link] [comments]

  • [R] DDIM Reconstruction Confusion
    by /u/adham-elarabawy (Machine Learning) on October 3, 2022 at 12:43 am

    I'm trying to leverage the "determinism" that is discussed in the DDIM paper in order to go from a clean image (x_0) to it's appropriate latent representation (latent as in the x_T noise), and then back. AKA, I want to go from the clean image to the pure noise that directly maps back to the same image when running the DDIM diffusion procedure. I attached a snippet from the DDIM paper that describes this behavior well: I've been throwing my head at this for a while and I have a few questions about the theory and also the implementation: Intuitively, where is the initial noise coming from? If we have a clean image, then how exactly do we derive the noise direction to start noising in? I understand that if we have random noise, then the DDIM sampling procedure will have determinism in the output, but I don't quite understand the reverse direction, since I'm not sure where the initial noise direction is rooted in. Implementation-wise, I don't really get where I can get the noise. Does this come from passing the clean image directly into the unet and getting a noise-residual, and then adding that to the clean image and iterating from there? When I tried doing this, I got some nasty results submitted by /u/adham-elarabawy [link] [comments]

  • [D] Most interesting papers from ICLR 2023 submissions?
    by /u/billjames1685 (Machine Learning) on October 2, 2022 at 10:11 pm

    Hey guys, I’m looking for new papers to read when I’m bored, are there any ICLR papers that have got your attention? submitted by /u/billjames1685 [link] [comments]

  • [P] A simple openAI gym dashboard in the browser
    by /u/vaaal88 (Machine Learning) on October 2, 2022 at 9:07 pm

    submitted by /u/vaaal88 [link] [comments]

  • [D] - Why do Attention layers work so well? Don't weights in DNNs already tell the network how much weight/attention to give to a specific input? (High weight = lots of attention, low weight = little attention)
    by /u/029187 (Machine Learning) on October 2, 2022 at 8:56 pm

    So an attention layer has a Q, K, and V vector My understanding is the goal is to say for a given query q, how relevant is the value v. From this the network learns which data is relevant to focus on for a given input. But what I don't get is why this is effective. Don't DNNs already do this with weights? A neuron in a hidden layer can be set off by any arbitrary combination of inputs, so in principle something like attention should be able to naturally emerge inside of a DNN. For example, image recognition neural network may learn to focus on specific patterns of pixels and ignore others. Why does hard coding this mechanism into the model so much benefit? submitted by /u/029187 [link] [comments]

  • [D] TensorRT in C++ vs in Python
    by /u/Commercial_Put577 (Machine Learning) on October 2, 2022 at 8:54 pm

    Will I see any significant decrease in runtime if I run the TensorRT inference in C++ instead of Python for my Yolov5 network? How about for my custom convolutional network? submitted by /u/Commercial_Put577 [link] [comments]

  • [D] Mixing paragraphs of a reading and generate a new meaning
    by /u/jabertolin (Machine Learning) on October 2, 2022 at 8:19 pm

    Would it be possible to take different texts, with similar topic , cut them in several paragraphs and mix them to create new text with meaning? If so, would it be too complex to do it? submitted by /u/jabertolin [link] [comments]

  • [D] - Have there been any cutting edge or practical use cases where neuro-evolution was used?
    by /u/029187 (Machine Learning) on October 2, 2022 at 7:32 pm

    Hi all, the post title basically says it all. Has neuro evolution ever been the best-in-class alg for a type of problem? submitted by /u/029187 [link] [comments]

  • [D] Types of Machine Learning Papers
    by /u/Lost-Parfait568 (Machine Learning) on October 2, 2022 at 7:25 pm

    submitted by /u/Lost-Parfait568 [link] [comments]

  • Tesla AI day 2022 video link and index [Discussion]
    by /u/MLisdabomb (Machine Learning) on October 2, 2022 at 7:05 pm ​ 17:04 Bot reveal 35:16 crash test 40:19 powertrain 45:48 biologically inspired design 49:43 visual navigation 56:25 motion adaptation 56:51 what's next ​ 58:10 autopilot intro 1:04:00 planning 1:11:28 occlusions 11:12:21 occupancy network 1:17:00 nerf discussion 1:19:07 auto labeling 1:20:00 14k gpus 1:23:22 optimized video training 1:25:29 auto pilot vision 1:28:00 model as language components ​ 1:34:36 sparsification 1:35:39 fsd lanes network in car 1:38:20 1B parameters, compiler tool chain 1:40:32 autolabeling 1:47:09 challenge cases 1:47:52 simulation 1:51:38 unreal engine 1:53:52 data engine, improve autopilot thru data ​ ​ 1:56:46 dojo super computer 2:02:43 dojo accelerator 2:05:43 voltage regulator module 2:07:38 vibrating capacitors 2:09:31 cooling solutions 2:11:00 dojo interface processor 2:12:17 dojo host interface 2:12:41 dojo cabinet 2:13:01 exapod 2:13:55 software stack 2:17:46 dojo compiler 2:20:48 dojo vs a100 2:22:42 ingest, dataloader 2:24:20 72 gpu rqcs to 4 dojo cabinets ​ 2:26:32 q&a submitted by /u/MLisdabomb [link] [comments]

  • [P] Talking head animation with StyleGAN!
    by /u/willowill5 (Machine Learning) on October 2, 2022 at 5:52 pm

    submitted by /u/willowill5 [link] [comments]

  • [D] Has anyone done or found a fair price-quality analysis of modern NLPs?
    by /u/alexlash (Machine Learning) on October 2, 2022 at 5:02 pm

    I'm not much involved in ML, but I've been tasked with finding the best price-quality text generation solution (basically, for generating ads and product descriptions). What I need is a custom solution. I've learned a bit about OpenAI, Cohere and Tune the Model APIs. But I couldn't find any decent research about the accuracy of their models and price-quality analysis based on it. Has anyone done, found such research, or is it impossible to do it at all? There's a lot of buzz about content generation, but there are no independent analytics??? If there is no research, can you recommend a tool/tools based on your experience? submitted by /u/alexlash [link] [comments]

  • BlenderBot Developers for hire? [D]
    by /u/philip_rhoades (Machine Learning) on October 2, 2022 at 1:43 pm

    People, ​ I am wondering whether there are any BlenderBot developers for hire? - am I likely to find them here? - if not, where? ​ Thanks, Phil. submitted by /u/philip_rhoades [link] [comments]

  • [R] natural and expressive motion generation for digital humans with text-to-motion: "a person turns to his right and paces back and forth"
    by /u/SpatialComputing (Machine Learning) on October 2, 2022 at 12:54 pm

    submitted by /u/SpatialComputing [link] [comments]

  • [N] New BetaML v0.8: model definition, hyperparameters tuning and fitting in 2 lines
    by /u/alobianco (Machine Learning) on October 2, 2022 at 12:22 pm

    Dear ML community, I'm pleased to announce BetaML v0.8. The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, with a detailed tutorial on its usage from Python or R (no wrapper packages are needed) and an extensive interface to MLJ. Aside from the support of the standard mod = Model([Options]), fit!(mod,X,[Y]), predict(mod,[X]) paradigm for 22 models (see list below) , this version brings the implementation of one of the easiest hyperparameter tuning functionality available on ML libraries. From model definition to tuning, fitting and prediction in just 3 lines of code: julia mod = ModelXX(autotune=true) # --> control autotune with the parameter `tunemethod` fit!(mod,x,[y]) # --> autotune happens here together with final fitting est = predict(mod,xnew) Autotune is hyperthreaded with model-specific defaults. For example for Random Forests the defaults are: julia tunemethod=SuccessiveHalvingSearch( hpranges = Dict("n_trees" => [10, 20, 30, 40], "max_depth" => [5,10,nothing], "min_gain" => [0.0, 0.1, 0.5], "min_records" => [2,3,5], "max_features" => [nothing,5,10,30], "beta" => [0,0.01,0.1]), loss = l2loss_by_cv, # works for both regression and classification res_shares = [0.08, 0.1, 0.13, 0.15, 0.2, 0.3, 0.4] multithreads = false) # RF are already multi-threaded For SuccessiveHalvingSearch, the number of models is reduced at each iteration in order to arrive at a single "best" model. Only supervised model autotuning is currently implemented, but GMM-based clustering autotuning is planned using BIC or AIC. Aside from hyperparameters autotuning, the other release notes are: support for all models of the new "V2" API that implements a "standard" mod = Model([Options]), fit!(mod,X,[Y]), predict(mod,[X]) workflow (details here). Classic API is now deprecated, with some of its functions be removed in the next BetaML 0.9 versions and some unexported. standardised function names to follow the [Julia style guidelines](ttps:// and the new BetaML code style guidelines new functions model_load and model_save to load/save trained models from the filesystem new MinMaxScaler (StandardScaler was already available as classical API functions scale and getScalingFactors) many bugfixes/improvements on corner situations new MLJ interface models to NeuralNetworkEstimator All models are coded in Julia and are part of the same package. Currently, BetaML includes 22 models): BetaML name MLJ Interface Category PerceptronClassifier LinearPerceptron Supervised regressor KernelPerceptronClassifier KernelPerceptron Supervised regressor PegasosClassifier Pegasos Supervised classifier DecisionTreeEstimator DecisionTreeClassifier, DecisionTreeRegressor Supervised regressor and classifier RandomForestEstimator RandomForestClassifier, RandomForestRegressor Supervised regressor and classifier NeuralNetworkEstimator NeuralNetworkRegressor, MultitargetNeuralNetworkRegressor, NeuralNetworkClassifier Supervised regressor and classifier GMMRegressor1 Supervised regressor GMMRegressor2 GaussianMixtureRegressor, MultitargetGaussianMixtureRegressor Supervised regressor KMeansClusterer KMeans Unsupervised hard clusterer KMedoidsClusterer KMedoids Unsupervised hard clusterer GMMClusterer GaussianMixtureClusterer Unsupervised soft clusterer FeatureBasedImputer SimpleImputer Unsupervised missing data imputer GMMImputer GaussianMixtureImputer Unsupervised missing data imputer RFImputer RandomForestImputer Unsupervised missing data imputer UniversalImputer GeneralImputer Unsupervised missing data imputer MinMaxScaler Data transformer StandardScaler Data transformer Scaler Data transformer PCA Data transformer OneHotEncoder Data transformer OrdinalEncoder Data transformer ConfusionMatrix Predictions assessment Predictions are quite good, often better than the leading packages, although the resource usage is still considerable. You have detailed BetaML tutorials on classification, regression and clustering in the documentation. submitted by /u/alobianco [link] [comments]

  • [D] Gpu for machine translation
    by /u/wrsage (Machine Learning) on October 2, 2022 at 10:13 am

    Gpu for machine translation Soo, i want to make machine translation rig for me to make my work easier. I work as translator and use currently using google api to reduce my workload. But my country have very few people so development of google translate is extremely bad. I had to fix some easiest sentenses like "Goodnight" since GT translate it wrong. That's why I decided to make my own translation system and use my own translations as base. So what is bare minimum required gpu for at least 10.000 pages of translations? Currently I'm considering p106-100, rx 580, 1060 6gb. I think these materials are enough, but let me know if it's not. submitted by /u/wrsage [link] [comments]

  • [N] Electric vehicules charging station hierarchical forecasting hackathon
    by /u/Nishkta (Machine Learning) on October 2, 2022 at 10:09 am

    For those interested to learn more and go beyond the EV hype, the Smarter Mobility Data Challenge propose you to tackle one of its most critical assets to manage, charging stations! The challenge is planned to last 2 months, with some with webinars session with EV load experts in order to discover the ins & outs of this particuliar domain. More details on registration is available on Codalab platform (Discord server included as well). Note that part of this hackathon is targeted for students from European institutions, but as all online hackathon, nothing prevent any users to use it as learning experience in the domain or methodology (this is quite an uncommon case of hierarchical forecasting problem). Here's a small extract behind this hackathon motivation: Transport represents almost a quarter of Europe greenhouse gas emissions. The development of electrical vehicles joinly with a low-carbon energy mix can help reducing these emissions and support the transportation sector in its low-carbon transition. Electric mobility development entails new needs for energy providers and consumers. Businesses and researchers are proposing solutions including pricing strategies and smart charging. The goal of these solutions is to avoid dramatically shifting EV users' behaviours and power plants production schedules. However, their implementation requires a precise understanding of charging behaviours. Thus, EV load models are necessary in order to better understand the impacts of EVs on the grid. With this information, the merit of EV charging strategies can be realistically assessed. Forecasting occupation of a charging station can thus be a crucial need for utilities to optimize their production units in accordance with charging needs. On the user side, having information about when and where a charging station will be available is of course of interest. This challenge aims at testing statistical and machine learning forecasting models to forecast the states of a set of charging station in the paris area at different geographical resolution. submitted by /u/Nishkta [link] [comments]

  • [D] Most Popular AI Research Sept 2022 - Ranked Based On GitHub Stars
    by /u/cloud_weather (Machine Learning) on October 2, 2022 at 8:19 am

    submitted by /u/cloud_weather [link] [comments]

  • Do companies/teams accept ppl coming from a completely different field into AI or ML? [D]
    by /u/ritheshgirish9 (Machine Learning) on October 2, 2022 at 3:58 am

    Will companies accept ppl coming from while different domain or background to ML or AI field? Fresh grad been working as a Production support and Release and deploy engineer for 2.5 years now. I'm learning about ML daily doing side projects getting my hands dirty, etc what not to get into ML career. But how do I convince recruiters that I'm a good fit so he can pass on my resume to the managers ?? Pretty sure if I apply on company career website I won't even get shortlisted since mye previous experience would be completely different from what I'm applying for. Let me know how you guys made it, would be really helpful. Every suggestion is welcome. submitted by /u/ritheshgirish9 [link] [comments]

  • [P] stablediffusion-infinity: Outpainting with Stable Diffusion on an infinite canvas
    by /u/Illustrious_Row_9971 (Machine Learning) on October 2, 2022 at 2:34 am

    submitted by /u/Illustrious_Row_9971 [link] [comments]