AWS Machine Learning Certification Specialty Exam Prep

AWS Machine Learning Specialty Certification Prep (Android)

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 Prep PRO

‎AWS Machine Learning Prep PRO
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
  • ‎AWS Machine Learning Prep PRO Screenshot
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

tolerance.

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

problem.

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:

Ingestion/Collection

Processing/ETL

Data analysis/visualization

Model training

Model deployment/inference

Operational

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] How can i get access to "Advanced Machine Learning Specialization" after it was removed from coursera
    by /u/bechir2000 (Machine Learning) on June 30, 2022 at 10:04 am

    "Advanced Machine Learning Specialization" has two very interesting courses for me such as "Addressing Large Hadron Collider Challenges by Machine Learning" and practical RL. This specialization is made by HSE University which is russian. BASED ON THAT REASON COURSERA REMOVED IT. I can't understand this decision and i don't agree with it even though i am against the war. So i am here looking for ways to get access to this course maybe through another platform or if someone downloaded the lectures. that would be very helpful. thank you submitted by /u/bechir2000 [link] [comments]

  • [D] Merging two iterators (pytorch dataloaders)
    by /u/Meddhouib10 (Machine Learning) on June 30, 2022 at 9:19 am

    Hello everyone ! So I have two dataloaders that I want to iterate throught, like not in a chained way. I want to simple elements from one or the other each iteration step randomly. Is there anyway to create a pytorch dataloader that does that ? Like what function should a pytorch dataloaders contain. I found nothing on this subject on the internet that’s why I’m asking for your help Thanks in advance submitted by /u/Meddhouib10 [link] [comments]

  • [P] Detectron2 - Same Code&Data // Different platforms // highly divergent results
    by /u/caenum (Machine Learning) on June 30, 2022 at 8:58 am

    Hello! I used different hardware/platforms to benchmark multiple possibilites. The Code runs in a jupyter Notebook. When i evaluate the different losses i get highly divergent results. I also checked the full .cfg with `cfg.dump()` - it is completely consistent. I appreciate any help. Thanks in advance. ​ I also added the value "cfg.SEED=55" to the parameters, but the output/results are the same.. ​ 1.Environment Microsoft Azure - Machine Learning STANDARD_NC6 Torch: 1.9.0+cu111 Results: ​ https://preview.redd.it/pg1vf2224q891.png?width=426&format=png&auto=webp&s=1c22a1f56a3be5c3bf11949b7810aa28454e75a9 2.Environment GoogleColab free Torch: 1.9.0+cu111 ​ Results: ​ https://preview.redd.it/ufh66ih24q891.png?width=426&format=png&auto=webp&s=b34bec072fc8a72c24ca4bd43eb42223ddace8b0 ​ Detectron2 Parameters: ​ cfg = get_cfg() cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/retinanet_R_101_FPN_3x.yaml")) cfg.DATASETS.TRAIN = ("dataset_train",) cfg.DATASETS.TEST = ("dataset_test",) cfg.DATALOADER.NUM_WORKERS = 2 cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/retinanet_R_101_FPN_3x.yaml") # Let training initialize from model zoo cfg.SOLVER.IMS_PER_BATCH = 2 cfg.SOLVER.BASE_LR = 0.00025 # 0.00125 pick a good LR cfg.SOLVER.MAX_ITER = 1200 # 300 iterations seems good enough for this toy dataset; you will need to train longer for a practical dataset cfg.SOLVER.STEPS = [] # do not decay learning rate cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 # faster, and good enough for this toy dataset (default: 512) #cfg.MODEL.ROI_HEADS.NUM_CLASSES = 25 # only has one class (ballon) cfg.MODEL.RETINANET.NUM_CLASSES = 3 # NOTE: this config means the number of classes, but a few popular unofficial tutorials incorrect uses num_classes+1 here. cfg.OUTPUT_DIR = "/content/drive/MyDrive/Colab_Notebooks/testrun/output" cfg.TEST.EVAL_PERIOD = 25 cfg.SEED=5 submitted by /u/caenum [link] [comments]

  • [D] Recommendation for books ( Medium - Advanced )
    by /u/Raph_Bellahs (Machine Learning) on June 30, 2022 at 8:28 am

    Hi Everyone, just finished my basics courses in Machine Learning, I am searching for books preferably in color 🙂 on the more advanced subjects ( NLP, Retrieval Information ... ) and some books maybe with End-To-End projects to know how big ML project can be done to have some more concrete things to say in an interview ​ thanks! submitted by /u/Raph_Bellahs [link] [comments]

  • [R] RankSEG: A Consistent Ranking-based Framework for Segmentation
    by /u/statmlben (Machine Learning) on June 30, 2022 at 7:20 am

    I am very excited to share our latest research: a new framework RankSEG on (image) segmentation. Abs: In this paper, we establish a theoretical foundation of segmentation with respect to the Dice/IoU metrics, including the Bayes rule and Dice/IoU-calibration, analogous to classification-calibration or Fisher consistency in classification. We prove that the existing thresholding-based framework with most operating losses are NOT consistent with respect to the Dice/IoU metrics, and thus may lead to a suboptimal solution. To address this pitfall, we propose a novel consistent ranking-based framework, namely RankDice/RankIoU, inspired by plug-in rules of the Bayes segmentation rule. Three numerical algorithms with GPU parallel execution are developed to implement the proposed framework in large-scale and high-dimensional segmentation. We study statistical properties of the proposed framework. We show it is Dice-/IoU-calibrated, and its excess risk bounds and the rate of convergence are also provided. The numerical effectiveness of RankDice/mRankDice is demonstrated in various simulated examples and Fine-annotated CityScapes and Pascal VOC datasets with state-of-the-art deep learning architectures. Conclusion: the proposed framework RankSEG consistently outperforms the existing thresholding-based framework (simply thresholding the estimated probabilities at 0.5). Contribution: We summarize our major contribution as follows: To our best knowledge, the proposed ranking-based segmentation framework RankDice, is the first consistent segmentation framework with respect to the Dice metric (Dice-calibrated). Three numerical algorithms with GPU parallel execution are developed to implement the proposed framework in large-scale and high-dimensional segmentation. We establish a theoretical foundation of segmentation with respect to the Dice metric, such as the Bayes rule and Dice-calibration. Moreover, we present Dice-calibrated consistency and a convergence rate of the excess risk for the proposed RankDice framework, and indicate inconsistent results for the existing methods. Our experiments in two simulated examples and two real datasets (CityScapes dataset and Pascal VOC 2021 dataset) suggest that {the improvement of RankDice over the existing framework is practically significant for various loss functions and network architectures. The percentage of improvement on the best performance (for each framework) are 3.13% (over threshold) and 4.96% (over argmax) for CityScapes dataset (PSPNet + CE), and 3.87% (over threshold) and 2.91% (over argmax) for Pascal VOC 2021 dataset (PSPNet + CE/BCE). For more information, please check: Paper: https://arxiv.org/abs/2206.13086 Github website: https://github.com/statmlben/rankseg submitted by /u/statmlben [link] [comments]

  • [D] Why are transformers still being used?
    by /u/DickMan64 (Machine Learning) on June 30, 2022 at 7:20 am

    We already have architecture(s) which are supposed to fix one of the biggest issues with transformers, namely that they scale quadratically with input size. The performer scales linearly, which should allow for much bigger context windows, yet looking at recent large language models from major players, all of them seem to be using the old transformer save for some minor improvements. The only exception was Flamingo which had to use a Perceiver because images are huge. So why haven't we ditched the transformer yet? submitted by /u/DickMan64 [link] [comments]

  • [D] How would go about tracking an ML run when the framework logs text to a txt log?
    by /u/mrwafflezzz (Machine Learning) on June 30, 2022 at 7:18 am

    I was hoping that Mlflow had a method or function for parsing a txt log, but I can't find anything. Does anyone know of an elegant solution that runs in parallel to the training process? submitted by /u/mrwafflezzz [link] [comments]

  • [D] Machine Learning and Metaverses
    by /u/True-Engineering-90 (Machine Learning) on June 30, 2022 at 6:06 am

    I am interested in Metaverses (many companies have announced that they are creating its own metaverses, not only Meta). And I would ask ML community, does anybody work on the projects of metaverse creation? What kind of machine learning tasks do you have inside? submitted by /u/True-Engineering-90 [link] [comments]

  • [N] Introducing Anomalib: A library for benchmarking, developing and deploying deep learning anomaly detection algorithms by Intel
    by /u/alder-ice (Machine Learning) on June 30, 2022 at 6:00 am

    Anomalib is Machine Library developed by AI researchers from Intel which implements state of the art algorithms for anomaly detection. Anomaly detection is popular use case in the industrial sector and such algorithms can help provide real-time feedback to manufactures on how well their production lines are performing. Anomaly Detection is a challenging problem often due to a biased dataset. Anomalous images can be scare therefore these algorithms are trained on good images in an unsupervised fashion. By learning the normality, upon inference, the models can detect whether images are anomalous or not. Anomalib was built using a PyTorchLightning Backbone and offers an easy way to deploy the models with OpenVino for inference speedup. Link to the github repo: https://github.com/openvinotoolkit/anomalib Link to a tutorial on how to train your custom dataset with anomalib: https://github.com/openvinotoolkit/anomalib/tree/development/docs/blog/001-train-custom-dataset Please feel free to check out the repo and give us your feedback submitted by /u/alder-ice [link] [comments]

  • [P] Recommender system advice
    by /u/ButtFlannel69 (Machine Learning) on June 30, 2022 at 5:02 am

    Hi! I've just started work on a recommender system that I'll be implementing from scratch and wanted to get some feedback on the possible routes I may take. Speed of implementation and explainability will be important in the first iteration but of course I would like to optimise the recommendations over time. I have several user inputs that will form the ratings between users and items and I will probably start with a relatively simple implementation such as KNN memory based collaborative filtering. For this as far as I'm aware I will need to aggregate the multiple inputs into a single rating? The first option is to manually weight each input to get a single rating and then input to the KNN CF model. The second option is to use a random forest + OOB error or another model + SHAP values to predict the user-item ratings and get the feature importance. The third is to use a hybrid of the previous two where the weights are set by the feature importance but the final predictions are made by the KNN CF model. I'm relatively new to recommendation systems so any constructive criticisms and advice on alternate approaches would be really, really welcome. Thanks in advance! submitted by /u/ButtFlannel69 [link] [comments]

  • [D] On advisors and PhD students
    by /u/carlml (Machine Learning) on June 30, 2022 at 3:07 am

    I think the answer to this question depends on heavily on the area at hand. That is why I am asking here, even though this question has been asked elsewhere a gazillion times. How much does your advisor help/contribute? How often do you meet? I am especially interested in people who have published papers. Who proposed the problem, and then found a solution? how much of that solution was joint work vs either of you submitting ideas to the other and being approved or rejected? how satisfied/dissatisfied do you feel with respect to your advisor? have you had multiple advisors? if so, how do they compare? Let me start by sharing my experience. I always take the initiative when organizing a meeting with my advisor; if I don't say anything, we probably wouldn't meet. I send him biweekly emails with my progress. Usually this entails a write-up explaining my ideas and their development. I think he skims through it, but he definitely does not read it carefully/go through the details. When we have a meeting I generally have to explain the content of the write-up. In terms of the content itself, he tells me whether the ideas/problem seem sound or not, but does not propose improvements. Sometimes, he proposes other ideas that would imply a significant shift of my current work, which honestly I tend to reject because I have already invested a great deal of time to my ideas and I am more emotionally attached to them (I know this latter point isn't good practice). Overall, I don't know how to feel because I don't really know what's generally expected. If I had to chose, however, I'd say I feel mildly satisfied. What's your experience? submitted by /u/carlml [link] [comments]

  • [D] How well does auto annotating a dataset with a pretrained model work?
    by /u/big_black_doge (Machine Learning) on June 30, 2022 at 2:44 am

    Hi reddit, I am asking this question because I don't see much about this in the literature. I want to build an object detection model (class + bounding box), but I have very little annotated data. I have an idea to use a pretrained model to create predicted annotations from a very large object classification (class, no bounding box) dataset. I can then filter those predicted annotations to use only high confidence annotations, and manually check the images to ensure quality. Then, I should have a large, high quality object detection dataset with which I can train my model with. I didn't see many papers on this type of thing. It's not exactly transfer learning, because it's actually building a task specific dataset from another type of dataset. Is there some reason why this wouldn't work? Or does anyone have any research on this type of idea? submitted by /u/big_black_doge [link] [comments]

  • [Discussion] Regarding Long Term Memory in NLP Models
    by /u/gabe415160 (Machine Learning) on June 30, 2022 at 12:56 am

    Does anyone know if there exists a NLP model, like Lambda, that takes every conversation attempts to update their weights in order to incorporate it into its training? My thought process would be instead of using attention and a subsection of the conversation to generate a response, it takes everything. Basically everything gets back propagated and adjusts the weights. This way the model might begin to "remember" its previous conversations. This may be a stretch and perhaps I am missing something fundamental, but it seems like an interesting experiment. I'd love to continue this conversation and elaborate more in the comments. submitted by /u/gabe415160 [link] [comments]

  • Use a custom image to bring your own development environment to RStudio on Amazon SageMaker
    by Michael Hsieh (AWS Machine Learning Blog) on June 29, 2022 at 10:04 pm

    RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on

  • Text classification for online conversations with machine learning on AWS
    by Ryan Brand (AWS Machine Learning Blog) on June 29, 2022 at 6:58 pm

    Online conversations are ubiquitous in modern life, spanning industries from video games to telecommunications. This has led to an exponential growth in the amount of online conversation data, which has helped in the development of state-of-the-art natural language processing (NLP) systems like chatbots and natural language generation (NLG) models. Over time, various NLP techniques for

  • Hyperparameter optimization for fine-tuning pre-trained transformer models from Hugging Face
    by Aaron Klein (AWS Machine Learning Blog) on June 29, 2022 at 4:57 pm

    Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune

  • Diagnose model performance before deployment for Amazon Fraud Detector
    by Julia Xu (AWS Machine Learning Blog) on June 29, 2022 at 4:13 pm

    With the growth in adoption of online applications and the rising number of internet users, digital fraud is on the rise year over year. Amazon Fraud Detector provides a fully managed service to help you better identify potentially fraudulent online activities using advanced machine learning (ML) techniques, and more than 20 years of fraud detection

  • [P] Neural Network Steganography (implementation) - Hiding secrets and malicious software in any neural network
    by /u/gabegabe6 (Machine Learning) on June 29, 2022 at 1:57 pm

    I saw a paper called EvilModel on how to hide malicious code in a neural network as we have thousands or millions of parameters that we can alter. This basic technique is based on the modification of the float32 values (but can be adapted to float16) where we modify the fraction bits or part of the fraction. Post/Tutorial on the process GitHub repo for the project EvilModel paper As I saw with my experiments, we could easily hide megabytes of code in a simple ResNet50 and get away with it. A well-trained (and generalized) network should not degrade in performance significantly. The testing of that is planned for a future post. Also, this method could be used for watermarking neural network weights which could help with copyright claims (e.g.: someone is using your open-sourced (and appropriately licensed) weights out of the box in a commercial product) submitted by /u/gabegabe6 [link] [comments]

  • [D] Training GANs with non-square images
    by /u/antarfrica (Machine Learning) on June 29, 2022 at 12:41 pm

    I am planning to train stylegan2 ada with rectangular images (aspect ratio = 16:9). Is it better to use (zero) padding, resizing, or train a rectangular GAN? Thankyou verymuch! submitted by /u/antarfrica [link] [comments]

  • [D] Mixed Precision Training: Difference between BF16 and FP16
    by /u/optimized-adam (Machine Learning) on June 29, 2022 at 11:44 am

    What differences in model performance, speed, memory etc. can I expect between choosing BF16 or FP16 for mixed precision training? Is BF16 faster / consumes less memory, since I have seen people say it is "more suitable for Deep Learning". Why is that the case? submitted by /u/optimized-adam [link] [comments]

  • [R] Use pretrained GANs and image classifier to generate images of the class
    by /u/ml_rl_questions (Machine Learning) on June 29, 2022 at 9:42 am

    Pretrained GANs and CLIP embeddings have been used to created images from arbitrary caption, by backpropagating CLIP similarity of the caption and the generated image down to the generator input noise. I am thinking of something simpler, where I would take a pretrained GAN, and backpropagate through some pretrained classifier (e.g. image et) down to the input noise to the Generator to generate images of that class. Is there any reference that does that? And more generally, i want to understand why this approach works - simple backpropagating the classifier loss to the image (and not through the generator) typically result in deep dream type of weird images. Why does this not happen when using a generator? Is it simply because the output of the generator lives in the manifold of "real" images? Is there more to it? Thanks in advance submitted by /u/ml_rl_questions [link] [comments]

  • [D] What are the lessons learned in the preparations of the dataset you will use to train a GANs?
    by /u/metover (Machine Learning) on June 29, 2022 at 8:09 am

    Hello friends, what are the key points we should pay attention to in the datasets you will prepare for GANs, do you have any suggestions? For example the distribution of the dataset should be like this, the images should be the same size, it is important to reduce all the images to this size, many things that I have not thought of at the moment? What are your recommendations? submitted by /u/metover [link] [comments]

  • [P] Unofficial Gato in TensorFlow
    by /u/AvisStudio (Machine Learning) on June 29, 2022 at 4:18 am

    https://github.com/OrigamiDream/gato I am building Deepmind's Gato imitation in TensorFlow. All necessary layers have been completely implemented. ​ However, I have no idea how to map out the training strategy, and I do not have enough datasets for this. The model seems impossible for end-to-end training because of its conditional and selective tokenizer and embeddings, and differentiable programming. ​ If you are interested in this project, add a star and notification to this repository for further updates. And someone who want to contribute to this project, please create a relevant issue or pull request. ​ Thank you. submitted by /u/AvisStudio [link] [comments]

  • [D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
    by /u/RepresentativeCod613 (Machine Learning) on June 28, 2022 at 9:55 pm

    YOLOv6 has been making a lot of noise in the past 24 hours. Based on its performance - rightfully so. YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. It outperforms YOLOv5 in accuracy and inference speed, making it the best OS version of YOLO architecture for production applications. I dived into the technical details published by the research group and made a qualitative and qualitative comparison between the results of YOLOv5 and YOLOv6. I invite you to read about all of these, with a bit of history on YOLO, in the my new blog submitted by /u/RepresentativeCod613 [link] [comments]

  • Creating and Analyzing a Dataset of Roe v. Wade Tweets Labeled by Abortion Stance [P]
    by /u/BB4evaTB12 (Machine Learning) on June 28, 2022 at 9:44 pm

    How do pro-choice vs. pro-life twitter users differ? I built a free, labeled dataset of #RoeVsWade tweets, and an ML classifier on top. Some insights: Pro-life users are 20.4x more likely to put "christ" and 16.1x more likely to put "maga" in their bio.Pro-choice users are 7.5x more likely to put "blm" and 6.5x more likely to put "she/her". Full analysis + link to raw dataset here. submitted by /u/BB4evaTB12 [link] [comments]

  • [N] PyTorch 1.12: TorchArrow, Functional API for Modules and NvFuser
    by /u/DreamFlasher (Machine Learning) on June 28, 2022 at 8:07 pm

    PyTorch 1.12 Release Notes Highlights Backwards Incompatible Change New Features Improvements Performance Documentation Highlights We are excited to announce the release of PyTorch 1.12! This release is composed of over 3124 commits, 433 contributors. Along with 1.12, we are releasing beta versions of AWS S3 Integration, PyTorch Vision Models on Channels Last on CPU, Empowering PyTorch on Intel® Xeon® Scalable processors with Bfloat16 and FSDP API. We want to sincerely thank our dedicated community for your contributions. Summary: Functional Module API to functionally apply module computation with a given set of parameters Complex32 and Complex Convolutions in PyTorch DataPipes from TorchData fully backward compatible with DataLoader Functorch with improved coverage for APIs nvFuser a deep learning compiler for PyTorch Changes to float32 matrix multiplication precision on Ampere and later CUDA hardware TorchArrow, a new beta library for machine learning preprocessing over batch data https://github.com/pytorch/pytorch/releases/tag/v1.12.0 https://pytorch.org/blog/pytorch-1.12-released/ submitted by /u/DreamFlasher [link] [comments]

  • Create audio for content in multiple languages with the same TTS voice persona in Amazon Polly
    by Patryk Wainaina (AWS Machine Learning Blog) on June 28, 2022 at 7:28 pm

    Amazon Polly is a leading cloud-based service that converts text into lifelike speech. Following the adoption of Neural Text-to-Speech (NTTS), we have continuously expanded our portfolio of available voices in order to provide a wide s