PyImageSearch

DECEMBER 4, 2023

Home Table of Contents ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent! This blog is the 1st of a 3-part series: ML Days in Tashkent — Day 1: City Tour (this tutorial) ML Days in Tashkent — Day 2: Sprints and Sessions ML Days in Tashkent — Day 3: Demos and Workshops ML Days in Tashkent — Day 1: City Tour Arriving at Tashkent!

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PyImageSearch

DECEMBER 11, 2023

Kicking Off with a Keynote The second day of the Google Machine Learning Community Summit began with an inspiring keynote session by Soonson Kwon, the ML Community Lead at Google. The focus of his presentation was clear and forward-thinking: Accelerate AI/ML research and application.

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Mlearning.ai

APRIL 6, 2023

Automate and streamline our ML inference pipeline with SageMaker and Airflow Building an inference data pipeline on large datasets is a challenge many companies face. SageMaker Batch Job Allows you to run batch inference on large datasets and generate predictions in a batch mode using machine learning (ML) models hosted in SageMaker.

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phData

MARCH 22, 2023

As companies continue to adopt machine learning (ML) in their workflows, the demand for scalable and efficient tools has increased. In this blog post, we will explore the performance benefits of Snowpark for ML workloads and how it can help businesses make better use of their data. Want to learn more? Can’t wait?

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Towards AI

JUNE 27, 2023

Let’s get started with the best machine learning (ML) developer tools: TensorFlow TensorFlow, developed by the Google Brain team, is one of the most utilized machine learning tools in the industry. This open-source library is renowned for its capabilities in numerical computation, particularly in large-scale machine learning projects.

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Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

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Heartbeat

JUNE 26, 2023

A guide to performing end-to-end computer vision projects with PyTorch-Lightning, Comet ML and Gradio Image by Freepik Computer vision is the buzzword at the moment. Today, I’ll walk you through how to implement an end-to-end image classification project with Lightning , Comet ML, and Gradio libraries.

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Smart Data Collective

AUGUST 16, 2022

In the vast majority of cases, the email looks like it’s from a legitimate source, but it actually contains malware that, once downloaded, can give the attacker access to the organization’s network. The post Can ML Fix Cybersecurity Challenges in Healthcare? Ransomware Attacks. appeared first on SmartData Collective.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). The ML Process The machine learning process typically consists of the following steps: Data Collection Gathering relevant data is the first step in the machine learning process.

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Heartbeat

MARCH 6, 2023

library(keras) library(cometr) library(tidyr) To download the Fashion MNIST dataset, add the following code to your R script. Ensure you have your API key from your Comet ML account, then create a .comet.yml First, let’s create a custom function to log losses to Comet ML after each step. comet.yml file in your working directory.

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Heartbeat

FEBRUARY 15, 2023

Add the code below to download the IMDB dataset that has 50K+ reviews for movies from the IMDB website. plot(history) Make sure you log the training loss and accuracy metrics to Comet ML. In addition, we logged some metrics like loss, accuracy, and epochs to Comet ML’s platform. Create a new R Script and call it train.R.

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Mlearning.ai

JULY 29, 2023

Submission Suggestions Deploy Open AI Whisper V2 Manage Endpoint — Azure ML was originally published in MLearning.ai In the example we extract the data from the json input and call the scikit-learn model's predict() method and return the result back """ #logging.info(data) inputs = base64.b64decode(data)

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KDnuggets

SEPTEMBER 20, 2019

This white paper provides the first-ever standard for managing risk in AI and ML, focusing on both practical processes and technical best practices “beyond explainability” alone. Download now.

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AWS Machine Learning Blog

AUGUST 4, 2023

This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models. In this step, we use some of these transformations to prepare the dataset for an ML model. Let’s look at the file without downloading it. Copy and paste the link into a new browser tab URL.

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Mlearning.ai

APRIL 24, 2023

Download the new Runway now. Text to Video Continue reading on MLearning.ai »

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Mlearning.ai

MAY 19, 2023

Here is HuggingFace Link: [link] From the Mosaic ML paper. Here is the HuggingFace Link: [link] From the Mosaic ML paper. This approach offers several benefits, including the elimination of the need to download the entire dataset before commencing training. This model was trained with 9.6M This model was trained with 86M tokens.

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Mlearning.ai

FEBRUARY 25, 2024

Download data. If you’ve been searching for new datasets to practice your time-series forecasting techniques, look no further. Continue reading on MLearning.ai »

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AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models. Business requirements We are the US squad of the Sportradar AI department.

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AWS Machine Learning Blog

SEPTEMBER 13, 2023

A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content. Amazon Comprehend lets non-ML experts easily do tasks that normally take hours of time.

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AWS Machine Learning Blog

NOVEMBER 26, 2023

Amazon Personalize is a fully managed machine learning (ML) service that makes it effortless for developers to deliver highly personalized user experiences in real time. You can get started without any prior ML experience, using APIs to easily build sophisticated personalization capabilities in a few clicks.

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Mlearning.ai

JULY 4, 2023

The CUDA toolkit can be downloaded from the NVidia website. Download model Now that all dependencies are installed, we are ready to download a model. The text-generation-webui provides a handy script which will place the downloaded model in the correct location. Simply run python download-model.py GPTQ-4bit-128g.

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Mlearning.ai

JUNE 10, 2023

Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption — this allows us to run ML models on user’s device keeping their data private as well as allowing offline model inference. models/download-ggml-model.sh Generate Core ML Model./models/generate-coreml-model.sh

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AWS Machine Learning Blog

FEBRUARY 20, 2024

In this post, we discuss deploying scalable machine learning (ML) models for diarizing media content using Amazon SageMaker , with a focus on the WhisperX model. Download the model and its components WhisperX is a system that includes multiple models for transcription, forced alignment, and diarization.

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Dataconomy

NOVEMBER 28, 2023

Download and Share: Your moving picture is ready! You can download it, share it with friends, and use it in other editing tools. Featured image credit: Runway ML Set Controls: Tell the tool which way you want things to move – up, down, left, right. You can make it longer if you want!

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AWS Machine Learning Blog

MAY 15, 2023

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. The Bulk Document Uploader provides results that you can download later for offline review. The output results are available for download for 7 days after processing.

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The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams. DVC Git LFS neptune.ai

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AWS Machine Learning Blog

JANUARY 22, 2024

Amazon Textract is a machine learning (ML) service that enables automatic extraction of text, handwriting, and data from scanned documents, surpassing traditional optical character recognition (OCR). Download the deployment code and sample vaccination card from GitHub. In the terminal, choose Upload Local Files on the File menu.

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AWS Machine Learning Blog

APRIL 11, 2023

Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit , developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. The no-cache-dir flag will disable the cache.

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IBM Journey to AI blog

MARCH 8, 2024

However, due to significant improvements in latency, throughput and bandwidth, 5G is capable of much faster download and upload speeds than previous networks. This means routine activities like downloading a file or working in the cloud is going to be much faster with a 5G connection than a connection on a different network.

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Mlearning.ai

SEPTEMBER 18, 2023

Free Download Now, But Future Access May Require Microsoft 365 Subscription Continue reading on MLearning.ai »

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AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. GitHub serves as a centralized location to store, version, and manage your ML code base.

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AWS Machine Learning Blog

DECEMBER 12, 2023

For instructions on how to install npm , refer to Downloading and installing Node.js Deploy SageMaker foundation models SageMaker is a fully managed machine learning (ML) service for developers to quickly build and train ML models with ease. Choose the lambda.cfn.yaml file that you downloaded, then choose Next.

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IBM Data Science in Practice

MARCH 8, 2023

If you are set up with the required systems, you can download the sample project and complete the steps for hands-on learning. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices. You can review the steps in this article to familiarize yourself with the process.

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DagsHub

NOVEMBER 1, 2023

They specialize in developing cutting-edge, AI-based drug delivery solutions, which integrate machine learning (henceforth: ML) and nanotechnology. They use ML, particularly graph neural networks, to analyze the interactions within molecules and develop models that predict chemical and biological properties.

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phData

JULY 17, 2023

The advancement of technology in large language models (LLMs), machine learning (ML), and data science can truly transform industries through insights and predictions. Just click this button and fill out the form to download it. Solutions Looking for Problems Many ML projects are spawned based on external inspiration.

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Flipboard

FEBRUARY 15, 2023

Amazon SageMaker JumpStart is the machine learning (ML) hub of SageMaker that offers over 350 built-in algorithms, pre-trained models, and pre-built solution templates to help you get started with ML fast. We then use a pre-built MLOps template to bootstrap the ML workflow and provision a CI/CD pipeline with sample code.

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AWS Machine Learning Blog

JUNE 8, 2023

Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. This provides a unified end-to-end ML experience across ML developers of varying levels of expertise.

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