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). In this comprehensive guide, I will demystify machine learning, breaking it down into digestible concepts for beginners. What is Machine Learning?

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Towards AI

APRIL 7, 2024

With the emergence of ARCGISpro which will replace ArcMap by 2026 mainly focusing on data science and machine learning, all the signs that machine learning is the future of GIS and you might have to learn some principles of data science, but where do you start, let us have a look.

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Towards AI

FEBRUARY 3, 2024

Photo by Markus Winkler on Unsplash Let’s get started: Machine Learning has become the most demanding and powerful tool in different domains of several industries in this digital era to solve many complex problems by revolutionizing the way of approaching those problems.

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Marktechpost

MAY 1, 2024

CyberSecEval 2 categorizes prompt injection assessment tests into logic-violating and security-violating types, covering a broad range of injection strategies. A robust test set evaluates FRR for cyberattack helpfulness risk, revealing LLMs’ ability to handle borderline requests while rejecting the most unsafe ones.

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Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Embrace the benefits of feature engineering to unlock the full potential of your Machine-Learning endeavors and achieve accurate predictions in diverse real-world scenarios.

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ODSC - Open Data Science

MARCH 6, 2023

In an effort to learn more about our community, we recently shared a survey about machine learning topics, including what platforms you’re using, in what industries, and what problems you’re facing. For currently-used machine learning frameworks, some of the usual contenders were popular as expected.

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Marktechpost

APRIL 10, 2024

Equitable Representation of Minority Classes: AnchorAL produces datasets with greater balance, which is necessary for precise categorization. Model Performance: AnchorAL improves classification accuracy by training models that are more performant than those trained by rival techniques. If you like our work, you will love our newsletter.

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ODSC - Open Data Science

MARCH 13, 2023

Recently, we posted the first article recapping our recent machine learning survey. There, we talked about some of the results, such as what programming languages machine learning practitioners use, what frameworks they use, and what areas of the field they’re interested in. As the chart shows, two major themes emerged.

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Marktechpost

DECEMBER 11, 2023

Although measuring the degree of uncertainty in human judgments is challenging, machine learning methods offer assurance in their forecasts. Nevertheless, many machine learning applications use pretrained models that don’t provide any measure of atypicality. Rather, they only offer confidence levels.

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Marktechpost

APRIL 29, 2024

Computer vision, machine learning, and data analysis across many fields have all seen a surge in the usage of synthetic data in the past few years. Concerning tabular data, one of the biggest obstacles is maintaining consistency when dealing with fluctuating percentages of numerical and categorical data.

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Towards AI

AUGUST 6, 2023

Are you overwhelmed by the recent progress in machine learning and computer vision as a practitioner in academia or in the industry? Motivation Recent updates in machine learning (ML) and computer vision (CV) are a mouthful, from Stable Diffusion for generative artificial intelligence (AI) to Segment Anything as foundation models.

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Marktechpost

MARCH 22, 2024

Researchers investigating LLM security vulnerabilities have explored various jailbreak attack methodologies, categorized into Human-Design, Long-tail Encoding, and Prompt Optimization. Human design involves manually crafting prompts to exploit model weaknesses, such as role-playing or scenario crafting.

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Towards AI

JULY 17, 2023

Pyspark MLlib | Classification using Pyspark ML In the previous sections, we discussed about RDD, Dataframes, and Pyspark concepts. In this article, we will discuss about Pyspark MLlib and Spark ML. Spark MLlib is a short form of spark machine-learning library. It works on distributed systems and is scalable.

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Topbots

JUNE 22, 2023

ChatGPT is a GPT ( G enerative P re-trained T ransformer) machine learning (ML) tool that has surprised the world. Moreover, its capacity to be an ML model trained for general tasks and perform very well in domain-specific situations is impressive. Domain-specific sentiment analysis ML model for financial news domain.

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Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning? Machine learning algorithms can make predictions or classifications based on input data.

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Mlearning.ai

FEBRUARY 28, 2023

Theoretical Explanations and Practical Examples of Correlation between Categorical and Continuous Values Without any doubt, after obtaining the dataset, giving entire data to any ML model without any data analysis methods such as missing data analysis, outlier analysis, and correlation analysis.

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AWS Machine Learning Blog

OCTOBER 26, 2023

Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. Our AI/ML research team focuses on identifying the best computer vision (CV) models for our system.

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Heartbeat

DECEMBER 13, 2023

Guide to evaluation metrics for classification in machine learning Photo by Jon Tyson on Unsplash In machine learning, data scientists use evaluation metrics to assess the model's performance in terms of the ability of the various machine learning models to classify the data points into their respective classes accurately.

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IBM Journey to AI blog

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

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Mlearning.ai

FEBRUARY 2, 2023

Mind Map: Mistakes in ML model training This blog highlights some important mistakes that one can make while training a machine learning model. Machine Learning model training is the process of teaching a model how to recognize patterns in data. What can go wrong in ML model training?

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Becoming Human

MAY 12, 2023

In what ways do we understand image annotations, the underlying technology behind AI and machine learning (ML), and its importance in developing accurate and adequate AI training data for machine learning models? Overall, it shows the more data you have, the better your AI and machine learning models are.

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Snorkel AI

JUNE 30, 2023

Jack Zhou, product manager at Arize , gave a lightning talk presentation entitled “How to Apply Machine Learning Observability to Your ML System” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So ML ends up being a huge part of many large companies’ core functions. Our agenda.

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Snorkel AI

JUNE 30, 2023

Jack Zhou, product manager at Arize , gave a lightning talk presentation entitled “How to Apply Machine Learning Observability to Your ML System” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So ML ends up being a huge part of many large companies’ core functions. Our agenda.

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Marktechpost

NOVEMBER 12, 2023

The recent development in the fields of Artificial Intelligence (AI) and Machine Learning (ML) models has turned the discussion of Artificial General Intelligence (AGI) into a matter of immediate practical importance. This framework has introduced three important dimensions: autonomy, generality, and performance.

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Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? ✔ how to reduce the complexity and computational expensiveness of ML models ? will my data help in this ? These code chunks will help you !!!

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Mlearning.ai

APRIL 17, 2023

Developing a machine learning model involves several steps: problem formulation, data collection, preprocessing, feature engineering, model-building, deployment, and monitoring. image by author Introduction Error analysis is a vital process in diagnosing errors made by an ML model during its training and testing steps.

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Pickl AI

JULY 28, 2023

Image Recognition using Machine Learning and MATLAB: With the advent of Machine Learning , it is now possible for computers to recognize and decipher objects, patterns, and other properties in digital photographs. The model then uses this information to categorize and analyze new objects and images it sees in the future.

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Mlearning.ai

JUNE 28, 2023

{This article was written without the assistance or use of AI tools, providing an authentic and insightful exploration of PyCaret} Image by Author ‍In the rapidly evolving realm of data science, the imperative to automate machine learning workflows has become an indispensable requisite for enterprises aiming to outpace their competitors.

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Towards AI

APRIL 7, 2024

U+1F44B Welcome to another exciting journey in the realm of machine learning. Today, we’re going to discuss about the often overlooked but incredibly crucial aspect of Building ML models, i.e, Deploying machine learning models. Why learning to deploy the ML model is important? apply(lambda x: str(x).replace('

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TensorFlow

FEBRUARY 3, 2023

If you're looking to create an exciting new web project or take your work to the next level, then I recommend adding machine learning (ML)! Near year, new solutions MediaPipe has been a great go-to solution for web developers interested in adding ML to their web applications. Resolve to make something great!

Machine Learning 52

Machine Learning ML Categorization Python 52

Snorkel AI

SEPTEMBER 5, 2023

As Snorkel Flow’s customer base and ML product capabilities have scaled, our cloud costs have unsurprisingly increased. In order to grow efficiently, we set out to understand how we can improve our margins while operating state-of-the-art ML software. GPUs) and involve “bursty” (i.e. high load but infrequent) compute.

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Marktechpost

MARCH 11, 2024

In deep learning, classification tasks show effectiveness with large neural networks, while regression tasks can benefit from reframing as classification, enhancing performance. This shift involves converting real-valued targets to categorical labels and minimizing categorical cross-entropy. Check out the Paper.

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Marktechpost

APRIL 5, 2024

The proposed approach was explained by analyzing neural network architectures, particularly transformers, from a categorical perspective, specifically utilizing topos theory. While traditional neural networks can be embedded in pretopos categories, transformers necessarily reside in a topos completion.

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Towards AI

AUGUST 25, 2023

How to get started with an AI project Vackground on Unsplash Background Here I am assuming that you have read my previous article on How to Learn AI. Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed.

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Heartbeat

FEBRUARY 15, 2023

It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. We pay our contributors, and we don’t sell ads.

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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. SageMaker Data Wrangler offers over 300 built-in transformations.

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Heartbeat

MARCH 6, 2023

Source: [link] Computer vision is an interesting field in machine learning as it helps computers understand what they see. Because the output of the network is a probability score of multiple categories, you can use a sparse categorical cross-entropy loss function. Add the following code block to your R script.

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