Pickl AI

MAY 29, 2024

Understanding Data Science Data Science is a multidisciplinary field that combines statistics, mathematics, computer science, and domain-specific knowledge to extract insights and wisdom from structured and unstructured data. Skills in data manipulation and cleaning are necessary to prepare data for analysis.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Flipboard

JULY 10, 2023

Figure 3 illustrates the visualization of the latent space and the process we discussed in the story, which aligns with the technical definition of the encoder and decoder. During training, the input data is intentionally corrupted by adding noise, while the target remains the original, uncorrupted data.

Deep Learning 131

Deep Learning Deep Learning Machine Learning Machine Learning 131

Pickl AI

NOVEMBER 2, 2023

Cheat sheets for Data Scientists Cheat sheets are like treasure maps for Data Scientists, helping them navigate the vast sea of information and tools available to them. These reference guides condense complex concepts, algorithms, and commands into easy-to-understand formats.

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Mlearning.ai

MAY 23, 2023

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. It further performs badly on the test data set.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

DagsHub

FEBRUARY 29, 2024

While data preparation for machine learning may not be the most “glamorous” aspect of a data scientist’s job, it is the one that has the greatest impact on the quality of model performance and consequently the business impact of the machine learning product or service. What are the different Data Preparation Steps?

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

Pickl AI

JULY 8, 2024

These pipelines automate collecting, transforming, and delivering data, crucial for informed decision-making and operational efficiency across industries. Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Snorkel AI

JANUARY 16, 2024

It’s not just about accuracy, and it’s definitely not just about one test set. The original paper that coined the term “ large language model ” was a 2007 Google paper where they used an algorithm called “Stupid Backoff.” You need data that’s labeled and curated for your use case. DK: For sure.

Supervised Learning 52

Supervised Learning AI AI Algorithm 52

Snorkel AI

JANUARY 16, 2024

It’s not just about accuracy, and it’s definitely not just about one test set. The original paper that coined the term “ large language model ” was a 2007 Google paper where they used an algorithm called “Stupid Backoff.” You need data that’s labeled and curated for your use case. DK: For sure.

Supervised Learning 52

Supervised Learning AI AI Algorithm 52

Snorkel AI

APRIL 28, 2023

Often, it requires you to co-design the algorithm and also the system set. If they’re necessary, how can we create a new algorithm to accommodate it? How can we adapt the model to different scenarios as systematic and data-efficient as possible? In this case, you can also use fairness as an objective for data debugging.

ML 52

ML ML Machine Learning Machine Learning 52

Snorkel AI

APRIL 28, 2023

Often, it requires you to co-design the algorithm and also the system set. If they’re necessary, how can we create a new algorithm to accommodate it? How can we adapt the model to different scenarios as systematic and data-efficient as possible? In this case, you can also use fairness as an objective for data debugging.

ML 52

ML ML Machine Learning Machine Learning 52

DagsHub

OCTOBER 23, 2024

Here are some challenges you might face while managing unstructured data: Storage consumption: Unstructured data can consume a large volume of storage. For instance, if you are working with several high-definition videos, storing them would take a lot of storage space, which could be costly.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Snorkel AI

JULY 3, 2023

It has always amazed me how much time the data cleaning portion of my job takes to complete. So today I’m going to talk about an approach I often use to help remedy the time burden: reusable data cleaning pipelines. Python’s definitely the most popular. AB : Makes sense. JG : Exactly. AB : Makes sense.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

It has always amazed me how much time the data cleaning portion of my job takes to complete. So today I’m going to talk about an approach I often use to help remedy the time burden: reusable data cleaning pipelines. Python’s definitely the most popular. AB : Makes sense. JG : Exactly. AB : Makes sense.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

It has always amazed me how much time the data cleaning portion of my job takes to complete. So today I’m going to talk about an approach I often use to help remedy the time burden: reusable data cleaning pipelines. Python’s definitely the most popular. AB : Makes sense. JG : Exactly. AB : Makes sense.

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

How to Learn Machine Learning

JUNE 9, 2025

If you’re hoping to deploy with success in the real world, this is definitely worth the read. Therefore, when a data scientist collaborates without the data engineering team or does not coordinate with stakeholders, we can end up with models that are good at technical deployment, but practically dead in the business.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

AWS Machine Learning Blog

DECEMBER 19, 2024

Apply the MinHash algorithm as shown in the preceding example and calculate the similarity scores between paragraphs. output_first_template = '''Given the classification task definition and the class labels, generate an input that corresponds to each of the class labels. Combine duplicate pairs into clusters.

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AWS Machine Learning Machine Learning Natural Language Processing 103