Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In ML, there are a variety of algorithms that can help solve problems. 12, 2021. [6] MIT Press, ISBN: 978–0262028189, 2014.

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Data Science Dojo

JANUARY 18, 2023

Fintech trends 2023 – Data Science Dojo With increasing reluctance from investors, lack of funding, and fewer jobs, 2023 might seem like a glum year ahead. AI and ML algorithms in fintech supporting financial transactions such as banking and lending will have an impartial say in which individuals have access to banking in 2023.

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Data Science Dojo

MARCH 15, 2024

It spun off in 2017 from OpenAI by its ex-research scientists, Peter Chen and Pieter Abbeel. Its robots are powered by a technology called the Covariant Brain, a machine-learning (ML) model to train and improve robots’ functionality in real-world applications.

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Mlearning.ai

AUGUST 6, 2023

To start off my journey in Medium, I will write a post on something which many students and even relatives have asked me: “ How did I end up teaching data science when I had majored in Engineering ?” I also started on my data science journey by attending the Coursera specialization by Andrew Ng —  Deep Learning.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

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Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. ML models are however statistical in nature, which theoretically means that their average performance may be very different from the one during a specific training run.

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Towards AI

JULY 20, 2023

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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Applied Data Science

MARCH 11, 2022

Both of these computations have a complexity scaling in the cube of the data’s number of features. This explain this statement at the NeurIPS 2017 Test-of-Time Award: It seems easier to train a bi-directional LSTM with attention than to compute the PCA of a large matrix. — Rahimi

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Towards AI

APRIL 1, 2023

One of the core ideas behind ChatGPT dates back to a research paper from 2017. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. Join thousands of data leaders on the AI newsletter.

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AWS Machine Learning Blog

AUGUST 7, 2023

AWS ProServe solved this use case through a joint effort between the Generative AI Innovation Center (GAIIC) and the ProServe ML Delivery Team (MLDT). AWS received about 100 samples of labeled data from the customer, which is a lot less than the 1,000 samples recommended for fine-tuning an LLM in the data science community.

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Mlearning.ai

MAY 15, 2023

Photo by Andrew Neel on Unsplash Introduction If you are working or have worked on any data science task then you definitely used pandas. So, pandas is a library which helps with performing data ingestion and transformations. apply(lambda x: x.year) df.groupby('year')['Sales'].mean() Yearly average sales.

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DagsHub

DECEMBER 11, 2023

The following points illustrates some of the main reasons why data versioning is crucial to the success of any data science and machine learning project: Storage space One of the reasons of versioning data is to be able to keep track of multiple versions of the same data which obviously need to be stored as well.

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Towards AI

OCTOBER 19, 2024

Figure 3: A “beeswarm” plot from SHAP to examine the impact of different features on income from census data The Challenge of Modern AI Models While these XAI techniques work well for traditional ML models, modern AI systems like Large Language Models (LLMs) present new challenges. References [1] F. Doshi-Velez and B. 2] […]

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Advances in neural information processing systems 30 (2017).

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AWS Machine Learning Blog

MARCH 14, 2024

Beyond hardware, data cleaning and processing, model architecture design, hyperparameter tuning, and training pipeline development demand specialized machine learning (ML) skills. Launched in 2017, Amazon SageMaker is a fully managed service that makes it straightforward to build, train, and deploy ML models.

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Iguazio

SEPTEMBER 20, 2023

20 Newsgroups A dataset containing roughly 20,000 newsgroup documents spanning a variety of topics, for text classification, text clustering and similar ML applications. million articles from 20,000 news sources across a seven day period in 2017 and 2018. Long-Form Content 14. The newsgroups are: comp.graphics, comp.os.ms-windows.misc,

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Snorkel AI

MAY 24, 2023

The Future of Data-centric AI virtual conference will bring together a star-studded lineup of expert speakers from across the machine learning, artificial intelligence, and data science field. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. Patil served as the first U.S.

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Snorkel AI

MAY 24, 2023

The Future of Data-centric AI virtual conference will bring together a star-studded lineup of expert speakers from across the machine learning, artificial intelligence, and data science field. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. Patil served as the first U.S.

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Heartbeat

FEBRUARY 6, 2023

When you’re working on an enterprise scale, managing your ML models can be tricky. arXiv preprint arXiv:1701.06659 (2017). Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Flipboard

NOVEMBER 24, 2023

The humble beginnings with Iris In 2017, SnapLogic unveiled Iris, an industry-first AI-powered integration assistant. Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. He currently is working on Generative AI for data integration.

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Heartbeat

MARCH 14, 2023

photo from Data Science Central Industrial automation, security and surveillance, and service robots are just a few examples of fields that might benefit from robotics’ ability to identify and track objects. Additional resources: Adrian, R. Deep learning for computer vision with Python.

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Heartbeat

NOVEMBER 8, 2023

Comet, a cloud-based platform for managing machine learning experiments, was developed in 2017 by a team of data scientists and machine learning experts. It offers a range of features that make it easier for users to track and compare different models and ML experiments, such as experiment tracking and model production monitoring.

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Dataconomy

APRIL 4, 2024

In 2017, some researchers published a seminal paper called, “Attention is all you need.” He is the founder and co-chair of FWD50, the world’s leading conference on public sector innovation, and has served as a visiting executive at Harvard Business School, where he helped create the curriculum for Data Science and Critical Thinking.

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Heartbeat

DECEMBER 4, 2023

arXiv preprint arXiv:1704.04861 (2017). Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. “Mobilenets: Efficient convolutional neural networks for mobile vision applications.”

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Heartbeat

JULY 24, 2023

4] Data source: [link] Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. 5098, 2023. doi:10.21105/joss.05098 05098 [2] Fedorov, A., Johnson, J., Damaraju, E., link] [3] F.

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Mlearning.ai

JANUARY 27, 2023

You can use the below resources for creating your data. ★ Kaggle image datasets: Link Users of Kaggle may discover and share data sets, study and develop models in a web-based data science environment, and collaborate with other data scientists and computer vision experts.

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Heartbeat

JUNE 14, 2023

PMLR, 2017. [2] arXiv preprint arXiv:1710.09412 (2017). [7] Calibrating deep neural networks using focal loss.” [Cross Validated] Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Mlearning.ai

JUNE 7, 2023

Although it is not an ML Project, it is a very interesting project with lots of functionalities. We have the IPL data from 2008 to 2017. Working Video of our App [link] Conclusion End-to-end machine learning projects are a vital component of the data science journey. Working Video of our App [link] 7.

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Heartbeat

OCTOBER 24, 2023

Facebook and Microsoft created this tool in 2017 to make it simpler for academics and engineers to migrate models between various deep-learning frameworks and hardware platforms. Introduction to ONNX and its benefits, with an example Photo by drmakete lab on Unsplash What is ONNX?

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Mlearning.ai

JULY 2, 2023

Following these steps, I will preprocess the data and perform a multivariate forecasting study. Pixabay, Pexels Hello, today I am thrilled to finally sit in front of my computer and delve into an exciting data science project that I have been putting off for quite some time.

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Heartbeat

JANUARY 23, 2023

The goal is to provide a public database of ML research articles, source code, and assessment metrics. Anyone can help by getting their hands on the data, building their own model, and then testing it against others. The following code provide homepage, citation, info, license and description. print("1-",qqp["train"].homepage)

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Mlearning.ai

APRIL 10, 2023

With the emergence of data science and AI, clustering has allowed us to view data sets that are not easily detectable by the human eye. Thus, this type of task is very important for exploratory data analysis. Cluster Analysis in Data-Driven Management and Decisions. Electronics. 2023; 12(7):1689. link] Leilei, S.,

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Mlearning.ai

DECEMBER 21, 2023

ML models are mathematical models and therefore require numerical data. Splitting: This step involves splitting documents into smaller manageable chunks. Smaller chunks are easier to search and to use in LLM context windows. Embedding: This step involves converting text documents into numerical vectors. Precise Similarity Search.

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Ocean Protocol

MARCH 10, 2023

Ocean Foundations Ocean Protocol was launched in 2017 with a whitepaper and a promise: to create the building blocks and tools to unleash an open, permissionless and secure data economy. We shipped Ocean V1 to enable data sovereignty (“your keys, your data”). Thresher’s target users are data scientists.

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Heartbeat

AUGUST 2, 2023

Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. This architecture comprises two primary components: the encoder and the decoder.

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Heartbeat

JULY 20, 2023

When evaluated on the MS COCO dataset test-dev 2017, YOLOv8x attained an impressive average precision (AP) of 53.9% Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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Explosion

AUGUST 3, 2017

Data science projects are said to have uneven returns, like start-ups: a minority of projects are very successful, recouping costs for a larger number of failures. Rather than spending a month figuring out an unsupervised machine learning problem, just label some data for a week and train a classifier.

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