datapine

MAY 14, 2019

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. “Machine Learning Yearning” by Andrew Ng.

Data Science 350

Data Science Machine Learning Big Data Data-driven 350

Rocket-Powered Data Science

DECEMBER 16, 2018

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data). What data do we have?

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Rocket-Powered Data Science

JULY 6, 2021

7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all. There was some research published earlier in 2020 that found that traditional, less complex algorithms can be nearly as good or better than deep learning on some tasks. And the goodness doesn’t stop there.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

DataRobot Blog

APRIL 21, 2022

Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time.

Deep Learning 145

Deep Learning Machine Learning Metrics Measurement 145

O'Reilly on Data

MARCH 31, 2020

Even with good training data and a clear objective metric, it can be difficult to reach accuracy levels sufficient to satisfy end users or upper management. That includes the ability to do your own analysis, to run SQL queries, to develop metrics, and to build dashboards. Is the product something that customers need?

Management 363

Management Machine Learning Experimentation Metrics 363

Cloudera

DECEMBER 4, 2024

System metrics, such as inference latency and throughput, are available as Prometheus metrics. Data teams can use any metrics dashboarding tool to monitor these. Users can deploy trained models, including GenAI models or predictive deep learning models, directly to the Cloudera AI Inference service.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. There needs to be a way to validate this against a given metric and validation set before deploying a model.

Modeling 257

Modeling Machine Learning Testing Metrics 257

KDnuggets

SEPTEMBER 27, 2023

10 ChatGPT Projects Cheat Sheet • Introduction to Deep Learning Libraries: PyTorch and Lightning AI • Top 5 Free Alternatives to GPT-4 • Machine Learning Evaluation Metrics: Theory and Overview • Kick Ass Midjourney Prompts with Poe

Deep Learning 123

Deep Learning Machine Learning Metrics 123

Corinium

JUNE 6, 2019

A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI. Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Domino Data Lab

APRIL 29, 2021

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.

Data Science 134

Data Science Deep Learning Modeling Forecasting 134

DataKitchen

APRIL 13, 2021

A complete DataOps program will have a unified, system-wide view of process metrics using a common data store. Metis Machine — Enterprise-scale Machine Learning and Deep Learning deployment and automation platform for rapid deployment of models into existing infrastructure and applications.

Testing 300

Testing Machine Learning Consulting Data Science 300

KDnuggets

OCTOBER 16, 2019

This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

Metrics 60

Metrics Deep Learning Machine Learning Modeling 60

Rocket-Powered Data Science

DECEMBER 15, 2018

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. In those intermediate steps it serves as an evaluation (or validation) metric. This challenge is known as the cold-start problem !

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

KDnuggets

DECEMBER 11, 2019

AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments and Key Trends; Down with technical debt! the most relevant Metrics in a Nutshell. Clean #Python for #DataScientists; Calculate Similarity?-?the

Deep Learning 54

Deep Learning Machine Learning Data Science Metrics 54

O'Reilly on Data

DECEMBER 12, 2019

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. For model training and selection, we recommend considering fairness metrics when selecting hyperparameters and decision cutoff thresholds.

Machine Learning 276

Machine Learning Modeling Testing Risk Management 276

DataRobot

FEBRUARY 10, 2021

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger is a hands-on guide that helps people with little math background understand and use deep learning quickly. Target leakage helped to explain the very low scores of the deep learning models.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

JANUARY 24, 2024

It’s the culmination of a decade of work on deep learning AI. Deep learning AI: A rising workhorse Deep learning AI uses the same neural network architecture as generative AI, but can’t understand context, write poems or create drawings. You probably know that ChatGPT wasn’t built overnight.

Deep Learning 112

Deep Learning Unstructured Data Machine Learning Modeling 112

CIO Business Intelligence

AUGUST 13, 2024

Unlike siloed or shallow automation efforts, deep automation architects a perspective that integrates customer experiences, value streams, human-machine collaboration, and synergistic technologies to create intelligent, self-adjusting businesses. Implement real-time dashboards to track performance across the organization.

Data-driven 130

Data-driven Metrics Optimization Machine Learning 130

Smart Data Collective

JANUARY 20, 2020

Most of these tools are powered by a specific Deep Learning engine which also assists in conversions, revenue generation, and better traffic generations. Deep Learning technologies are also in place to measure content performance and the existing trends which eventually make sure whether the existing content plan will work or not.

Deep Learning 128

Deep Learning ROI Data-driven Marketing 128

CIO Business Intelligence

JUNE 21, 2022

In addition to quantitative ROI metrics, HPC research was also shown to save lives, lead to important public/private partnerships, and spur innovations. . Real-time big data analytics, deep learning, and modeling and simulation are newer uses of HPC that governments are embracing for a variety of applications. HPC Growth in U.S.

Deep Learning 111

Deep Learning Big Data ROI Software 111

Sisense

APRIL 10, 2020

There are many performance metrics to evaluate performance of Machine Learning models. This metric can be used in classification analyses to identify a model’s ability to predict a desired attribute, based on the training data. Nowadays text data is huge, so Deep Learning also comes into the picture.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. With frameworks like Tensorflow , Keras , Pytorch, etc.,

Visualization 97

Visualization Deep Learning Machine Learning Modeling 97

CIO Business Intelligence

AUGUST 15, 2024

A virtual assistant may save employees time when searching for old documents or composing emails, but most organizations have no idea how much time those tasks have taken historically, having never tracked such metrics before, she says. There are a lot of cool AI solutions that are cheaper than generative AI,” Stephenson says.

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

Smart Data Collective

FEBRUARY 14, 2021

Outline Your Product with Deep Learning Modeling. Deep learning tools can make it easier to model these products. It will become even easier with deep learning algorithms at your fingertips. There are a lot of metrics that need to be tracked with data analytics tools.

Data-driven 107

Data-driven Big Data Deep Learning Data mining 107

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

More knots make the learned feature transformation smoother and more capable of approximating any monotonic function. As a result, selecting knots according to the quantiles of the input data (or even linearly across the domain), and then steadily increasing their number as long as the metrics improve works well in practice.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Smart Data Collective

DECEMBER 21, 2020

At present, insurers use AI to assess individuals’ risk using quite generalized metrics, often based on their age, location, and gender. This is significant because each piece of data input into a system supports the deep learning of AI and the generation of insights. More accurate policy pricing.

Insurance 142

Insurance Cost-Benefit Big Data Unstructured Data 142

Smart Data Collective

JULY 1, 2023

The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning. Consistency and agreement Establish an agreement metric (e.g., This will reduce inconsistencies and errors in annotations.

Machine Learning 93

Machine Learning Metrics Uncertainty Deep Learning 93

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. This view actually delivers four out of the five efficiency metrics that we discussed in the previous blog post.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Cloudera

FEBRUARY 25, 2021

Here’s a preview of what you can leverage with one click in CML: Deep Learning for Anomaly Detection. Apply modern, deep learning techniques for anomaly detection to identify network intrusions. Deep Learning for Image Analysis. Build a semantic search application with deep learning models.

Deep Learning 84

Deep Learning Machine Learning Visualization Forecasting 84

Cloudera

OCTOBER 5, 2020

Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds. CDP enables enterprise customers to leverage Apache Spark 3.0

Enterprise 93

Enterprise Machine Learning Deep Learning Data Science 93

CIO Business Intelligence

JUNE 1, 2022

Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

Domino Data Lab

SEPTEMBER 9, 2019

For example, with those open source licenses we can download their text, parse, then compare similarity metrics among them: In [12]: pairs = [?. ["mit", "asl"],?. ["asl", "bsd"],?. ["bsd", "mit"] ?]? ?for deep learning on edge devices. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

IBM Big Data Hub

NOVEMBER 29, 2023

Furthermore, as modeling techniques become increasingly sophisticated in data science, including deep learning and predictive and generative models, companies and vendors must work diligently to prevent unintentional connections that could leak a person’s identity and expose them to third-party attacks.

Metrics 80

Metrics Machine Learning Statistics Risk 80

Sisense

AUGUST 6, 2020

An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Today, we’re developing AI in an era where data is treated as code, or at least as an extension of code, because the code alone cannot achieve deep learning without the data.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

CIO Business Intelligence

APRIL 26, 2022

For personnel, cameras look for personal protective equipment (PPE) use, such as hard hats and safety glasses, and then the system either sends alerts to a manager if PPE is not being worn or keeps track of metrics that a safety officer uses to determine whether training is needed. How can we impact manufacturing revenue? .

Manufacturing 98

Manufacturing Optimization Metrics Deep Learning 98

Business Over Broadway

DECEMBER 20, 2018

Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Second, the calculation of the metric (a difference score) results in an ambiguous score that is difficult to interpret. First, the “research” behind the NPS claims is flawed.

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

Cloudera

SEPTEMBER 20, 2022

The state of the art in AI systems for artistic tasks almost universally use deep-learning models, which presuppose a significant amount of compute resources both to create them, and once created to continue to use them for producing images. Access — who can use it? Data — where does it come from? Misuse — what could go wrong?

Deep Learning 88

Deep Learning Cost-Benefit Risk Data-driven 88