A 2019 Guide to Speech Synthesis with Deep Learning
KDnuggets
SEPTEMBER 9, 2019
In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.
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KDnuggets
SEPTEMBER 9, 2019
In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.
O'Reilly on Data
MARCH 19, 2020
Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.
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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications
Manufacturing Sustainability Surge: Your Guide to Data-Driven Energy Optimization & Decarbonization
From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
KDnuggets
DECEMBER 5, 2019
Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.
Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications
Manufacturing Sustainability Surge: Your Guide to Data-Driven Energy Optimization & Decarbonization
From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
Insight
MAY 14, 2020
In this post, I demonstrate how deep learning can be used to significantly improve upon earlier methods, with an emphasis on classifying short sequences as being human, viral, or bacterial. As I discovered, deep learning is a powerful tool for short sequence classification and is likely to be useful in many other applications as well.
KDnuggets
AUGUST 1, 2019
Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges.
Data Science 101
SEPTEMBER 24, 2019
Is the list missing a project released in 2019? Open Source Data Science Projects. If so, please leave a comment.
KDnuggets
OCTOBER 10, 2019
We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
Insight
SEPTEMBER 20, 2019
The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The loss function used is illustrated in the figure below, with “A” representing the ground truth (manually labeled mask) and “B” representing the model generated mask. Here, we built a model to mimic this process. on test data.
Dataiku
JUNE 26, 2020
In the last few years, we’ve seen a lot of breakthroughs in reinforcement learning (RL). From 2013 with the first deep learning model to successfully learn a policy directly from pixel input using reinforcement learning to the OpenAI Dexterity project in 2019, we live in an exciting moment in RL research.
Cloudera
MARCH 13, 2019
A Data Scientist : Organizations who show how they improved analytics, delivered new actionable intelligence, or designed systems for distributed deep learning and artificial intelligence to the organization’s business and customers. Stay tuned for March 19, 2019 as the winners are unveiled at the Luminaries dinner in Barcelona.
Smart Data Collective
MAY 2, 2019
Big data is vital for helping SEO companies identify and rectify inefficiencies in their models. There are a number of deep learning tools that evaluate social media activity. Local businesses need to rely heavily on SEO in 2019. Deep learning and other big data tools will be essential in the year is moving forward.
CIO Business Intelligence
APRIL 25, 2023
A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. Meanwhile, however, many other labs have been developing their own generative AI models.
CIO Business Intelligence
OCTOBER 4, 2023
We’ve been working on this for over a decade, including transformer-based deep learning,” says Shivananda. An example of the impact of AI can be seen from 2019 to 2022, when the company’s loss rate reduced by almost half, in part thanks to advances in algorithms and AI technology.
O'Reilly on Data
FEBRUARY 4, 2019
In this post, I’ll describe some of the key areas of interest and concern highlighted by respondents from Europe, while describing how some of these topics will be covered at the upcoming Strata Data conference in London (April 29 - May 2, 2019). Machine Learning model lifecycle management. Deep Learning.
CIO Business Intelligence
JANUARY 23, 2023
As OpenAI’s exclusive cloud provider it will see additional revenue for its Azure services, as one of OpenAI’s biggest costs is providing the computing capacity to train and run its AI models. The deal, announced by OpenAI and Microsoft on Jan. Additionally, it may not always be able to understand or respond to certain inputs correctly.”
KDnuggets
DECEMBER 2, 2019
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.
KDnuggets
JULY 24, 2019
Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.
Corinium
JUNE 6, 2019
Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation. What differentiates Fractal Analytics?
CIO Business Intelligence
JUNE 7, 2022
Those numbers represent the projected growth of chatbot interactions among banking customers between 2019 to 2023 and the cost savings from 862 hours less of work by support personnel, according to research by Juniper Research. In business, when a trend is forecast to grow by more than 3000% and generate cost savings of $7.3
TDAN
JUNE 2, 2020
With multiple technologies involved, even deep learning algorithms can’t do the trick. 2019 witnessed record-breaking AI funding, and it’s mostly possible because, over the years, decision making has […]. The domain of AI and data science so far has created significant value in the technological landscape.
bridgei2i
MAY 14, 2019
BANGALORE, May 14, 2019. BRIDGEi2i is pleased to host Alex Smola – VP & Distinguished Scientist at AWS for an informative and hands-on learning session on Computer Vision GluconCV & D2L.ai on 18th May 2019 at the BRIDGEi2i auditorium. For more details on the meetup, please click here. About Alex Smola.
KDnuggets
NOVEMBER 18, 2019
When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.
KDnuggets
NOVEMBER 13, 2019
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
CIO Business Intelligence
MAY 1, 2024
ChatGPT e le hallucination sui casi giudiziari I progressi compiuti nel 2023 dai large language models (LLM) hanno suscitato un interesse diffuso per il potenziale di trasformazione dell’IA generativa in quasi tutti i settori. “Anche quando è la tecnologia ad automatizzare la discriminazione, la responsabilità ricade sul datore di lavoro”.
KDnuggets
AUGUST 9, 2019
Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. The models we're talking about here might be taking you multiple days to train or even weeks or months. Who is this guide for? students, academics, etc.
KDnuggets
AUGUST 5, 2019
Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling.
O'Reilly on Data
MARCH 31, 2020
Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.
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.
KDnuggets
OCTOBER 14, 2019
Also: Activation maps for deep learning models in a few lines of code; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned; 10 Great Python Resources for Aspiring Data Scientists.
O'Reilly on Data
MARCH 20, 2019
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.
Cloudera
NOVEMBER 15, 2021
In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . It facilitates AI because, to be useful, many AI models require large amounts of data for training. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful.
FineReport
DECEMBER 19, 2019
If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Of course, other BI tools such as Power BI and Qlikview also have their own advantages. From Google.
CDW Research Hub
JULY 31, 2019
Recent advances in machine learning, and more specifically its subset, deep learning, have made it possible for computers to better understand natural language. These deep learning models can analyze large volumes of text and provide things like text summarization, language translation, context modeling, and sentiment analysis.
O'Reilly on Data
MARCH 18, 2020
The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. But what kind?
O'Reilly on Data
FEBRUARY 18, 2020
Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%. But sustained interest in cloud migrations—usage was up almost 10% in 2019, on top of 30% in 2018—gets at another important emerging trend. Still cloud-y, but with a possibility of migration.
Insight
AUGUST 17, 2020
Kim Vo (Coaching & Development Lead, Interview Strategy Team) and Emily Kearney (Program Director, Data Science) during the fall session, September 2019. Cameron built an app for streaming positive, relevant news for Black audiences by scraping and processing articles through Natural Language Processing (Google’s BERT model).
The Unofficial Google Data Science Blog
NOVEMBER 17, 2020
by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.
Domino Data Lab
APRIL 3, 2019
Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. Newer work in machine learning (e.g.,
KDnuggets
OCTOBER 25, 2019
Semiotics helps us understand the importance of context to determining the meaning of a term and discourse communities provide us with the background context (mental model) by which to correctly interpret its meaning correctly.
O'Reilly on Data
SEPTEMBER 11, 2019
Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 2 in frequency in proposal topics; a related term, “models,” is No.
DataKitchen
APRIL 13, 2021
DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. ModelOps and MLOps fall under the umbrella of DataOps,with a specific focus on the automation of data science model development and deployment workflows.
O'Reilly on Data
JUNE 27, 2019
Improvements in documentation, ease-of-use, and its production-ready implementation of key deep learning models, combined with speed, scalability, and accuracy has made Spark NLP a viable option for enterprises needing an NLP library. A three-part series on “Comparing production-grade NLP libraries”.
datapine
NOVEMBER 19, 2019
While we’ve seen traces of this in 2019, it’s in 2020 that computer vision will make a significant mark in both the consumer and business world. Already in our shortlist of tech buzzwords 2019, artificial intelligence is on the front scene for next year again. Artificial Intelligence (AI). Connected Retail. Hyperautomation.
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