🔻GOOGLE LEVERAGES MACHINE LEARNING TO IMPROVE DOCUMENT DETECTION CAPABILITIES
Google has been employing a new scanner that uses machine learning to improve detection. Since the scanner launched, Google has boosted the detection of Office documents by 10%. Impressively, Google’s new scanner is getting better at detecting “adversarial, bursty attacks” with the detection rate jumping by 150%.
Interestingly, Google says that 58% of all malware targeting Gmail users comes from malicious documents, the vast majority of that coming from Office documents alone.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/google-leverages-machine-learning-to-improve-document-detection-capabilities/
#AI
#cybersecurity
#machinelearning
#google
#datascience
Google has been employing a new scanner that uses machine learning to improve detection. Since the scanner launched, Google has boosted the detection of Office documents by 10%. Impressively, Google’s new scanner is getting better at detecting “adversarial, bursty attacks” with the detection rate jumping by 150%.
Interestingly, Google says that 58% of all malware targeting Gmail users comes from malicious documents, the vast majority of that coming from Office documents alone.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/google-leverages-machine-learning-to-improve-document-detection-capabilities/
#AI
#cybersecurity
#machinelearning
#datascience
Analytics Insight
Google Leverages Machine Learning to Improve Document Detection Capabilities
Google has been employing a new scanner that uses machine learning to improve spam detection. Since the scanner launched, Google has boosted the detection of Office documents by 10%. Impressively, Google’s new scanner is getting better at detecting “adversarial…
🔻AI app can detect coronavirus from sound of cough
🔹The app has a 70% accuracy rate.
Researchers have developed a new app that uses artificial intelligence technology to determine whether a person has COVID-19 based on the sound of their cough. The app has a 70% accuracy rate.
Source: EPFL
you can record your cough on a smartphone and find out whether you might have COVID-19. So how can a smartphone app detect the new coronavirus? “According to the World Health Organization, 67.7% of people who have the virus present with a dry cough – producing no mucus – as opposed to the wet cough typical of a cold or allergy,” says David Atienza, a professor at EPFL’s School of Engineering who is also the head of ESL and a member of the Coughvid development team.
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📌Via: @cedeeplearning
https://neurosciencenews.com/ai-cough-coronavirus-16145/
#deeplearning
#neuralnetworks
#AI
#machinelearning
#accuracy
🔹The app has a 70% accuracy rate.
Researchers have developed a new app that uses artificial intelligence technology to determine whether a person has COVID-19 based on the sound of their cough. The app has a 70% accuracy rate.
Source: EPFL
you can record your cough on a smartphone and find out whether you might have COVID-19. So how can a smartphone app detect the new coronavirus? “According to the World Health Organization, 67.7% of people who have the virus present with a dry cough – producing no mucus – as opposed to the wet cough typical of a cold or allergy,” says David Atienza, a professor at EPFL’s School of Engineering who is also the head of ESL and a member of the Coughvid development team.
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📌Via: @cedeeplearning
https://neurosciencenews.com/ai-cough-coronavirus-16145/
#deeplearning
#neuralnetworks
#AI
#machinelearning
#accuracy
Neuroscience News
AI app can detect coronavirus from sound of cough - Neuroscience News
Researchers have developed a new app that uses artificial intelligence technology to determine whether a person has COVID-19 based on the sound of their cough. The app has a 70% accuracy rate.
🔹Auto-Regressive Generative Models (#PixelRNN, #PixelCNN++)
Authors : Harshit Sharma, Saurabh Mishra
The basic difference between Generative Adversarial Networks (GANs) and Auto-regressive models is that GANs learn implicit data distribution whereas the latter learns an explicit distribution governed by a prior imposed by model structure.
Some of the advantages of Auto-regressive models over GANs are:
1. Provides a way to calculate likelihood
2. The training is more stable than GANs
3. It works for both discrete and continuous data
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📌Via: @cedeeplearning
https://towardsdatascience.com/auto-regressive-generative-models-pixelrnn-pixelcnn-32d192911173
#neuralnetworks
#deeplearning
#cnn
#rnn
#GANs
Authors : Harshit Sharma, Saurabh Mishra
The basic difference between Generative Adversarial Networks (GANs) and Auto-regressive models is that GANs learn implicit data distribution whereas the latter learns an explicit distribution governed by a prior imposed by model structure.
Some of the advantages of Auto-regressive models over GANs are:
1. Provides a way to calculate likelihood
2. The training is more stable than GANs
3. It works for both discrete and continuous data
————————————
📌Via: @cedeeplearning
https://towardsdatascience.com/auto-regressive-generative-models-pixelrnn-pixelcnn-32d192911173
#neuralnetworks
#deeplearning
#cnn
#rnn
#GANs
Medium
Auto-Regressive Generative Models (PixelRNN, PixelCNN++)
Authors : Harshit Sharma, Saurabh Mishra
Glia are turning out to be central to many neurological functions, including pain perception
https://www.quantamagazine.org/glial-brain-cells-long-in-neurons-shadow-reveal-hidden-powers-20200127/
Via: 📌@cedeeplearning
Other social media: https://linktr.ee/cedeeplearning
https://www.quantamagazine.org/glial-brain-cells-long-in-neurons-shadow-reveal-hidden-powers-20200127/
Via: 📌@cedeeplearning
Other social media: https://linktr.ee/cedeeplearning
Quanta Magazine
Glial Brain Cells, Long in Neurons’ Shadow, Reveal Hidden Powers
The glial cells of the nervous system have been eclipsed in importance by neurons for decades. But glia are turning out to be central to many neurological functions, including pain perception.
🔹Conditional Image Generation with PixelCNN Decoders
By Francisco Ingham
This paper explores the potential for conditional image modelling by adapting and improving a convolutional variant of the PixelRNN architecture. Pixel-CNN can be conditioned on a vector to generate similar images. This vector can be either a series of labels representing ImageNet categories or an embedding produced by a convolutional network trained on face images.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://medium.com/a-paper-a-day-will-have-you-screaming-hurray/day-5-conditional-image-generation-with-pixelcnn-decoders-a8fc68b103a2
#deeplearning
#neuralnetworks
#machinelearning
#cnn
#pixelcnn
#pixelrnn
By Francisco Ingham
This paper explores the potential for conditional image modelling by adapting and improving a convolutional variant of the PixelRNN architecture. Pixel-CNN can be conditioned on a vector to generate similar images. This vector can be either a series of labels representing ImageNet categories or an embedding produced by a convolutional network trained on face images.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://medium.com/a-paper-a-day-will-have-you-screaming-hurray/day-5-conditional-image-generation-with-pixelcnn-decoders-a8fc68b103a2
#deeplearning
#neuralnetworks
#machinelearning
#cnn
#pixelcnn
#pixelrnn
🧠Army develops big data approach to neuroscience - Neuroscience News
https://neurosciencenews-com.cdn.ampproject.org/c/s/neurosciencenews.com/big-data-neuroscience-15626/amp/
Via: 📌@cedeeplearning
Other social media: https://linktr.ee/cedeeplearning
https://neurosciencenews-com.cdn.ampproject.org/c/s/neurosciencenews.com/big-data-neuroscience-15626/amp/
Via: 📌@cedeeplearning
Other social media: https://linktr.ee/cedeeplearning
Neuroscience News
Army develops big data approach to neuroscience - Neuroscience News
Big data study combines information from a diverse set of experiements to identify patterns of brain activity common across people and tasks.
🔻Intro to TensorFlow for Deep Learning (free course from Udacity)
Learn how to build deep learning applications with TensorFlow. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your #TensorFlow w models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.
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📌Via: @cedeeplearning
https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
#free #machinelearning
#datascience #math
#deeplearning #udacity
Learn how to build deep learning applications with TensorFlow. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your #TensorFlow w models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.
——————————————
📌Via: @cedeeplearning
https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
#free #machinelearning
#datascience #math
#deeplearning #udacity
Udacity
TensorFlow for Deep Learning Training Course | Udacity
Learn online and advance your career with courses in programming, data science, artificial intelligence, digital marketing, and more. Gain in-demand technical skills. Join today!
🔹HOW DEEPMIND STRENGTHENS ECOLOGICAL RESEARCH USING MACHINE LEARNING?
According to a DeepMind’s blog, the Serengeti-Mara ecosystem is globally unparalleled in its biodiversity, hosting an estimated 70 large mammal species and 500 bird species, thanks in part to its unique geology and varied habitat types. Around 10 years ago, the Serengeti Lion Research program installed hundreds of motion-sensitive cameras within the core of the protected area which is triggered by passing wildlife, capturing animal images frequently, across vast spatial scales, allowing researchers to study animal behavior, distribution, and demography with great spatial and temporal resolution.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/deepmind-strengthens-ecological-research-using-machine-learning/
#deepmind #deeplearning
#machinelearning #math
#datascience #neuralnetworks
According to a DeepMind’s blog, the Serengeti-Mara ecosystem is globally unparalleled in its biodiversity, hosting an estimated 70 large mammal species and 500 bird species, thanks in part to its unique geology and varied habitat types. Around 10 years ago, the Serengeti Lion Research program installed hundreds of motion-sensitive cameras within the core of the protected area which is triggered by passing wildlife, capturing animal images frequently, across vast spatial scales, allowing researchers to study animal behavior, distribution, and demography with great spatial and temporal resolution.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/deepmind-strengthens-ecological-research-using-machine-learning/
#deepmind #deeplearning
#machinelearning #math
#datascience #neuralnetworks
Analytics Insight
How DeepMind Strengthens Ecological Research Using Machine Learning?
Google’s DeepMind has collaborated with ecologists and conservationists to develop machine learning methods to help study the behavioral dynamics of an entire African animal community in the Serengeti National Park and Grumeti Reserve in Tanzania.
🔹FROM DETECTING CANCER TO SURGICAL HYPERTENSION, MACHINE LEARNING IS POWERFUL
by Priya Dialani
Machine learning models could furnish doctors and masters with data that will help prevent readmissions or other treatment options, or help forestall things like delirium, current areas of active improvement. Notwithstanding blood pressure, machine learning could locate an extraordinary use in the ICU, in predicting sepsis, which is critical for patient survival. Having the option to process that data in the ICU or in the emergency department, that would be a critical zone to utilize these machine learning analytics models.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/from-detecting-cancer-to-surgical-hypertension-machine-learning-is-powerful/
#machinelearning
#deeplearning
#datascience #healthcare
#neuralnetworks
#imagedetection
#computervision
by Priya Dialani
Machine learning models could furnish doctors and masters with data that will help prevent readmissions or other treatment options, or help forestall things like delirium, current areas of active improvement. Notwithstanding blood pressure, machine learning could locate an extraordinary use in the ICU, in predicting sepsis, which is critical for patient survival. Having the option to process that data in the ICU or in the emergency department, that would be a critical zone to utilize these machine learning analytics models.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/from-detecting-cancer-to-surgical-hypertension-machine-learning-is-powerful/
#machinelearning
#deeplearning
#datascience #healthcare
#neuralnetworks
#imagedetection
#computervision
Analytics Insight
From Detecting Cancer to Surgical Hypertension, Machine Learning is Powerful
Artificial intelligence and Machine Learning is getting progressively sophisticated at doing what people do, more rapidly and at a lower cost. The potential for both AI and robotics in healthcare is tremendous. AI and robotics are progressively a part of…
🔻Reducing risk, empowering resilience to disruptive global change
by Mark Dwortzan
The MIT Joint Program on the Science of Global Change launched in 2019 its Adaptation-at-Scale initiative (AS-MIT), which seeks evidence-based solutions to global change-driven risks. Using its Integrated Global System Modeling (IGSM) framework, as well as a suite of resource and infrastructure assessment models, AS-MIT targets, diagnoses, and projects changing risks to life-sustaining resources under impending societal and environmental stressors, and evaluates the effectiveness of potential risk-reduction measures.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: http://news.mit.edu/2020/reducing-risk-empowering-resilience-disruptive-global-change-0123
#deeplearning #math
#neuralnetworks
#machinelearning
#datascience
#globalchange
#MIT #research
by Mark Dwortzan
The MIT Joint Program on the Science of Global Change launched in 2019 its Adaptation-at-Scale initiative (AS-MIT), which seeks evidence-based solutions to global change-driven risks. Using its Integrated Global System Modeling (IGSM) framework, as well as a suite of resource and infrastructure assessment models, AS-MIT targets, diagnoses, and projects changing risks to life-sustaining resources under impending societal and environmental stressors, and evaluates the effectiveness of potential risk-reduction measures.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: http://news.mit.edu/2020/reducing-risk-empowering-resilience-disruptive-global-change-0123
#deeplearning #math
#neuralnetworks
#machinelearning
#datascience
#globalchange
#MIT #research
🔻🔻Using AI to predict breast cancer and personalize care
MIT/MGH's image-based deep learning model can predict breast cancer up to five years in advance.
A team from MIT’s #Computer_Science and #Artificial_Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future. Trained on mammograms and known outcomes from over 60,000 MGH patients, the model learned the subtle patterns in breast tissue that are precursors to malignant tumors.
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📌Via: @cedeeplearning
http://news.mit.edu/2019/using-ai-predict-breast-cancer-and-personalize-care-0507
#deeplearning
#neuralnetworks
#machinelearning
#datascience
#MIT #math
#prediction
#computervision
MIT/MGH's image-based deep learning model can predict breast cancer up to five years in advance.
A team from MIT’s #Computer_Science and #Artificial_Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future. Trained on mammograms and known outcomes from over 60,000 MGH patients, the model learned the subtle patterns in breast tissue that are precursors to malignant tumors.
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📌Via: @cedeeplearning
http://news.mit.edu/2019/using-ai-predict-breast-cancer-and-personalize-care-0507
#deeplearning
#neuralnetworks
#machinelearning
#datascience
#MIT #math
#prediction
#computervision
MIT News
Using AI to predict breast cancer and personalize care
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital has created a deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years…
🔹Deep Learning With Apache Spark: Part 1
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
By Favio Vazquez,
Spark, defined by its creators is a fast and general engine for large-scale data processing.
The fast part means that it’s faster than previous approaches to work with Big Data like classical MapReduce. The secret for being faster is that Spark runs on Memory (RAM), and that makes the processing much faster than on Disk.
The general part means that it can be use for multiple things, like running distributed SQL, create data pipelines, ingest data into a database, run Machine Learning algorithms, work with graphs, data streams and much more.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/04/deep-learning-apache-spark-part-1.html
#apachespark
#databricks
#deeplearning
#pipeline
#machinelearning
#datasicence
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
By Favio Vazquez,
Spark, defined by its creators is a fast and general engine for large-scale data processing.
The fast part means that it’s faster than previous approaches to work with Big Data like classical MapReduce. The secret for being faster is that Spark runs on Memory (RAM), and that makes the processing much faster than on Disk.
The general part means that it can be use for multiple things, like running distributed SQL, create data pipelines, ingest data into a database, run Machine Learning algorithms, work with graphs, data streams and much more.
—————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/04/deep-learning-apache-spark-part-1.html
#apachespark
#databricks
#deeplearning
#pipeline
#machinelearning
#datasicence
🔹Deep Learning With Apache Spark: Part 2
In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
By Favio Vazquez
The continuous improvements on Apache Spark lead us to this discussion on how to do Deep Learning with it. I created a detailed timeline of the development of Apache Spark until now to see how we got here.
Deep Learning Pipelines is an open source library created by Databricks that provides high-level APIs for scalable deep learning in Python with Apache Spark.
Deep Learning Pipelines builds on Apache Spark’s ML Pipelines for training, and with Spark DataFrames and SQL for deploying models. It includes high-level APIs for common aspects of deep learning so they can be done efficiently in a few lines of code.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/05/deep-learning-apache-spark-part-2.html
#apachespark
In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
By Favio Vazquez
The continuous improvements on Apache Spark lead us to this discussion on how to do Deep Learning with it. I created a detailed timeline of the development of Apache Spark until now to see how we got here.
Deep Learning Pipelines is an open source library created by Databricks that provides high-level APIs for scalable deep learning in Python with Apache Spark.
Deep Learning Pipelines builds on Apache Spark’s ML Pipelines for training, and with Spark DataFrames and SQL for deploying models. It includes high-level APIs for common aspects of deep learning so they can be done efficiently in a few lines of code.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/05/deep-learning-apache-spark-part-2.html
#apachespark
🔻Top 8 Python Machine Learning Libraries
Part 1 of a new series investigating the top #Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
1. scikit-learn
2. Keras
3. XGBoost
4. StatsModels
5. LighGBM
6. CatBoost
7. PyBrain
8. Eli5
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/10/top-python-machine-learning-libraries.html
#deeplearning
#machinelearning
#libraries
#datascience
Part 1 of a new series investigating the top #Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
1. scikit-learn
2. Keras
3. XGBoost
4. StatsModels
5. LighGBM
6. CatBoost
7. PyBrain
8. Eli5
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/10/top-python-machine-learning-libraries.html
#deeplearning
#machinelearning
#libraries
#datascience
🔻Top 13 Python Deep Learning Libraries
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
Of course, these lists are entirely subjective as many libraries could easily place in multiple categories. For example, TensorFlow is included in this list but Keras has been omitted and features in the Machine Learning library collection instead. This is because #Keras is more of an ‘end-user’ library like #SKLearn, as opposed to #TensorFlow which appeals more to researchers and Machine Learning engineer types.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/11/top-python-deep-learning-libraries.html
#machinelearning
#deeplearning
#datascience
#paython
#libraries
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
Of course, these lists are entirely subjective as many libraries could easily place in multiple categories. For example, TensorFlow is included in this list but Keras has been omitted and features in the Machine Learning library collection instead. This is because #Keras is more of an ‘end-user’ library like #SKLearn, as opposed to #TensorFlow which appeals more to researchers and Machine Learning engineer types.
————————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.kdnuggets.com/2018/11/top-python-deep-learning-libraries.html
#machinelearning
#deeplearning
#datascience
#paython
#libraries
🔹DISCOVER THE RISING TRENDS IN COGNITIVE SYSTEMS AND COMPUTING
by Smriti Srivastava
1. Cognitive system investments
2. Cognitive system in healthcare
3. Cognitive system in travel
4. Cognitive system in fitness
5. Cognitive system in transportation
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/discover-rising-trends-cognitive-computing-systems/
#deeplearning
#computervision
#cognitive
#trend
by Smriti Srivastava
1. Cognitive system investments
2. Cognitive system in healthcare
3. Cognitive system in travel
4. Cognitive system in fitness
5. Cognitive system in transportation
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/discover-rising-trends-cognitive-computing-systems/
#deeplearning
#computervision
#cognitive
#trend
Analytics Insight
Discover The Rising Trends in Cognitive Systems and Computing
The advents of cognitive systems are at the rise in the healthcare and wellness industry. Imagine a medical assistant that could synthesize and interpret medical research, studies, and textbooks and provide people with personalized care suggestions.
🔻BEST DATA PREPARATION TOOLS TO LOOK OUT FOR IN 2020
Businesses need to map data from different sources in order to get better insights. This process of mapping data is what we call data preparation. Therefore, we have brought you the top 10 Data Preparation tools to look out for in 2020:
1. Altair Monarch
2. Microsoft Power BI
3. Alteryx
4. Tableau Prep
5. Paxata
6. Trifaca
7. TMMData
8. TIBCO Software
9. SAP
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.analyticsinsight.net/best-data-preparation-tools-to-look-out-for-in-2020/
#datatools
#datascience
#powerbi
#preparation
#insight
#bigdata
Businesses need to map data from different sources in order to get better insights. This process of mapping data is what we call data preparation. Therefore, we have brought you the top 10 Data Preparation tools to look out for in 2020:
1. Altair Monarch
2. Microsoft Power BI
3. Alteryx
4. Tableau Prep
5. Paxata
6. Trifaca
7. TMMData
8. TIBCO Software
9. SAP
——————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.analyticsinsight.net/best-data-preparation-tools-to-look-out-for-in-2020/
#datatools
#datascience
#powerbi
#preparation
#insight
#bigdata
INTEL EYES Deep Learning with VERTEX.AI acquisition for its Movidius unit
by Kamalika Some
#Intel has big plans to leverage artificial intelligence technology into all aspects of its business. The #computer_processing giant has acquired Vertex.AI, a Seattle-based start-up founded in 2015 focused to develop deep learning for every #platform for its Movidius unit. The seven-member team behind Vertex.AI including the founders Choong Ng, Jeremy Bruestle and Brian Retford are all set to join Intel Movidius team into its Artificial Intelligence Products Group.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/intel-eyes-deep-learning-with-vertex-ai-acquisition-for-its-movidius-unit/
#deeplearning
#neuralnetworks
#AI #machinelearning
by Kamalika Some
#Intel has big plans to leverage artificial intelligence technology into all aspects of its business. The #computer_processing giant has acquired Vertex.AI, a Seattle-based start-up founded in 2015 focused to develop deep learning for every #platform for its Movidius unit. The seven-member team behind Vertex.AI including the founders Choong Ng, Jeremy Bruestle and Brian Retford are all set to join Intel Movidius team into its Artificial Intelligence Products Group.
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📌Via: @cedeeplearning
https://www.analyticsinsight.net/intel-eyes-deep-learning-with-vertex-ai-acquisition-for-its-movidius-unit/
#deeplearning
#neuralnetworks
#AI #machinelearning
Analytics Insight
Intel Eyes Deep Learning with Vertex.AI Acquisition for its Movidius Unit | Analytics Insight
Intel has big plans to leverage artificial intelligence technology into all aspects of its business. It is going strong over acquisitions on AI tech companies including Mighty AI, DataRobot, and Lumiata. Recently, Intel has acquired Vertex.AI focused to develop…
🔹Hot trend in Artificial Intelligence - Deep Learning
The term “deep learning” involves the application of artificial neural networks to carry out advanced pattern recognition. Once trained, these algorithms are applied on to fresh data to draw insights.
According to a report from McKinsey Global Institute, a company could hope to gain 1 to 9 percent of its revenues through the application of deep learning depending on the industry the algorithms are deployed in.
🔻Business Potential in Deep Learning
Most of the business potential in deep learning would emerge from two broad domains: marketing and sales, and supply chains and manufacturing.
🔻Impediments to Deep Learning
On the road to deep learning, there are plenty of stumbling blocks. The biggest obstacles involve data, starting with how to collect, clean and label it that makes them practical for training machine learning systems.
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📌Via: @cedeeplearning
link: https://www.analyticsinsight.net/hot-trend-in-artificial-intelligence-deep-learning/
The term “deep learning” involves the application of artificial neural networks to carry out advanced pattern recognition. Once trained, these algorithms are applied on to fresh data to draw insights.
According to a report from McKinsey Global Institute, a company could hope to gain 1 to 9 percent of its revenues through the application of deep learning depending on the industry the algorithms are deployed in.
🔻Business Potential in Deep Learning
Most of the business potential in deep learning would emerge from two broad domains: marketing and sales, and supply chains and manufacturing.
🔻Impediments to Deep Learning
On the road to deep learning, there are plenty of stumbling blocks. The biggest obstacles involve data, starting with how to collect, clean and label it that makes them practical for training machine learning systems.
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📌Via: @cedeeplearning
link: https://www.analyticsinsight.net/hot-trend-in-artificial-intelligence-deep-learning/
🔻How AI will transform healthcare (and can it fix the US healthcare system?)
by Imtiaz Adam
This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.
👇🏻Read the entire article through the link below
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📌Via: @cedeeplearning
https://www.kdnuggets.com/2019/09/ai-transform-healthcare.html
#AI #healthcare
#quantom_computing
#startups #deeplearning
#machinelearning
#datascience
by Imtiaz Adam
This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.
👇🏻Read the entire article through the link below
——————————
📌Via: @cedeeplearning
https://www.kdnuggets.com/2019/09/ai-transform-healthcare.html
#AI #healthcare
#quantom_computing
#startups #deeplearning
#machinelearning
#datascience
KDnuggets
How AI will transform healthcare (and can it fix the US healthcare system?) - KDnuggets
This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.
🔻How (not) to use Machine Learning for time series forecasting: The sequel
by Vegard Flovik
Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.
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📌Via: @cedeeplearning
https://www.kdnuggets.com/2020/03/machine-learning-time-series-forecasting-sequel.html
#deeplearning
#machinelearning
#timeseries
#prediction
by Vegard Flovik
Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.
———————————
📌Via: @cedeeplearning
https://www.kdnuggets.com/2020/03/machine-learning-time-series-forecasting-sequel.html
#deeplearning
#machinelearning
#timeseries
#prediction
KDnuggets
How (not) to use Machine Learning for time series forecasting: The sequel - KDnuggets
Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something…