🔹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

🔻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

🔻🔻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

🔹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

🔹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

🔻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

🔻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

🔹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

🔻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

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

🔹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

🔻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

🔻Top 10 Statistics Mistakes Made by Data Scientists

🔹by Norman Niemer

The following are some of the most common statistics mistakes made by data scientists. Check this list often to make sure you are not making any of these while applying statistics to data science.

1. Not fully understanding the objective function

2. Not having a hypothesis on why something should work

3. Not looking at the data before interpreting results

4. Not having a naive baseline model

5. Incorrect out-sample testing

6. Incorrect out-sample testing: applying preprocessing to full dataset

7. Incorrect out-sample testing: cross-sectional data & panel data

8. Not considering which data is available at point of decision

9. Subtle Overtraining

10. "need more data" fallacy
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2019/06/statistics-mistakes-data-scientists.html

#datascience
#machinelearning
#statistics
#github

Gift will allow MIT researchers to use artificial intelligence in a biomedical device

🔹by Maria Iacobo

Researchers in the MIT Department of Civil and Environmental Engineering (CEE) have received a gift to advance their work on a device designed to position living cells for growing human organs using acoustic waves. The Acoustofluidic Device Design with Deep Learning is being supported by Natick, Massachusetts-based MathWorks, a leading developer of mathematical computing software.
“One of the fundamental problems in growing cells is how to move and position them without damage,” says John R. Williams, a professor in CEE. “The devices we’ve designed are like acoustic tweezers.”
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📌Via: @cedeeplearning

http://news.mit.edu/2020/gift-to-mit-cee-artificial-intelligence-biomedical-device-0129

#deeplearning
#MIT #math
#machinelearning
#AI #datascience
#biomedical

Hi guys 👋🏿

From today we’ll be uploading “Introduction to Deep Learning” course by prof. Andrew Ng (Stanford lecturer and cofounder of coursera, deeplearning ai etc.)

🔹Make sure to send this awesome course to your friends.

If you have any suggestion or need a different course, don't hesitate to tell me: @pudax
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📌 @cedeeplearning
📌 Other social media: https://linktr.ee/cedeeplearning

⚪️ Introduction to Deep Learning by Andrew Ng

Source: Coursera

Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)

🔖 Welcome (Deep Learning Specialization)
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python

🔻HOW TO SOLVE 90% OF NLP PROBLEMS: A STEP-BY-STEP GUIDE

🔹by Emmanuel Ameisen

Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (#NLP).
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📌Via: @cedeeplearning

https://www.topbots.com/solve-ai-nlp-problems-guide/

#deeplearning
#neuralnetworks
#machinelearning
#text_data
#datascience

8 Ways Artificial Intelligence Takes Publishing to the Next Level | Hacker Noon

https://hackernoon.com/8-ways-artificial-intelligence-takes-publishing-to-the-next-level-rvnk3z3m

📌Via: @cedeeplearning
other social media: https://linktr.ee/cedeeplearning

https://www.slashgear.com/breakthrough-electronic-cells-could-learn-like-a-human-brain-20617405/

📌Via: @cedeeplearning
other social media: https://linktr.ee/cedeeplearning

🔹Google leverages computer vison to enhance the performance of robot manipulation

by Priya Dialani

The possibility that robots can figure out how to directly see the affordances of actions on objects (i.e., what the robot can or can’t do with an item) is called affordance-based manipulation, explored in research on learning complex vision-based manipulation skills including grasping, pushing, and tossing. In these #frameworks, affordances are represented as thick pixel-wise action-value maps that gauge how great it is for the #robot to execute one of a few predefined movements in every area.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/google-leverages-computer-vision-enhance-performance-robot-manipulation/

#computervision
#deeplearning
#neuralnetworks
#machinelearning