🔻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

🔹Facial recognition in retail banking and IP surveillance
by Priya Dialani

In the last decade, we have seen an increase in the utilization of innovation in numerous business segments to improve and better connect with customers. This is particularly valid in the banking and finance division. Since the beginning of the #digital_revolution facial recognition has been picking up prominence over touch and type based interactions because of the convenience it offers without settling on the security of transactions. #Facial_recognition is one of the various ways banks can diminish friction in customers’ experience and increase productivity and availability.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.analyticsinsight.net/facial-recognition-in-retail-banking-and-ip-surveillance/

#imagerecognition
#facerecognition
#deeplearning
#AI #math #datascience

🔹Data Engineer VS Data Science

🔻The reign of data is upon us
this quote is something management consultants preach to their corporate customers. many looking to exploit their data. But a good first step to do this would be up front investment is data engineering.

🔻Who are Data Engineers?
Data engineers typically are responsible for #processing raw data and extracting that from source systems. They also build the #ingestion_layer, and the #infrastructure to process and enrich the data.

🔻Data Engineers and the business
They should have the technical chops but also be able to work directly on product teams, or Scrums alongside of subject matter experts and data scientists.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://analyticsnomad.com/data-engineering-vs-data-science/

#datascience
#machinelearning
#AI #dataengineer
#datascienctist

🔻Skills Needed to Become a Data Scientist (Learn, Grasp, Implement)

By DATAFLAIR TEAM

A data scientist is better statistician than any software engineer and better engineer as compared to any statistician. A data scientist is termed to be the “sexiest job of the 21st century.

🔻Do not miss out this article !
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://data-flair.training/blogs/skills-needed-to-become-a-data-scientist/

#datascience
#datascientist
#skill #python #math
#machinelearning

⚪️ Introduction to Neural Networks by Andrew Ng

🔹Source: Coursera

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

🔖 Lecture 0 About This Course
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

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

🔻Is Deep Learning Overhyped?

With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
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📌via: @cedeeplearning

https://www.kdnuggets.com/2016/01/deep-learning-overhyped.html

#deeplearning
#machinelearning
#hype #neuralnetworks
#Yoshua_Bengio

🔻Why Deep Learning is Radically Different From Machine Learning

🖊By Carlos Perez

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2016/12/deep-learning-radically-different-machine-learning.html

#deeplearning #machinelearning
#neuralnetworks #AI #ANN