🔹🔹 Autonomous vehicle landscape 2020: The leaders of self-driving cars race

Self-Driving Car is yet to take a leap from sci-fi to real-world application. With rising debates and discussions at scale regarding the rollout of the autonomous vehicle, people are skeptical about its service towards them. However, far-far away from ordinary man’s thoughts, in the land of innovative technologies and amid top-notch leaders of the race of innovation, self-driving cars are no more a far-off star.

⚪️ Moreover, according to Bloomberg, here the top 5 leaders of autonomous vehicles landscape in 2020:

🔹 Waymo
Investment: US$3 billion

🔹 Cruise
Investment: US$9+ billion

🔹 Argo AI
Investment: US$2.6 billion (VW); US$1 billion (Ford)

🔹 Aurora
Investment: US$700+ million

🔹 Aptiv
Investment: Undisclosed
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📌Via: @cdedeeplearning

https://www.analyticsinsight.net/autonomous-vehicle-landscape-2020-leaders-self-driving-cars-race/

#deeplearning #neuralnetworks
#machinelearning
#self_driving_cars
#datascience

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 9 Computation Graph

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #logistic_regression
#graph #computation_graph

🔻 Deep learning accurately stains digital biopsy slides

Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.

🔹 This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

A Good Read 👌
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📌Via: @cedeeplearning

http://news.mit.edu/2020/deep-learning-provides-accurate-staining-digital-biopsy-slides-0522

#deeplearning #machinelearning
#neuralnetworks
#MIT #math #AI

⚪️ Visualizing the world beyond the frame

🔹Researchers test how far artificial intelligence models can go in dreaming up varied poses and colors of objects and animals in photos.

🔹To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity. Others have asked the models to generate pictures of their own, using a form of artificial intelligence called GANs, or generative adversarial networks. In both cases, the aim is to fill in the gaps of image datasets to better reflect the three-dimensional world and make face- and object-recognition models less biased.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2020/visualizing-the-world-beyond-the-frame-0506

#deeplearning #GANs #math
#machinelearning #visualization
#AI #MIT #datascience

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 10 Derivatives With Computation Graphs

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #logistic_regression
#graph #computation_graph

⭕️ A foolproof way to shrink deep learning models

​Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.

🖋By Kim Martineau

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models.
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📌Via: @cedeeplearning

http://news.mit.edu/2020/foolproof-way-shrink-deep-learning-models-0430

#deeplearning #AI #model
#MIT #machinelearning
#datascience #neuralnetworks
#algorithm #research

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 11 Logistic Regression Gradient Descent

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #logistic_regression
#gradient #gradient_descent

🔋 Machine-learning tool could help develop tougher materials

Engineers develop a rapid screening system to test fracture resistance in billions of potential materials.

🖊 By David L. Chandler

For engineers developing new materials or protective coatings, there are billions of different possibilities to sort through. Lab tests or even detailed computer simulations to determine their exact properties, such as toughness, can take hours, days, or more for each variation. Now, a new artificial intelligence-based approach developed at MIT could reduce that to a matter of milliseconds, making it practical to screen vast arrays of candidate materials.
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📌Via: @cedeeplearning

http://news.mit.edu/2020/machine-learning-develop-materials-0520

#machinelearning #deeplearning
#neuralnetworks #material #AI
#datascience #MIT #engineering

❌ Deep learning is a blessing to police for crime investigations

Deep learning architectures these days are applied to computer vision, speech recognition, machine translation, bioinformatics, drug design, crime inspections and various other fields. Deep learning uses deep neural networks based on which actions are triggered and have produced results comparable to human experts. When compared to traditional machine learning algorithms which are linear, deep learning algorithms are hierarchical. These are based on increasing complexity and abstraction. Now, these are helpful in police investigations in the way these processes available information.

In the police investigations, deep learning helps through the video analysis. Videos gathered from multiple sources are feed into the deep learning systems. Through the software, we can identify and differentiate various targets appearing on the footage.
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📌 Via: @cedeeplearning

https://www.analyticsinsight.net/deep-learning-is-a-blessing-to-police-for-investigations/

#deeplearning #machinelearning
#neuralnetworks #videodetection
#analysis #AI #math #datascience
#artificial_intelligence

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 12 Gradient Descent on m Examples

Neural Networks and Deep Learning
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📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #logistic_regression
#gradient #gradient_descent

🔹🔹 Deep Learning for Detecting Pneumonia from X-ray Images

🖊By Abhinav Sagar

🔻This article covers an end to end pipeline for pneumonia detection from X-ray images.

⚪️ Environment and tools

scikit-learn
keras
numpy
pandas
matplotlib

🔻🔻Do not miss out this article!!
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/06/deep-learning-detecting-pneumonia-x-ray-images.html

#deeplearning #python
#machinelearning #numpy
#pandas #matplotlib
#keras #scikit_learn #healthcare #image_recognition

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 13 Vectorization

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #vectorization

⚪️ Metis Webinar: Deep Learning Approaches to Forecasting

🔹Metis Corporate Training is offering Deep Learning Approaches to Forecasting and Planning, a free webinar focusing on the intuition behind various deep learning approaches, and exploring how business leaders, data science managers, and decision makers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2020/06/metis-webinar-deep-learning-approaches-forecasting.html

#deeplearning #forecasting #metis #webinar #machinelearning #neuralnetworks #free #datascience

🔹 How to Think Like a Data Scientist

🖊By Jo Stichbury

🔻So what does it take to become a data scientist? For some pointers on the skills for success, I interviewed Ben Chu, who is a Senior Data Scientist at Refinitiv Labs.

🔻Be curious
🔻Be scientific
🔻Be creative
🔻Learn how to code
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/think-like-data-scientist-data-analyst.html

#datascience #machinelearning
#tutorial #roadmap
#python #math #statistics #neuralnetworks

🔹 Study by - LinkedIn Learning.
some important skills needed by companies for 2020
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📌Via: @cedeeplearning
📌Other social media:https://linktr.ee/cedeeplearning

#skill #python #machinelearning #computerscience #datascience
#tutorial #softskills #hardskills

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 14 More Vectorization Examples

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #vectorization

🔻 Data science roadmap 2020

🔹Mathematics
🔹Fundamentals
🔹Programming Language
🔹Probability and Statistics
🔹Data Collection and Wrangling
🔹Data Visualization
🔹Machine Learning
🔹Data Science Competition Participation
🔹Resume Creation and Interview Preparation
🔹Neural Network and Deep Learning
🔹Big Data
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📌Via: @cedeeplearning

https://medium.com/@ArtisOne/data-science-roadmap-2020-b256fb948404

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 15 Vectorizing Logistic Regression

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #vectorization

CSNNs: Unsupervised, Backpropagation-free Convolutional Neural Networks for Representation Learning
[ICMLA]
[Bonifaz Stuhr, Jürgen Brauer]

This work combines Convolutional Neural Networks (CNNs), clustering via Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks of Convolutional Self-Organizing Neural Networks (CSNNs), which learn representations in an unsupervised and Backpropagation-free manner.

paper: https://arxiv.org/abs/2001.10388

📌 via: https://news.1rj.ru/str/cedeeplearning

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 16 Vectorizing Logistic Regression's Gradient Computation

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#logistic_regression #gradient_computation