🔹Generative vs. Discriminative Algorithms

To understand GANs, you should know how generative #algorithms work, and for that, contrasting them with discriminative algorithms is instructive. Discriminative algorithms try to classify input data; that is, given the features of an instance of data, they predict a label or category to which that data belongs.

Another way to think about it is to distinguish discriminative from generative like this:

1. #Discriminative models learn the boundary between classes
2. #Generative models model the #distribution of individual classes
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📌Via: @cedeeplearnig
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/generative-adversarial-network-gan

#GAN
#deeplearning
#neuralnetworks
#machinelearning

A self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort.

https://arxiv.org/abs/2002.05314

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

🔻A Beginner's Guide to Convolutional Neural Networks (#CNNs)

Convolutional neural networks are neural networks used primarily to classify images (i.e. name what they see), cluster images by similarity (photo search), and perform object recognition within scenes. For example, convolutional neural networks (ConvNets or CNNs) are used to identify faces, individuals, street signs, tumors, platypuses (platypi?) and many other aspects of visual data.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/convolutional-network

#deeplearning
#neuralnetworks
#machinelearning
#math
#datascience

🔻Data for Deep Learning

🔹Types of Data:
1. sound
2. text
3. images
4. time series
5. video

🔹Use Cases:
1. classification
2. clustering
3. predictions

🔹Data Attributes:
1. relevancy
2. proper classification
3. formatting
4. accessibility

🔹Minimum Data Requirement:
The minimums vary with the complexity of the problem, but 100,000 instances in total, across all categories, is a good place to start.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/data-for-deep-learning

#deeplearning
#machinelearning
#neuralnetworks
#classification
#clustering
#data

🔹Deep Autoencoders

A deep autoencoder is composed of two, symmetrical deep-belief networks that typically have four or five shallow layers representing the encoding half of the net, and second set of four or five layers that make up the decoding half.
The layers are restricted Boltzmann machines, the #building_blocks of deep-belief networks, with several peculiarities that we’ll discuss below. Here’s a simplified schema of a deep autoencoder’s structure, which we’ll explain below.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/deep-autoencoder

#autoencoder
#deepbeliefnetwork
#neuralnetworks
#machinelearning

🔹HOW COMPUTER VISION, AI, AR AND OTHERS ARE ENHANCING IN-VEHICLE EXPERIENCES?

By Smriti Srivastava

Some latest emerging in-vehicle technologies that are changing how people interact with cars:

🔹Authentication Through Biometric

🔹In-vehicle Voice Assistant


🔹Augmented Reality for Heads-up Displays

🔹Reducing Human Error Through Vision-based Monitoring

🔹Retail and Entertainment

Tech-optimized Parking
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/computer-vision-ai-ar-others-enhancing-vehicle-experiences/

#selfdrivingcar
#deeplearning
#AI
#computervision

🔻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

🔻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

🔹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

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

🔹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

🧠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

🔻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

🔹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

🔹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