⚪️Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

Lecture 7 Logistic Regression Cost Function

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
#regression

📕 State of Deep Reinforcement Learning: Inferring future outlook

Today machines can teach themselves based upon the results of their own actions. This advancement in Artificial Intelligence seems like a promising technology through which we can explore more innovative potentials of AI. The process is termed as deep reinforcement learning.

🔻What Future Holds for Deep Reinforcement Learning?

Experts believe that deep reinforcement learning is at the cutting-edge right now and it has finally reached a to be applied in real-world applications. They also believe that moving it will have a great impact on AI advancement and can eventually researchers closer to Artificial General Intelligence (AGI).
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/state-deep-reinforcement-learning-inferring-future-outlook/

#deeplearning #AI #AGI
#reinforcement #math
#datascience #machinelearning

📕 Data Mining Methods for Recommender Systems

✒️ by Xavier Amatriain
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📌Via: @cedeeplearning

#datamining #recommendersystems
#clustering #classification #regression
#machinelearning #datascience

⭕️ Top 10 machine learning startups of 2020

✒️ by Priya Dialani

🌀 As per #Crunchbase, there are 8,705 startups and organizations today depending on AI and machine learning for their essential applications, products, and services. Practically 83% of AI and machine learning startups that Crunchbase tracks, had just three or fewer funding rounds, the most well-known being seed rounds, angel rounds, and early-stage rounds.

1. Alation
2. Graphcore
3. AI.reverie
4. DataRobot
5. Anodot
6. Viz.ai
7. FogHorn
8. Jus Mundi
9. Rosetta.ai
10. Folio3
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📌Via: @cedeeplearning

link: https://www.analyticsinsight.net/top-10-machine-learning-startups-of-2020/

#machinelearning #AI
#datascience #starutp
#technology #hightech
#deeplearning #neuralnetworks

📕 Automated Machine Learning: The Free eBook

✒️ By Matthew Mayo

There is a lot to learn about automated machine learning theory and practice. This free eBook can get you started the right way.

The book's table of contents is as follows:

Part I: AutoML Methods
1. Hyperparameter Optimization
2. Meta-Learning
3. Neural Architecture Search

Part II: AutoML Systems
4. Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
5. Hyperopt-Sklearn
6. Auto-sklearn: Efficient and Robust Automated Machine Learning
7. Towards Automatically-Tuned Deep Neural Networks
8. TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
9. The Automatic Statistician

Part III: AutoML Challenges
10. Analysis of the AutoML Challenge Series 2015–2018
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/automated-machine-learning-free-ebook.html

#automl #machinelearning
#automated_ML #free #ebook

⭕️ Top 6 Open Source Pre-trained Models for Text Classification you should use

1. XLNet
2. ERNIE
3. Text-to-Text Transfer Transformer (T5)
4. Binary - Partitioning Transformation (BPT)
5. Neural Attentive Bag-of-Entities Model for Text Classification (NABoE)
6. Rethinking Complex Neural Network Architectures for Document Classification
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📌Via: @cedeeplearning


https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/

#classification #machinelearning
#datascience #model #training
#deeplearning #dataset #neuralnetworks
#NLP #math #AI

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 8 More Derivative 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 #logistic_regression
#regression

Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. [by OpenAI 2020]

Provided with genre, artist, and lyrics as input, Jukebox outputs a new music sample produced from scratch. Below, we show some of our favorite samples.

📚 Paper: https://arxiv.org/abs/2005.00341

📎 Code: [pythorch implementation] https://github.com/openai/jukebox/

🔗 Page: https://openai.com/blog/jukebox/

🎵 Samples: https://soundcloud.com/openai_audio/jukebox-novel_lyrics-78968609

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

Don't you know it's gonna be alright
Let the darkness fade away
And you, you gotta feel the same
Let the fire burn
Just as long as I am there
I'll be there in your night
I'll be there when the
condition's right
And I don't need to
Call you up and say
I've changed
You should stay
You should stay tonight
Don't you know it's gonna be alright
Don't you know it's gonna be alright
When you don't know how to feel
When you're looking for some love
And you gotta feel the same
'Cause I don't need to
Call you up and say
I've changed
You should stay
You should stay tonight
Don't you know it's gonna be alright
I feel the same
Don't you know it's gonna be alrigh

🔖 The Best NLP with Deep Learning Course is Free

Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html

#deeplearning #NLP
#neuralnetworks
#machinelearning
#free #AI #math

🔹🔹 A Holistic Framework for Managing Data Analytics Projects

Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.

🔻The Data Science Delivery Process

Data science initiatives are project-oriented, so they have a defined start and end. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a high-level, extensible process that is an effective framework for data science projects.

Although the steps are shown in the general order in which they are executed, it is important to note that CRISP-DM, like the Agile software development process, is an iterative process framework. Each step can be revisited as many times as needed to refine problem understanding and results.
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

#Agile #CRISP_DM #Data_Analytics #Data_Management #Data_Mining #datascience #Decision_Management, #Development #Software Engineering

👆🏻👆🏻 A Holistic Framework for Managing Data Analytics Projects

🔻 The six CRISP-DM steps are:

1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

#data_management #datamining
#datascience #machinelearning
#preprocessing #agile #project

🔹🔹 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