Python step by step (🔹Free🔹)

Good interactive tutorial from sololearn which will teach you python step by step in a simple way. We suggest you to check it out.

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link: https://www.sololearn.com/User/Login/?ReturnUrl=%2fPlay%2fPython%2f

📌Via: @cedeeplearning

#python
#tutorial
#machinelearning

🔹Statistics versus machine learning

Statistics draws population inferences from a sample, and machine learning finds generalizable predictive patterns.

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#deeplearning
#machinelearning
#statistics

https://www.nature.com/articles/nmeth.4642

🔹3 Ways Machine Learning Can Help Entrepreneurs

1. Machine learning is lightening the workload for humans.

2. Machine learning is “writing the recipe” to personalize ad spend.

3. The tech behind self-driving cars can improve efficiency in myriad ways.

link: https://www.entrepreneur.com/article/336283

📌Via: @cedeeplearning

#marketing
#machinearning
#business
#deeplearning

🔹Uses of machine learning in marketing

We've entered an era in which marketers are being bombarded by volumes of data about consumer preferences. In theory, all of this information should make grouping users and creating relevant content easier, but that's not always the case. Generally, the more data added to a marketer’s workflow, the more time required to make sense of the information and take action.

link: https://www.entrepreneur.com/article/338447

📌Via: @cedeeplearning

#machinelearning
#marketing
#deeplearning
#business

🔻Google trains chips to design themselves

One of the key challenges of computer design is how to pack chips and wiring in the most ergonomic fashion, maintaining power, speed and energy efficiency. The process is known as chip floor planning, similar to what interior decorators do when laying out plans to dress up a room. With digital circuitry, however, instead of using a one-floor plan, designers must consider integrated layouts within multiple floors. As one tech publication referred to it recently, chip floor planning is 3-D Tetris.

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https://techxplore.com/news/2020-04-google-chips.html

#deepleraning
#machinelearning
#AI

🔻Free trainings you can register from edureka on following areas:

Big Data, Data Science, RPA, DEEP Learning, DevOps, Tableau, Selenium,IoT

from: edureka.co

📌Via: @cedeeplearning

#big_data
#machinelearning
#datascience
#deeplearning
#free_courses
#tutorial

🔻Ranked universities and top AI programs in the world

1. Carnegie Mellon University
2. MIT
3. Stanford University
4. University of California - Berkeley
5. University of Washington
6. Cornell University
7. Georgia Institute of Technology
8. University of Illinois - Urbana- Champaign
9. University of Texas - Austin
10. University of Michigan - Ann Arbor

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https://www.usnews.com/best-graduate-schools/top-science-schools/artificial-intelligence-rankings

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#top_universities
#machinelearning
#AI
#deeplearning

🔹Gartner’s 2020 Magic Quadrant For Data Science And Machine Learning Platforms

Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive to get a place in the report.

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https://www.forbes.com/sites/janakirammsv/2020/02/20/gartners-2020-magic-quadrant-for-data-science-and-machine-learning-platforms-has-many-surprises/#3acae7d13f55

📌Via: @cedeeplearning

#machinelearning
#deeplearning
#platform
#gartner

🔻10 Best Machine Learning Frameworks in 2020

1. #TensorFlow
2. Google Cloud ML Learning
3. Apache Mahout
4. Shogun
5. Sci-Kit Learn
6. #PyTorch or TORCH
7. H2O
8. Microsoft Cognitive Toolkit (#CNTK)
9. #Apache MXNet
10. Apple's Core ML

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https://www.cubix.co/blog/best-machine-learning-frameworks-in-2020

📌Via: @cedeeplearning

#deeplearning
#machinelearning
#datascience

🔻Data Scientist Positions Available at Princeton

Princeton University is building a community of data scientists to work in partnership with its world-renowned faculty and students to help solve data-driven research problems. You will work with faculty in a collaborative, multidisciplinary environment and actively contribute your skills to advance scientific discovery and have access to Princeton's first-class resources, the opportunity to co-author academic publications, to offer short courses and workshops on data science, and to collaborate the larger computational data science community.

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link: https://csml.princeton.edu/news/data-scientist-positions-available-princeton

📌Via: @cedeeplearning

#datascience
#machinelearning
#deeplearning
#university
#community

🔹How Algorithms Can Predict Our Intentions Faster Than We Can

Artificial Intelligence (AI) and Natural Language Processing (NLP) can gather data from anywhere online where we leave a mark. This includes our social media posts, our email, and even any small comments we leave on blog posts. Every trace we leave online allows NLP to track and predict our future decisions.
This article highlight how NLP can impact our day-to-day lives with the use of case studies.

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https://www.entrepreneur.com/article/328776

📌Via: @cedeeplearning

#NLP
#AI
#machinelearning
#deeplearning
#algorithm

https://www.paperswithcode.com/

🔹Look at these amazing websites for machine learning and deep learning projects along with the research papers and corresponding codes. It's a good resource for inviting yourself into challenge.

📌Via: @cedeeplearning

🔻Basic-Mathematics-for-Machine-Learning

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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📌Via: @cedeeplearning

https://github.com/hrnbot/Basic-Mathematics-for-Machine-Learning/blob/master/Cheat%20Sheet%20Suggested%20by%20Siraj%20Raval/Calculus%20Cheat%20Sheet.pdf

#machinelearning
#deeplearning
#neuralnetworks

🔹Microsoft Rolls Out 🔻Free🔻 AI Courses Geared Toward Business Leaders

Microsoft is releasing a new set of artificial intelligence courses geared toward business leaders. The free instructional videos and case studies focus on the less technical aspects of the technology as it applies to top execs attempting to integrate AI, including strategy, company culture and ethical responsibilities, into their operations.

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📌Via: @cedeeplearning

https://www.adweek.com/digital/microsoft-rolls-out-free-ai-courses-geared-toward-business-leaders/amp/

#machinelearning
#deeplearning
#AI
#free

🟢 74 Summaries of Machine Learning and NLP Research

📗 you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). They cover a wide range of different topics, authors and venues.

🔗http://www.marekrei.com/blog/74-summaries-of-machine-learning-and-nlp-research/

Via: @cedeeplearning 📌

📗 Adapted Center and Scale Prediction: More Stable and More Accurate

📕 In order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, they have proposed some adaptations based on a detector, Center and Scale Prediction(CSP). The main contributions of their paper are:

1. Improve the robustness of CSP and make it easier to train.
2. Propose a novel method to predict width, namely compressing width.
3. Achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy.
4. Explore some capabilities of Switchable Normalization which are not mentioned in its original paper.

Link: http://arxiv.org/abs/2002.09053

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Neural network architectures

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture.

https://towardsdatascience.com/neural-network-architectures-156e5bad51ba

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

📗Progressive Learning and Disentanglement of Hierarchical Representations

📕present a strategy to progressively learn independent hierarchical representations from high- to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction.
arXiv, Apr 3, 2020
Link: http://arxiv.org/abs/2002.10549


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