Logistic Regression: A Concise Technical Overview

Link: https://www.kdnuggets.com/2019/01/logistic-regression-concise-technical-overview.html

Practical Machine Learning – Sunila Gollapudi (en)
#book #middle #theory

----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle

Practical Machine Learning – Sunila Gollapudi (en)
#book #middle #theory

----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle

❇️ مجموعه 10 کورس رایگان در حوزه دیتاساینس و یادگیری ماشین

1️⃣ Machine Learning
(University of Washington)
2️⃣ Machine Learning
(University of Wisconsin-Madison)
3️⃣ Algorithms (in journalism)
(Columbia University )
4️⃣ Practical Deep Learning
(Yandex Data School)
5️⃣ Big Data in 30 Hours
(Krakow Technical University )
6️⃣ Deep Reinforcement Learning Bootcamp
(UC Berkeley(& others))
7️⃣ Introduction to Artificial intelligence
(University of Washington)
8️⃣ Brains, Minds and Machines Summer Course
(MIT)
9️⃣ Design and Analysis of Algorithms
(MIT)
🔟 Natural Language Processing
(University of Washington)
لینک:
https://goo.gl/Riybxs

#MachineLearning #DataScience #Course #DeepLearning #BigData #AI


----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle

7 Ways Artificial Intelligence Can Be Used in An Educational Setting
January 21, 2019 https://www.re-work.co/blog/7-ways-ai-can-be-used-in-education

You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...

#ml
#course
#resource

@machinelearning_tuts

http://www.claoudml.co/

Nice article by Dat Tran about some mathematicians trying to make sense of neural networks. Some of the findings are quite obvious to machine learning practitioners/researchers like deeper network with many layers and fewer neurons aka ResNet are better than shallow networks with few layers but many neurons per layer. It's still interesting though to see that there's an effort in trying to build a "general theory" of neural networks which one usually obtains from experiences and a lot of trial and error. Maybe this will help in the future to do less trial and error.

Dat Tran (https://www.linkedin.com/in/dat-tran-a1602320/)

#deeplearning
#machinelearning
#ml
#article

@machinelearning_tuts

image

https://www.quantamagazine.org/foundations-built-for-a-general-theory-of-neural-networks-20190131/

Artificial Intelligence and Games by Georgios N. Yannakakis

https://www.8freebooks.net/download-artificial-intelligence-and-games-georgios-n-yannakakis-pdf/

Which Deep Learning Framework is Growing Fastest? Read a comparison between major Deep learning frameworks in terms of demand, usage, and popularity https://www.kdnuggets.com/2019/05/which-deep-learning-framework-growing-fastest.html

New AI Strategy Mimics How Brains Learn to Smell

Today’s artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there’s a car in an image, at differentiating between depictions of cats and dogs. “But they are rather pathetic at composing music or writing short stories,” said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. “They have great trouble reasoning meaningfully in the world.”

#deeplearning
#machinelearning
#brainmimic
#smelling

@machinelearning_tuts

For more information:
https://www.quantamagazine.org/new-ai-strategy-mimics-how-brains-learn-to-smell-20180918/

The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.

Browse this awesome portal for State-of-the-Art Machine Learning and Deep Learning Algorithms — 700+ leaderboards • 1000+ tasks • 800+ datasets • 10,000+ papers with code: https://paperswithcode.com/sota

MAREK REI
THOUGHTS ON MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

74 Summaries of Machine Learning and NLP Research

MAREK NOVEMBER 12, 2019 UNCATEGORIZED

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

#DeepLearning #MachineLearning

❇️ @Machinelearning_tuts

Math Reference Tables 📗

1. General 📘
Number Notation
Addition Table
Multiplication Table
Fraction-Decimal Conversion
Interest
Units & Measurement Conversion
2. Algebra 📘
Basic Identities
Conic Sections
Polynomials
Exponents
Algebra Graphs
Functions
3. Geometry 📘
Areas, Volumes, Surface Areas
Circles
4. Trig 📘
Identities
Tables
Hyperbolics
Graphs
Functions
5. Discrete/Linear 📘
Vectors
Recursive Formulas
Linear Algebra
6. Other 📘
Constants
Complexity
Miscellaneous
Graphs
Functions
7. Stat 📘
Distributions
8. Calc 📘
Integrals
Derivatives
Series Expansions
9. Advanced 📘
Fourier Series
Transforms

📍 http://math2.org/

———————————
|@machinelearning_tuts|
———————————

N-shot learning
You may be asking, what the heck is a shot, anyway? Fair question.A shot is nothing more than a single example available for training, so in N-shot learning, we have N examples for training. For more information read 👇🏿

https://blog.floydhub.com/n-shot-learning/

——————————
@machinelearning_tuts

NUSCCF

A new efficient subspace and K-means clustering based method to improve Collaborative Filtering

https://github.com/soran-ghadri/NUSCCF

@machinelearning_tuts

Detectron

Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

https://github.com/facebookresearch/Detectron

@machinelearning_tuts

Keras is a machine learning framework that might be your new best friend if you have a lot of data and/or you’re after the state-of-the-art in AI: deep learning. Plus, it’s the most minimalist approach to using TensorFlow, Theano, or CNTK is the high-level Keras shell.

Key Things to Know:
🔴 Keras is usable as a high-level API on top of other popular lower level libraries such as Theano and CNTK in addition to Tensorflow.
🔴 Prototyping here is facilitated to the limit. Creating massive models of deep learning in Keras is reduced to single-line functions. But this strategy makes Keras a less configurable environment than low-level frameworks.

#machinelearning
#deeplearning
#keras

@deeplearning_tuts