How comfortable are you working on #UnsupervisedLearning problems? Here are 5 comprehensive tutorials to help you learn this critical topic:

1. An Introduction to #Clustering and it's Different Methods - https://bit.ly/2Fwykil

2. Exploring Unsupervised #DeepLearning #Algorithms for #ComputerVision - https://bit.ly/2HPWV39

3. Introduction to Unsupervised Deep Learning (with #Python codes) - https://bit.ly/2HDkMUA

4. Essentials of
#MachineLearning Algorithms (with Python and R Codes) - https://bit.ly/2TQjJWW

5. An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning - https://bit.ly/2JzcyOR


✴️ @AI_Python_EN

Here are 4 awesome articles to learn #ObjectDetection from scratch:

• Understanding and Building an Object Detection Model from Scratch in #Python - https://bit.ly/2ErXMVK

• Part 1: A Step-by-Step Introduction to the Basic Object Detection #Algorithms - https://bit.ly/2V4nqp8

• Part 2: A Practical Implementation of the Faster R-CNN Algorithm for Object Detection - https://bit.ly/2Ugrjdx

• Part 3: A Practical Guide to Object Detection using the Popular YOLO Framework - https://bit.ly/2uq7n9y

✴️ @AI_Python_EN

Protecting your #DeepLearning model will be the key area to focus on from cyber attacks to your models and algorithms.

Placing these is public cloud environments may severely affect your ability to protect these models and algorithms.

You need to prepare to defend these.

What are these adversarial attacks?

1. l2-norm attacks: in these attacks the attacker aims to minimize squared error between the adversarial and original image. These typically result in a very small amount of noise added to the image.
2. l∞-norm attacks: this is perhaps the simplest class of attacks which aim to limit or minimize the amount that any pixel is perturbed in order to achieve an adversary’s goal.
3. l0-norm attacks: these attacks minimize the number of modified pixels in the image.

Below is an example of an l2-norm attack where the left is classified as jeep but the right as a minivan.

#cyberattacks #algorithms #models #deeplearning

✴️ @AI_Python_EN

Making Algorithms Trustworthy

#Algorithms

Algorithms

✴️ @AI_Python_EN

Important Machine Learning algorithms and their Hyperparameters

#machinelearning #datascience #statistics #algorithms

✴️ @AI_Python_EN

Not everyone knows but my #book has its Github repository where all #Python code used to build illustrations is gathered.

So, while reading the book, you can actually run the described #algorithms, play with hyperparameters and #datasets, and generate your versions of illustrations.

https://github.com/aburkov/theMLbook

✴️ @AI_Python_EN

All the Super-Resolution algorithms in one place.
"A Deep Journey into Super-resolution: A Survey"

#pytorch #ai #algorithms

https://lnkd.in/dfnd5se

✴️ @AI_Python_EN

George Box's observation that "Essentially, all models are wrong, but some are useful" is one of the most quoted of all statistical proverbs.

However, we need to ask "Useful for what?"

There are many #machinelearning #algorithms and statistical models that are difficult to interpret but useful in predictive analytics. Sometimes all we need are predictions and classifications that are sufficiently accurate for decision purposes.

Often in the business world there is little or no theory to guide statisticians and data scientists. Moreover, we may not have the data necessary for a good understanding of why some customers are heavier purchasers of our product than others, for instance.

That said, predictions and classifications that are "accurate enough" often aren't good enough. We may need a reasonable - if imperfect - understanding of the Why for these predictions and classifications to be useful.

This is obvious in "hard" scientific research but just as true in the behavioral and social sciences, marketing included.

Being able to design primary studies and having a good grasp of causal analysis, IMO, is necessary to be a full stack analytics professional, as opposed to being a full stack programmer or IT professional. These are different occupations.


"Causation: The Why Beneath The What," an interview with Tyler VanderWeele, a Harvard epidemiologist and authority on causal analysis might be of interest
http://www.greenbookblog.org/2017/07/17/causation-the-why-beneath-the-what/

✴️ @AI_Python_EN

Cornell University - Machine Learning for Intelligent Systems (CS4780/ CS5780)

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.


Youtube Video Lectures:
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Lecture Notes:
http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

#artificialintelligence #machinelearning #deeplearning #AI #algorithms #computerscience #datascience

✴️ @AI_Python_EN

In the future, you may be fired by an algorithm

#algorithms

✴️ @AI_Python_EN

Google Machine Learning (101 slides) - Jason Mayes

posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #DeepLearning #BigData #NeuralNetworks #Algorithms #DataScientists #ReinforcementLearning

✴️ @AI_Python_EN

How comfortable are you working on #UnsupervisedLearning problems? Check out these 5 comprehensive tutorials to learn this critical topic:

1. An Introduction to #Clustering and it's Different Methods - https://lnkd.in/f2enbhy

2. Exploring Unsupervised #DeepLearning #Algorithms for #ComputerVision - https://lnkd.in/fSK7NNC

3. Introduction to Unsupervised Deep Learning (with #Python codes) - https://lnkd.in/fQT_cJ5

4. Essentials of #MachineLearning Algorithms (with Python and R Codes) - https://lnkd.in/fdEGhjf

5. An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning - https://lnkd.in/fFptJcG

✴️ @AI_Python_EN