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Jean de Nyandwi Profile picture
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28 Nov, 13 tweets, 5 min read
Machine Learning Weekly HighLights 💡

Made of:

◆3 things from me
◆2 from from others and
◆1 from the community
This week, I explored different object detection libraries, wrote about the hyper-parameter optimization methods, and updated the introduction to machine learning in my complete ML packaged free online book.

I also reached 6000 followers 🎉. Thank you for your support again!
#1

Object detection libraries

#2

Different hyper-parameter optimization techniques

#3

And lastly, here are the updates about the complete ML online free book:

2 things from other people

#1 Machine Learning BootCamp

@Al_Grigor has been teaching a free great machine learning course based on his book.

The course materials are available at

github.com/alexeygrigorev…
#2 Applying ML

@eugeneyan made a great collections of resources on how to apply machine learning: curated papers, blogs, and interviews.

Great collection, Eugene. Like you mentioned, there's a gap between knowing ML vs applying it. Thus, this is 💯💯

applyingml.com
1 from the community: RedCaps

RedCaps is a new largest image-text pair dataset that contains 12M image-text pairs, collected from Reddit. Image
RedCaps dataset can be useful for vision and language tasks such as image captioning and visual question answering.

Paper: arxiv.org/abs/2111.11431
Page: https://t.co/V0CW1xJC2W

RedCaps was collected by @jcjohnss, @kdexd, @gauravkaul7 and @zubinaysola
There is a demo of the dataset trained on VirTex(Learning Visual Representations from Textual Annotations) model that you can try on @huggingface.

I tried it for my own images and it seems to work well.

huggingface.co/spaces/umichVi…
That's it from this week.

A summary of the highlights:

◆Object detection libraries
◆Hyper-parameter optimization methods
◆Updates on Intro to ML in my ML
Complete packaged online free book

◆Alexey ML BootCamp
◆Eugene MLOps resources

◆RedCaps image-text pair dataset
Thanks for reading.

You can retweet or share these highlights to anyone who you thing might benefit from reading them.

Follow @Jeande_d for more ML contents.

Until the next week, stay safe!
A correction on RedCaps website:

redcaps.xyz

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More from @Jeande_d

Jean de Nyandwi Profile picture
29 Nov
How to think about precision and recall:

Precision: What is the percentage of positive predictions that are actually positive?

Recall: What is the percentage of actual positives that were predicted correctly?

🧵🧵
The fewer false positives, the higher the precision. Vice-versa.

The fewer false negatives, the higher the recall. Vice-versa. Image
How do you increase precision? Reduce false positives.

It can depend on the problem, but generally, that might mean fixing the labels of those negative samples(being predicted as positives) or adding more of them in the training data.
Read 12 tweets
Jean de Nyandwi Profile picture
21 Nov
Machine Learning Weekly Highlights 💡

Made of:

◆2 things from me
◆2 from other creators
◆2+1 from the community

A thread 🧵
This week, I wrote about activation functions and why they are important components of neural networks.

Yesterday, I also wrote about image classification, one of the most important computer vision tasks.
#1

Here is the thread about activation functions

Read 12 tweets
Jean de Nyandwi Profile picture
20 Nov
Image classification is one of the most common & important computer vision tasks.

In image classification, we are mainly identifying the category of a given image.

Let's talk more about this important task 🧵🧵
Image classification is about recognizing the specific category of the image from different categories.

Take an example: Given an image of a car, can you make a computer program to recognize if the image is a car?
One might ask why we even need to make computers recognize the images. He or she would be right.

Humans have an innate perception system. Identifying or recognizing the objects seems to be a trivial task for us.

But for computers, it's a different story. Why is that?
Read 15 tweets
Jean de Nyandwi Profile picture
17 Nov
Activations functions are one of the most important components of any typical neural network.

What exactly are activation functions, and why do we need to inject them into the neural network?

A thread 🧵🧵
Activations functions are basically mathematical functions that are used to introduce non linearities in the network.

Without an activation function, the neural network would behave like a linear classifier/regressor.
Or simply put, it would only be able to solve linear problems or those kinds of problems where the relationship between input and output can be mapped out easily because input and output change in a proportional manner.

Let me explain what I mean by that...
Read 27 tweets
Jean de Nyandwi Profile picture
14 Nov
Machine Learning Weekly Highlights 💡

◆3 things from me
◆2 things from other people and
◆2 from the community

🧵🧵
This week, I wrote about what to consider while choosing a machine learning model for a particular problem, early stopping which is one of the powerful regularization techniques, and what to know about the learning rate.

The next is their corresponding threads!
1. What to know about a model selection process...

Read 13 tweets
Jean de Nyandwi Profile picture
12 Nov
Learning rate is one of the most important hyperparameters to adjust well during the ML model training.

A high learning rate can speed up the training, but it can cause the model to diverge. A low rate can slow the training.

Here are different learning rate curves Image
A low learning rate can also give poor results.

A good recommended practice is to usually start with a high rate and then reduce it accordingly.

There are many techniques that can be used to achieve that. They are called learning rate schedulers.
Example of learning rate scheduling techniques:

◆Power scheduler
◆Exponential scheduler
◆Piecewise constant or multi-factor scheduler
◆Performance scheduler
◆Cosine schedule
Read 4 tweets

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