Python image processing libraries

◆Scikit-Image
◆Pillow
◆NumPy(image is just an array of pixels)

The following are more than image processing, they provide state-of-the-art computer vision and machine learning algorithms:

◆OpenCV
◆OpenMMLab
Also, most machine learning frameworks have image processing functions.

TensorFlow has tf.image and Keras took that further to image processing layers that you can insert inside the model.
For PyTorch users, there is torchvision that contains image processing functions and state-of-the-art vision algorithms.

pytorch.org/vision/stable/…

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More from @Jeande_d

8 Oct
The machine learning research community is very and very vibrant.

Here is what I mean...🧵🧵
In 1958, Frank Rosenblatt invented a perceptron, a very simple algorithm that would later turn out to be the core and origin of to days intelligent machines.
In essence, the perceptron is a simple binary classifier that can determine whether or not a given input belongs to a specific class.

Here is the algorithm of perceptron:
Read 27 tweets
7 Oct
The most useful courses are free. They are only challenging and hard to complete, which is why they are useful.

Here are 4 examples of the free machine learning courses that with enough dedication can help you get useful skills.

🧵
1. Machine Learning by Andrew Ng. on Coursera

Price: Free
Students: Over 4 million people

coursera.org/learn/machine-…
2. Full Stack Deep Learning by UC Berkeley

Price: Free

fullstackdeeplearning.com/spring2021/
Read 7 tweets
4 Oct
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 10 tweets
26 Sep
Releasing a complete machine learning package containing over 30 end to end notebooks for:

◆Data analysis
◆Data visualization
◆Data cleaning
◆Classical ML
◆Computer vision
◆Natural language processing

Everything is now accessible here:

github.com/Nyandwi/machin…
Every single notebook is very interactive.

It starts with a high-level overview of the model/technique being covered and then continues with the implementation.

And wherever possible, there are visuals to support the concepts.
Here is an outline of what you will find there:

PART 1 - Intro to Programming and Working with Data

◆Intro to Python for Machine Learning
◆Data Computation With NumPy
◆Data Manipulation with Pandas
◆Data Visualization
◆Real EDA and Data Preparation
Read 10 tweets
25 Sep
Here are 7 samples from what's coming tomorrow:

1. Data visualization with Seaborn

◆Relational Plots
◆Distribution Plots
◆Categorical Plots
◆Regression Plots
◆Multiplots
◆Matrix Plots: Heat and Cluster Maps
◆Style and Color

colab.research.google.com/drive/1Qkf53B4…
2. Exploratory Data Analysis

◆A quick look into the dataset
◆Summary statistics
◆Finding the basic information about the dataset
◆Checking missing data
◆Checking feature correlations

colab.research.google.com/drive/1iMpQOWH…
3. A Friendly Intro to Machine Learning

◆Intro to ML Paradigm
◆Machine Learning Workflow
◆Evaluation Metrics
◆Handling Underfitting and Overfitting

colab.research.google.com/drive/14uySoOh…
Read 9 tweets
20 Sep
TensorFlow or PyTorch?

Forget about numbers. They are both great at what they do, which is putting machine learning codes together.
TensorFlow is most popular in industries, and PyTorch in research organizations/academics,

but the number of industries that use PyTorch and the number of researches made with TensorFlow have been all increasing.
If you are choosing what to learn for the first time, what is the best than the other does not really matter that much.

Focus on one, know its ins and outs, avoid going back and forth learning all of them, and let everybody else use their favorite tools.
Read 5 tweets

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