DEV Community: Moyantri Koley

My experiences in the hacker community

Moyantri Koley — Thu, 01 Apr 2021 06:12:45 +0000

Getting involved has been the most important thing I've done for my career. I want to say some reasons for that in this post.
My Story
I started to code in computer science from first year. My first community event was volunteering at a Tech-fest event(kolkata- MINI MAKERS FAIR) in my city. It was a great experience. Then, one month later I participated in different project competitions, poster competition, photography, and Lecture series. I started attending hackathons from 3rd year.
This year's LHD: Share is my first hackathon, which I am really enjoying. Joining #Eddiehub is the best thing. Collaboration first, code second this line is powerful. I was always curious to solve challenges and make things done. Things I have done in this LHD were quite brainstorming and interesting. I have learned a lot from my first Local Hack Day. To be honest I wasn’t expected that I will learn these many new things in a week. I shocked myself that I’m doing these many things. It was an amazing local hack day event for me. I would like to thank MLH for hosting these amazing events and my favorite guild EddieHub and guild members. After attending MLH I have learnt team and collaborate in projects makes you a better person and that help you to become a better coder, as you always learn something new from every hackathons.

The communities that I am a part of:

DSC,UEMK
OSA
SPIE
IETE.UEMK
EDDIEHUB
SPIE-OSA-UEMK student chapter- Secretary
Facebook Developer Circles, Kolkata
ACM
Hackclub
WomenTech Network-Global Volunteer.

how can you start getting involved?
Here are some tips,

Answer Questions, #Discussion
Twitter
Blogging
Developer Circles
Chat Groups
Contribute to open source
Meetups
Conferences

Tech communities not only enable members to upscale their technology skills, but also provide them high visibility among IT recruiters – giving them a unique opportunity. Lastly, I love to be part of communities.

UNSUPERVISED LEARNING

Moyantri Koley — Sat, 05 Dec 2020 05:55:26 +0000

Unsupervised Learning

Unsupervised learning is where only the input data is present and no corresponding output variable is there. Unsupervised learning has a lot of potential ranging anywhere from fraud detection to stock trading.

Unsupervised learning has two categories of algorithms:

Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

Association: An association rule learning problem is where you want to discover rules that describe a large portion of your data.
Association rules mining are used to identify new and interesting insights between different objects in a set, frequent pattern in transactional data or any sort of relational database.
*Anomality detection
*Neural Networks

Clustering
Clustering is similar to classification, but the basis is different. In clustering, you don't know what you are looking for, and you are trying to identify some segments or clusters in your data.
Probabilistic Clustering – clustering your data points into clusters on a probabilistic scale.

These variations on the same fundamental concept might look something like this in code:

Import the KMeans package from Scikit Learn

from sklearn.cluster import KMeans

Grab the training data

x = os.path(‘train’)

Set the desired number of clusters

k = 5

Run the KMeans algorithm

kmeans = KMeans(n_clusters=k).fit(x)

Show the resulting labels

kmeans.labels_

Any clustering algorithm will typically output all of your data points and the respective clusters to which they belong. It’s up to you to decide what they mean and exactly what the algorithm has found.

Unsupervised learning is suitable for exploring unknown data. If you don’t know what you need to find, then this is the perfect method for you.
It’s quite costly to annotate large datasets. As a result, experts rely on a few examples to work on the problem.

Learn how to implement the following Machine learning Clustering models:

1.K-mean Clustering
2.Hierarchical Clustering
The main problem is how to use the right estimator for our problems?
You can use the https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html map for your problem.

Happy coding!

Data Augmentation :

Moyantri Koley — Tue, 10 Nov 2020 14:36:47 +0000

🎯 Data augmentation is a technique through which one can increase the size of the data for the training of the model without adding the new data. Techniques like padding, cropping, rotating, and flipping are the most common methods that are used over the images to increase the data size.

Note: Both Keras Preprocessing Layers and tf.image can be used for data augmentation. For finer control , tf.image is preferred.