Hands-On Guide to PyTorch Geometric (with Python code)

Released under MIT license, built on PyTorch, PyTorch Geometric(PyG) is a python framework for deep learning on irregular structures like graphs, point clouds and manifolds, a.k.a Geometric Deep Learning and contains much relational learning and 3D data processing methods. Graph Neural Network(GNN) is one of the widely used representations learning methods but the implementation of…

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Multidimensional multi-sensor time-series data analysis framework

Multidimensional multi-sensor time-series data analysis framework. In this blog post, I will take you through my package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided. The demo notebook can be found on here. One of the specific use case applications focused on “Unsupervised Feature Selection” using the package can be found in the blog post here.

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Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning

Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling and training and TorchScript for serving.

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Understanding Categorical Data

Feature engineering is a crucial step in building a performant machine learning model. Understanding categorical variables and encoding those variables with the right encoding techniques is paramount during the data cleaning and preparation stage. A survey published on Forbes says that Data preparation accounts for about 80% of data scientists’ work. Data scientists spend 60% of their time cleaning and organizing data.

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Detecting a car is TESLA or NOT using Deep Learning with Fast.AI

Hey, there hope you are doing well. So, recently I started going through the Fast.AI deep learning curriculum where two brilliant persons — Jeremy Howard and Rachel Thomas teach Deep Learning. One is a very experienced programmer and another is a mathematician so what could be a better combination .

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82 Python projects with source code

82 Python Projects with Source Code solved and explained for free. Python is one of the best programming languages. Due to its readability and beginner-friendly nature, it has been accepted by industries around the world. So to master Python for any field you have to work on projects. In this article, I will introduce you to 82 Python projects with source code.

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Algorithmic Trading with Python and Machine Learning Part-1

From shouting on the counters to mobile applications, trading has significantly advanced over the last several decades. This progress has made the capability to trade equities, bonds, ETFs, CFDs, etc., available to everyone (depending on your country of residence). Though GUIs provided by brokers can be easy to use, it can get quite painful when managing a large portfolio, involved in day trading, analyzing or tracking a large number of financial instruments, etc. This is where algorithmic trading comes to save us.

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What is Artificial Intelligence? How does AI work, types and the future of it?

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The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life.

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Multi-Label Classification

Multi-label classification sounds similar to multi-label classification but is completely different. These are one of the few terms that when you read about you get the idea completely but when you try to implement your brain goes like multi… what? What was that? Well, I can assure you that feeling confused is quite normal as there are a lot of fancy terms in machine learning and the purpose of this blog is to clarify one of them.

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Essential math for Data Science: Introduction to matrices and the matrix product

As you saw in Essential Math for Data Science, vectors are a useful way to store and manipulate data. You can represent them geometrically as arrows, or as arrays of numbers (the coordinates of their ending points). However, it can be helpful to create more complicated data structures – and that is where matrices need to be introduced.

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Getting started with 5 essential Natural Language Processing libraries

This article is an overview of how to get started with 5 popular Python NLP libraries, from those for linguistic data visualization, to data preprocessing, to multi-task functionality, to state of the art language modeling, and beyond.

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All the Mathematics you need for Artificial Intelligence

A Compiled List of Essential Math topics to supercharge your journey into AI. A week back, I wrote an article on How to Get into Data Science in 2021 and since then I’ve received quite a few emails asking me just how much math is required in Artificial Intelligence. This guide is an absolute life-saver for beginners, so you can study the topics that matter most, and an even better resource for practitioners, such as myself, who require a quick breeze-through on these concepts.

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7 best Python IDEs for developers in 2021

Best Python IDEs

Either we talk about Data Science or Website Development or Artificial Intelligence & Machine Learning or any other domain, there is one thing common in all these areas – Python! The language has experienced significant growth in its demand and popularity in the last few years and is currently ranking at the #1 position at various renowned indices for top programming languages. However, to work with Python efficiently and effectively, you would require a great Python IDE!

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Markov Chain explained

In this article, I will explain and provide the python implementations of Markov chain. This article will not be a deep dive into the mathematics behind Markov chains, instead, it will prioritize the conceptual understanding of how it works and how to implement it with python. I left resources I’ve used and other materials at the bottom of this article which goes into a deep dive in the mathematics behind Markov chains.

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