Using r/WallStreetBets data for Numerai Signals submission.

I stumbled upon Arjun Rohlfing-Das ‘s excellent post on Sentiment Analysis for Trading with Reddit Text Data that uses r/wallstreetbets data for sentiment analysis which seems to be holding predictive power.

Just give me the code

This notebook is built upon the work of Arjun Rohlfing-Das’s notebook. It predicts for entire market. I have modified it to work with all listed symbols.

This is a ‘Run All’ notebook. Once you have setup the PRAW credentials, all you have to do is, just click Run all from colab and it will grenerate a .csv …


Treating Punctuation restoration as translation with Transformers.

Illustration of seq2seq model for punctuation restoration

Task

The transcript we get in ASR is often not punctuated and to use it in other tasks, we need a punctuated text. There are many approaches for this but I wanted to explore seq2seq Transformers with this and possibly for multi-lingual application too.


Can you make unique and equally good predictions?

A still from signals.numer.ai film

If you think Numerai’s main tournament is hard, then you might want to take a look at Signals! It’s more ambitious, and of course, harder! Signals provide a platform to evaluate your financial models and earn some NMR cryptocurrency too!

“Beating the wisdom of the crowds is harder than recognizing faces or driving cars” — Marcos López de Prado

If you are new to Numerai main tournament, this might help.

Just give me the code

This notebook has taken inspiration from the example_model.py and Jason Rosenfeld’s notebook.

From a Numerai participant’s perspective 💡

From the tournament perspective, the main difference between these two…


Don’t just submit and wait, evaluate!

Glowing numerai

Update — DEC 19, 2020: The notebook has been updated according to the new target “Nomi”. TARGET_NAME is now only “target” instead of “target_kazutsugi” .

Just Give me the code

Note: This isn't a 'Run all' and submit notebook. I have tried to make this flexible so feel free to experiment and customize according to your style and workflow.

This post on Model Diagnostics. It also has links to community-written posts on the metrics.

Also, check out A guide to “The hardest data science tournament on the planet” if you want to get started with submitting your predictions for the tournament.

Background

Now, having already submitted…


Data science for fun? for crypto? Why not both? 😀

Source: Numerai blog

Update — DEC 01, 2020: The notebook has been updated according to the new target “Nomi”. TARGET_NAME is now “target” instead of “target_kazutsugi”

Just Give Me The Code:

Make sure you have signed up on numer.ai as you’ll need to set up your API keys to make submissions directly from colab.

💡 The Numerai tournament problem

The Numerai data science problem is like a typical supervised machine learning problem, where the data has several input features and corresponding labels (or targets). And our goal is to learn a mapping from input to targets using various techniques. We usually split data into training and validation parts…


Classifying digits by training a model on MNIST dataset is really a fun thing to do with the frameworks available and putting it to production would be great.

Code: https://github.com/parmarsuraj99/Autoencoders

We know that neural networks can be seen as ‘Universal Function Estimators’ , means we can map them to their correct label. This is called Supervised learning approach.

What if we don’t have labels ? We are left with images only? What can we do with them? Now this is getting interesting. We can train a network to improve resolution of an image, De-noise them, even Generate new samples. …


TensorFlow Logo over The Code in this page

I have been using Python for creating and training my Machine Learning Models which requires setting up quiet a few things(I mostly use Google Colab though). Currently, I am learning Machine Learning and web development along side Android App development.

If you are also into Deep Learning then you must have done Basic Linear regression and the MNIST classification challenge which is the basic problem in Computer Vision. So when I learned about TensorFlow Lite it inspired me to make an app which can utilize the features of Android Smartphone, so I created this basic MNIST handwritten digits classification App.


Linear Regression

It is one of the simplest Algorithm to get started. The goal here is to fit a straight line between two or more variables. Where one is independent and another is dependent (i.e, Y=f(X), means that the value of Y changes according to X but we can take any X in the range given.). Thus, we try to find a relationship (definition of f(X)) between Y and X. Which is in the form of a line Y = m*X + b.

  • Y — Dependent
  • X — independent
  • m — Slope
  • b — Intercept

We try to predict real values at…


What is Regression?

According to Wikipedia,

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed

Just give me the code:

Trending AI Articles:

1. Ten trends of Artificial Intelligence (AI) in 2019

2. Bursting the Jargon bubbles…


Gradient Descent

Gradient Descent is the most popular optimization strategy, used machine learning and deep learning right now. It can be combined with almost every algorithm yet it is easy to understand. So, everyone planning to go on the journey of machine learning should understand this.

Introduction

Gradient descent is used to find local minima of a given function. So, It is a convex function based optimization algorithm.

It is simply used to find the values of the parameters at which the given function reaches its nearest minimum cost.

We start by defining initial parameters and then with the derivation of the function…

Suraj Parmar

Solving problems, one sense, at a time. #ML , “The best way to learn is to teach.” parmarsuraj99@gmail.com About: https://parmarsuraj99.github.io/suraj-parmar/

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