I’m reading a book on machine learning where the author uses the Random Forest Regression model to fit a dataset. The confidence interval for the root mean squared error is then computed using the following code.

Does anyone know why the code works? Under what assumptions does the sum of squared errors follow a generalized t distribution (unintuitive to me, I feel like it should follow a ${chi}^2$ distribution?

from scipy import stats confidence = 0.95 squared_errors = (final_predictions - y_test) ** 2 #y_test is real values, final_predictions is predicted values of y ci = np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1, loc=squared_errors.mean(), scale=stats.sem(squared_errors))) 

Outside of the large industry labs (FAIR, Brain etc.) and various start ups, what kind of companies are using DL in industry (ideally in Europe)?

Sorry if this isn’t the best place to ask this question but I’d imagine it’s most relevant here.

I am training a VAE on a NLP task, I found that the reconstruction part is really memory consuming. Just wonder if there is any other method to reconstruct the input but without using auto-regressive.

I have thought about 1. Negative sampling, like what word2vec does, then we don’t need to normalize on the whole vocabulary 2. Bag of words, just simply averaging the word vectors and then use it as sentence vector, then reconstruct this sentence vector by minimizing MSE. Since this method reconstruct only one vector for one sentence instead every words, this will be fast and memory friendly. But I am not sure if this can work, or if the neural network will learn some trivial representation instead, e.g. all zeros

Any other idea? Thanks~

Hi everyone,

I’m looking for an implementation of a specific paper or something similar, can someone point me in the good direction?

They use a modified A3C, but to be honest I’m mostly interested in the algorithm because I need to use imitation learning with multiple discrete actions.

Paper: https://arxiv.org/abs/1803.05402 Blog post: https://www.ea.com/seed/news/seed-imitation-learning-concurrent-actions

I hope I’m posting in the appropriate subreddit.

Thanks!

Hi, sometimes I am in a blue mood, and I am thinking about the following questions:

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Will I be automated away in the future?

Is it all just hype?

Am I doing it correctly?

Are others faster than me? How can I become faster?

Why is the process sometimes so tedious?

How can I become a faster/better/more valuable Data Scientist?

Why do easy things sometimes take so long?

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I am wondering, which thoughts do you have about the way you do Data Science at work?

And what do you do about it? e.g. learn new skills or libraries, go to meetups, …

Features of this project:

1. No external library used – sklearn, tensorflow, pytorch etc…
2. Uses a fit() and predict() function approach – just like sklearn. This makes any model very easy to plug and use.
3. Properly commented code with an OOP based approach.
4. I have utilised these implementations in my own pet projects on real datasets and they work without any errors. Obviously, not optimised for speed but they are more for learning the concept and math.

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List of the algorithms:

• Supervised Learning
  • Regression Algorithms
    • Linear Regression
    • Ridge Regression
    • Lasso Regression
    • Decision Trees Regressor
    • K-Nearest Neighbors Regressor
    • Neural Networks
  • Classification Algorithms
    • Binary Logistic Regression
    • Softmax Regression
    • Decision Tree Classifier
    • Adaboost Binary Classifier
    • K-Nearest Neighbors Classifier
    • Neural Network Classifier

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• Unsupervised Learning
  • K-Means
  • Gaussian Mixture Models
  • Variational Autoencoder (In Progress)

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Github Link – https://github.com/aditya1702/Machine-Learning-and-Data-Science/tree/master/Implementation%20of%20Machine%20Learning%20Algorithms

Suggestions are always welcome. Hope you enjoy 🙂

Online Portfolio – https://adityavyas17.com

[PyTorch Based]

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Hi,

for those of you who are interested in RL,

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I recently implemented basic RL algorithms such as

REINFORCE, vanilla actor-critic, DDPG, A3C, DQN and PPO with PyTorch.

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Characteristics are as follows :

• Each algorithm is complete within a single file.
• Length of each algorithm is up to 100~150 lines of codes.
• Every algorithm can be trained within 30 seconds, even without GPU.
• Envs are fixed to “CartPole-v1”. You can just focus on the implementations.

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As you can see in the name of the repository,

I tried to make the code as brief and intuitive as possible.

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Hope you enjoy 🙂

Thank you.

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https://github.com/seungeunrho/minimalRL

I will use it for things like Kaggle’s challenges and/or ML projects. Does the ROCm framework allow discrete performances (comparable to a laptop with similar price and NVIDIA GPU)?