Hey all, I’m participating in a contest where we have to do sentiment analysis on drug reviews. It has three categories as outputs : positive, negative, neutral.

I’ve done the basics pre- processing, tf-idf vectorizer etc. I’m now on the model application process. Can you suggest some best practices that I might have missed?

I’ve tried out many models like Logistic Regression, LinearSVM, Randomforest etc. LR came out the best. If I improve my f1score by 0.1 I might actually win the contest. Can you please help? It’s currently at 0.54

Hey guys! I’m new to AI and machine learning and I created these 3 laws(similar to 3 laws of thermodynamics) which can govern a self thinking(self-conscious?..maybe in the future?) robot or machine.I would love to hear your input on this.

1st law Law of Efficiency: A Systems efficiency can be determined by how well it handles the entropy present in its environment and the variables given to it.

2nd law Law of Ideal: A system is said to be ideal if it can take into consideration and handle on its own each and every event that happens in its environment and universe.

3rd law Law of Safe: A system is said to be safe if it handles and responds on its own to any event happening in its environment in a universally accepted manner.

What do you think on this?

I built a deep learning workstation to play around with some deep learning models. Nothing to beastly, just a single GTX1060.

What’s the most convenient method to connect to it with my laptop and run experiments on the GPU?

Hi everyone.

I made an AI agent to play the game of Snake using Reinforcement Learning https://youtu.be/yp826Ybh4rU . Here is a link to the code https://github.com/AI-Olympics/Snake-AI . Any suggestions and positive feedback are welcome.

Edit : While making this project I faced a small hurdle i.e the snake AI would perform good and keep increasing it’s average score as training goes on, but after a score/length of near 50, it’s average score would start decreasing and if trained for any longer the AI would become ridiculous at eating food. Is there a way to solve this problem?

Does anyone know what’s going on with AISTATS 2020? The web page https://www.aistats.org/ still directs to AISTATS 2019. Usually, the deadline is in early October, so I’d have expected to see an announcement by now?

I’ve implemented a seq2seq generative character model in pure Numpy on top of CS231n’s assignment code. It’s nothing fancy but I think being able to see things in matrix multiplication level gives good intuitions about concepts in machine learning. Thus this repository contains reference implementations for

• CharRNN (Sequence to Sequence RNN to produce characters with temperature)
• Unit tests with numerical gradients to check implementations
• Character Embedding
• Recurrent Neural Networks
• Temporal Affine Layer
• Temporal Softmax
• Adam optimizer
• Solver (Run training loop, update weights, save model, record logs, etc.)
• Data loading, character mapping, masking non-frequent characters

Hope it will be useful for others, as well!

Repo

https://github.com/kirbiyik/generate-any-text

I am looking to sparse training; that is, during each training step, only a very small fraction of the weights go through the forward and backwards pass. So, only a small fraction of weights need to be loaded into memory at once.

I was thinking of having the weights saved in sqlite or hdf5, and then having a keras layer which copies the relevant weights stored for the forward pass. And then after the update step, those weights are update into the disk.

I know there are ways to directly setting the weights outside training https://stackoverflow.com/questions/51354186/how-to-update-weights-manually-with-keras

But I am wondering if any framework allows manually updating variables, after they have been already updated in a training step.

Lately there seems to be a developed increase in coin collecting. Especially in circulated change in the US. As a long time coin collector and computer guru. Would it make sense to create a paid app to do this for ordinary people? Simply take a pic or front and back and get: 1)Estimated Grade 2)Metal composition 3)Unique (Rare) Features 4)Estimater Market Valeu

I am excited to announce that Skafos has open sourced the code for our Boot Finder App. This app uses an image similarity model to identify which boots on the Zappos website are most similar to a picture taken by the app user.

Here are our repos:

• Model building code: https://github.com/skafos/boot-finder-model
• App building code: https://github.com/skafos/BootFinder

We have open sourced this code because we’ve talked to many people who are interested in putting ML models into iOS Apps, but either don’t have a good use case, or have trouble connecting all the technical pieces. (App development in Swift, model development in Python, integrating the pieces, retraining, etc.) We are hoping our repos, and our platform Skafos, will help people to connect the dots and build ML-powered apps.

Here are some of the technical details:

• We built our model in Python 3.6 using Turi Create, converted it to Core ML, and embedded it into an iOS App.
• We use Kubernetes to automatically retrain each Sunday night.
• Skafos is used to deliver model updates to the app.

Happy building! Feel free to drop your questions below! For additional info, please visit our website at skafos.ai.