ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....

PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.

It allows you to go from preparing data to deploying a model within SECONDS.

PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:

- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!

Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.

The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.

To learn more about PyCaret, please visit the official website https://www.pycaret.org

#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret

❇️ @AI_Python_EN

Reinforcement Learning

Let's say we have an agent in an unknown environment and this agent can obtain some rewards by interacting with the environment.

The agent is tasked to take actions so as to maximize cumulative rewards. In reality, the scenario could be a bot playing a game to achieve high scores, or a robot trying to complete physical tasks with physical items; and not just limited to these.

Like humans, RL agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.

This kind of learning by trial-and-error, based on rewards or punishments, is known as reinforcement learning (RL).

TensorTrade is an open-source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning.

https://github.com/tensortrade-org/tensortrade

#artificialintelligence #machinelearning #datascience #datascience #python

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

Big GANs Are Watching

You It is the state-of-the-art unsupervised GAN, which parameters are publicly available. They demonstrate that object saliency masks for GAN-produced images can be obtained automatically with BigBiGAN. These masks then are used to train a discriminative segmentation model. Being very simple and easy-to-reproduce, our approach provides competitive performance on common benchmarks in the unsupervised scenario.

Github: https://github.com/anvoynov/BigGANsAreWatching

Paper : https://arxiv.org/abs/2006.04988

#datascience #machinelearning #artificialintelligence #deeplearning

Lecture Notes in Deep Learning: Feedforward Networks — Part 3 | #DataScience #MachineLearning #ArtificialIntelligence #AI

https://bit.ly/2Z2GgQY

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules Mittal et al.: #ArtificialIntelligence #DeepLearning #MachineLearning

https://arxiv.org/abs/2006.16981

In future #AI hiring other AI be like: Job Profile: *human baby sitter*
- Experience : trained on 100 years of past data.
- Test Accuracy : 99.9999
- Precision: blah
- recall : blah
- AUC : blah blah
- Inference time: A.C
- Trained on : Latest "alien" TPUs and GPUs
- Bias : blah Note: AI trained on old TPUs will not be considered. And then AI will gossip with each other about bias and discrimination they have to go through compared to others like:
- "Wouldn't I be considered if I am trained on X country's data?"
- "Why was she considered even though she has outliers in the data?"
- "I am trained on old TPUs, I won't be considered? What!" LOL #artificialintelligence #machinelearning