Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an #AI to write Python code with Python code  #MachineLearning #DeepLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.
Continue reading “Teaching an AI to write Python code with Python code • Will cars dream?”

Photon ML Tutorial · linkedin/photon-ml Wiki · GitHub

.@LinkedIn #MachineLearning team shares tutorial on building #Recommender systems

  • You will need to install Docker or Docker Toolbox on your system to use it.
  • After you have installed Docker, launch the Docker daemon (this happens automatically on some systems).
  • Remove the tutorial container by running docker rm photon-ml-tutorial .
  • To access the Photon ML Tutorial, navigate to http://localhost:5334/tutorial .

Read the full article, click here.


@kdnuggets: “.@LinkedIn #MachineLearning team shares tutorial on building #Recommender systems”


photon-ml – A scalable machine learning library on Apache Spark


Photon ML Tutorial · linkedin/photon-ml Wiki · GitHub

Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an #AI to write Python code with Python code  #MachineLearning #DeepLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

Read the full article, click here.


@MikeTamir: “Teaching an #AI to write Python code with Python code #MachineLearning #DeepLearning”


OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.


Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an AI to write Python code with Python code • Will cars dream?

Teaching #AI to write #Python code with #Python code #DeepLearning #LSTM #MachineLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

Read the full article, click here.


@kdnuggets: “Teaching #AI to write #Python code with #Python code #DeepLearning #LSTM #MachineLearning”


OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.


Teaching an AI to write Python code with Python code • Will cars dream?

GitHub

A handful of #TensorFlow tutorials:  #DeepLearning #MachineLearning

  • Tensorflow Tutorials using Jupyter Notebook
  • Machine Learing Basics with TensorFlow: Linear Regression / Logistic Regression with MNIST / Logistic Regression with Custom Dataset
  • Hope the tutorials to be a useful recipe book for your deep learning projects.
  • Tried to explain as kindly as possible, as these tutorials are intended for TensorFlow beginners.
  • Most of the codes are simple refactorings of Aymeric Damien’s Tutorial or Nathan Lintz’s Tutorial .

Read the full article, click here.


@randal_olson: “A handful of #TensorFlow tutorials: #DeepLearning #MachineLearning”


Tensorflow-101 – TensorFlow Tutorials


GitHub

GitHub

Very interesting code, lda2vec tools for interpreting natural language  #machinelearning #NLP

  • We build a model that builds both word and document topics, makes them interpreable, makes topics over clients, times, and documents, and makes them supervised topics.
  • lda2vec also yields topics over clients.
  • lda2vec the topics can be ‘supervised’ and forced to predict another target.
  • It’s research software, and we’ve tried to make it simple to modify lda2vec and to play around with your own custom topic models.
  • LDA on the other hand is quite interpretable by humans, but doesn’t model local word relationships like word2vec.

Read the full article, click here.


@andradeandrey: “Very interesting code, lda2vec tools for interpreting natural language #machinelearning #NLP”


Contribute to lda2vec development by creating an account on GitHub.


GitHub