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Both the slides and demos of my session "Deep learning from zero to hero" (in italian language) at AI&ML Conference 2019

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AI&ML Conference 2019 - Deep learning from zero to hero

Here both the slides and demos of my talk "Deep learning from zero to hero" at AI&ML Conference 2019

System requirements

The only dependence of the demos is docker. All demos run in the docker image rucka/deeplearning

The demos

  • 1: The simpson transfer learning

    • Train the model with 30 epochs (accuracy ~50%): ./exec_cmd.sh /code/retrain.fast.sh
    • Train the model with 4000 epochs (accuracy ~90%): ./exec_cmd.sh /code/retrain.sh
    • Evaluate the fast model: ./exec_cmd.sh /code/evaluate.fast.sh /data/simpson/test_set/0.jpg
    • Evaluate the accurated model: ./exec_cmd.sh /code/evaluate.sh /data/simpson/test_set/0.jpg
  • 2: Stock price regression forecast: execute the script ./run_book.sh, open the jupyter home link showed in the command logs and select the notebook 1. stock regression.ipynb

  • 3: Stock trend classification forecast: execute the script ./run_book.sh, open the jupyter home link showed in the command logs and select the notebook 2. stock classification.ipynb. This notebook contains:

    • Multi layer perceptron network
    • Convolutional network
    • CNN vs MLP
    • Multi value classification

Available scripts

Slides

The slide markup has been compiled with Deckset app for OSX. If you need, you can give a try of the trial version available at the website.

The pdf slides are available here

The slides are also published on slideshare.

Abstract (italian)

Hai sentito parlare di Deep Learning ma credi che la teoria alla base sia troppo complessa? Non hai una laurea in matematica e statistica e pensi che il machine learning non faccia per te? Niente paura: hai solo bisogno di una conoscenze di base di Python.

Mai sentito la regola dell’80/20? Con il 20% delle conoscenze puoi raggiungere l’80% dei risultati: in questo talk ti mostrerò in modo pratico tramite delle demo - alcuni trucchi per costruire dei buoni modelli predittivi, evitando di perdere (tanto) tempo nella scelta dei tools e delle librerie necessarie al tuo scopo.

L’obbiettivo è fornirti le basi pratiche con cui scegliere un modello di rete neurale, farne training e ottimizzarlo nel modo più adatto alla tipologia del problema che devi affrontare.

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Both the slides and demos of my session "Deep learning from zero to hero" (in italian language) at AI&ML Conference 2019

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