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Deep learning lecture
Jupyter Notebook Python
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Deep learning: an historical perspective


This repository contains the sildes of the seminar “Deep learning: an historical perspective”, given October 31 2pm-3pm at SJTU co-organized by SJTU School of Mathematical Sciences and SJTU-ParisTech Elite Institute of Technology (Intro_Deep_SJTU_2018.pdf, 21.5 MB)

It also contains the slides of the introduction to deep learning 3h lecture given at the ECAS-ENBIS 1-Day Summer School, on the 6 of september, 2018 (00_Main_Deep_2018.pdf)

It comes together with practical exercices on deep learning with the solution in python based on keras and some jupyter notebooks within the so called directory.

Deep learning practical session


Keras should be available on your python environment.
You can install this library in the local environment using pip (pip3 install keras) require opencv-python
You can also install this library in the local environment using pip (pip install opencv-python) (based on MNIST)

The purpose of this code is to help to take our first step in deep learning by reproducing the results given on the MNIST site. In less than 3 minutes, you will build and train a fully connected neural network (NN) performing less than 1.5% error on the MNIST database, and then, in less than 15 minutes, a convolutional neural network performing less than 1% error.
It comes together with the jupyter notebook TP_Deep_1_MNIST.ipynb. The other jupyter notebook TP_Deep_1_MNIST_Optim.ipynb, proposes a comparizon of different optimizers on the MNIST fashion dataset.

require a web cam, and opencv-python pip install opencv-python

It works with the directories contained in these zip files


to make it run you may:

  • dowload the file to some directory and move to this directory with python (e.g. cd ../Deep_learning_lecture)
  • define the class MonArg as follows
class MonArg(object):
   def __init__(self, train, val):
        self.train_dir = train
        self.val_dir = val
        self.nb_epoch = NB_EPOCHS
        self.batch_size = BAT_SIZE
        self.output_model_file = "inceptionv3-ft.model"
        self.plot = "store_true"
  • define some constants
IM_WIDTH, IM_HEIGHT = 299, 299 
FC_SIZE = 1024
  • import the file and run it
import TP_Deep_3_fine_tuning

To go further

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