This repository contains the sildes 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

Keras should be available on your python environment.
You can install this library in the local environment using pip (pip3 install keras)

TP_Deep_2_webcam.py require opencv-python
You can also install this library in the local environment using pip (pip install opencv-python)

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

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

It works with the directories contained in these zip files

• train_cheese.zip
• test_cheese.zip

to make it run you may:

• dowload the TP_Deep_3_fine_tuning.py 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 
NB_EPOCHS = 25
BAT_SIZE = 32
FC_SIZE = 1024
NB_IV3_LAYERS_TO_FREEZE = 172

• import the TP_Deep_3_fine_tuning.py file and run it

import TP_Deep_3_fine_tuning
TP_Deep_3_fine_tuning.train(MonArg("train_cheese","test_cheese"))

This link is to run the first part of these 3 tutorials, tp1 in the browser with Zero installation time. You just need a google acount. It is all free of charge!

https://colab.research.google.com/github/StephaneCanu/Deep_learning_lecture/blob/master/TP1_MNIST.ipynb#scrollTo=PcgyO7Izza2w

• A 'Brief' History of Neural Nets and Deep Learning, December 24, 2015 by Andrey Kurenkov
• A History of Deep Learning, May 30, 2018 by Andrew Fogg
• Timeline of machine learning on wikipedia