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)
TP_Deep_2_webcam.py require opencv-python
You can also install this library in the local environment using pip (
pip install opencv-python)
TP_Deep_1_MNIST.py (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 TP_Deep_3_fine_tuning.py file to some directory and move to this directory with python (e.g.
- 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"))
To go further
- A 'Brief' History of Neural Nets and Deep Learning, December 24, 2015 by Andrey Kurenkov
- Deep Learning 101 - Part 1: History and Background, February 23, 2017 by Andrew L. Beam
- A History of Deep Learning, May 30, 2018 by Andrew Fogg
- Deep Learning in Neural Networks: An Overview, 2015 by Jürgen Schmidhuber
- Timeline of machine learning on wikipedia