Difference between revisions of "Artificial Neural Networks and Deep Learning"

Revision as of 00:14, 14 September 2021

The following are last-minute news you should be aware of ;-)

15/09/2021: Lectures start today!

Course Aim & Organization

Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm.

Nowadays, deep neural network can outperform traditional hand-crafted algorithms, achieving human performance in solving many complex tasks, such as natural language processing, text modeling, gene expression modeling, and image recognition. The course provides a broad introduction to neural networks (NN), starting from the traditional feedforward (FFNN) and recurrent (RNN) neural networks, till the most successful deep-learning models such as convolutional neural networks (CNN) and long short-term memories (LSTM).

The course major goal is to provide students with the theoretical background and the practical skills to understand and use NN, and at the same time become familiar and with Deep Learning for solving complex engineering problems.

Teachers

The course is composed of a blending of lectures and exercises by the course teachers and a teaching assistant.

• Matteo Matteucci: the course teacher and this is his webex room
• Giacomo Boracchi: the course co-teacher and this is his webex room
• Francesco Lattari: the course teaching assistant and this is his webex room
• Eugenio Lomurno: the course teaching assistant and this is his webex room

Course Program and Syllabus

This goal is pursued in the course by:

• Presenting major theoretical results underpinning NN (e.g., universal approx, vanishing/exploding gradient, etc.)
• Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
• Illustrating the best practices on how to successfully train and use these models (e.g., dropout, data augmentation, etc.)
• Providing an overview of the most successful Deep Learning architectures (e.g., CNNs, sparse and dense autoencoder, LSTMs for sequence to sequence learning, etc.)
• Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.

We have compiled a detailed syllabus of the course student can use to double check their preparation against before the exam.

• [2020/2021] Course Syllabus: a detailed list of topics covered by the course and which students are expected to know when approaching the exam

Detailed course schedule

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all days we have lectures!!

Note: Lecture timetable interpretation
* On Wednesday, in T.2.1, Team 1, starts at 15:15, ends at 17:00
* On Wednesday, in T.2.1, Team 2, starts at 17:30, ends at 19:15
* On Thursday, in teacher webex room, starts at 16:30, ends at 19:15

Note: Teams division is based on your Codice Persona (and should minimize overlap)
* Team 1: odd Codice Persona
* Team 2: even Codice Persona

Course Evaluation

Course evaluation is composed of two parts:

• A written examination covering the whole program graded up to 22/30
• 2 home projects in the form of a "Kaggle style" challenge practicing the topics of the course graded up to 4/30 each

The final score will sum the grade of the written exam and the grade of the home projects. Home projects are not compulsory and they are issued only once a year.

Teaching Material (the textbook)

Lectures will be based on material from different sources, teachers will provide their slides to students as soon they are available. As a general reference you can check the following text, but keep in mind that teachers will not follow it strictly

• Deep Learning. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016.

The remaining material about the course is available through WeBeep.

Past Online Exams

As for the last semester, Politecnico di Milano exams will be held online. We have not yet decided the digital environment we are going to use for the exams, but you can have a flavor of what to expect by looking at past semester exam calls.

• 19/06/2020 Online Exam
• 15/07/2020 Online Exam
• 03/09/2020 Online Exam

Note: this year written exams will be graded 26 points instead of 27. This is because the past year challenges were 5 points while this year they are 6 points worth. This means that the maximum mark is still 26+6=32. You can get an extra point if you participate in the second phase of the Image Segmentation Challenge thus the full mark could reach in principle 33. To get a laude it is required to pass 30.

Lab software setup

For the lab in class we suggest you install TensorFlow 2 on you machine so to be able to follow the coding examples step by step. Here what you should do:

• Install Anaconda according to your distro (Windows/Linux), python 3.7 from [1]
• From terminal (Anaconda Prompt in Windows):
  • conda create -n tf_env python=3.7 tensorflow-gpu
  • conda activate tf_env
  • pip install --upgrade pip
  • pip install jupyter
  • pip install pillow
• Test your Tensorflow install
  • Run python from terminal (Anaconda Prompt in Windows) with «python»
  • import tensorflow
  • print(tensorflow.__version__) -> your should get version 2.1.0 or higher
• Test your Jupiter install
  • From terminal (Anaconda Prompt in Windows) use the command jupyter notebook -> a Jupiter tab should appear in your browser
  • On top right click on «New» and select «Python 3» from the menu -> a Jupiter Notebook should appear in a new tab
  • Write code 3b and 3c in cell "In [ ]:" and execute clicking on «Run».

Kaggle Homeworks

As part of the evaluation (up to 6 marks in the final grade) we are issuing 3 homeworks in the form of "Kaggle style" competitions. They are meant to practice the course topics on simple image recognition tasks.

[2020/2021]

• Image Classification Homework: the first homework is about image classification with convolutional neural networks. The deadline to submit the results is November the 22nd.
• Image Segmentation Homework: the second homework is about image segmentation. This competition is open also to external participants so it lasts until January, you are not requested to participate to the two Stages, just at the Development one and your delivery is expected by December 20th. There are different datasets and leaderboards you can decide to participate to any of the leaderboard and to use any approach you prefer for segmentation.
• Visual Question answering Homework: the third homework is about visual question answering and it mixes feature extraction from images and text in order to solve an multi domain classification tas. This time we do not want to put pressure on you and the deadline is quite relaxed, you have until the 31st of January to submit your solution.

• Extra point -> Image Segmentation Homework: from 19th January 2021 till 22nd January 2021 there will be the second phase of the image segmentation challenge where new data will be released to train you models and participate to the final leaderboard. One extra point for you if you fine tune your models and participate into this!!!

[2019/2020]

• Image Classification Homework: the first homework is about image classification with convolutional neural networks. The deadline to submit the results is November the 30th.
• Image Segmentation Homework: the second homework is about image segmentation with convolutional neural networks and the like. The deadline to submit the results is December the 17th.
• Visual Question Answering Homework: the third homework is about visual question answering with convolutional and recurrent neural networks ... plus word2vec. The deadline to submit the results is January the 15th.