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

Revision as of 11:58, 18 November 2020

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

08/11/2020: First homework challenge published here
18/10/2020: Updated links to lectures videos and lab notebooks 
14/10/2020: Requested changes of team have been authorized by Presidenza ... stay tuned I will update you on this soon 
13/10/2020: IMPORTANT CHANGE !!! -> Tomorrow 14/10/2020 lectures will be issued ONLY ONLINE!!!! 
09/10/2020: Uploaded lab notebooks and lab recordings
08/10/2020: Change of schedule on the 15/10/2020 we will have online lecture
08/10/2020: Today's video uploaded and Neural Networks Traning Slides updated in the cross-validation part
06/10/2020: IMPORTANT CHANGE !!! -> Tomorrow 07/10/2020 lectures will be issued ONLY ONLINE!!!! 
02/10/2020: Published a guide to install the software which will be used in the labs here
02/10/2020: Published fixed slides about feed forward neural networks
01/10/2020: Pool to request the change of Team is here 
23/09/2020: Tomorrow 24/09/2020 we are going to have the online lecture as planned 
23/09/2020: Today's lectures published
22/09/2020: Added slides on Perceptron, Hebbian learning and feed forward neural networks
20/09/2020: Added links to the lecture recordings and uploaded slides 
14/09/2020: FIX - Team 1 ODD numbers, Team 2 EVEN numbers !!!
14/09/2020: FIX - the hours of the second team were overlapping to the first, now they are correctly one after the other
13/09/2020: The course is about to start ... stay tuned!

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

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 doublecheck their preparation against before the exam.

• [2019/2020] 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 2.0.2 (EX N.0.2), starts at 15:15, ends at 17:00
* On Wednesday, in 2.1.2 (EX N.1.2), starts at 17:30, ends at 19:15
* On Thursday, in teacher webex room, starts at 16:30, ends at 19:15

Course Evaluation

Course evaluation is composed of two parts:

• A written examination covering the whole program graded up to 26/32 ... may be less
• A home project in the form of a "Kaggle style" competition practicing the topics of the course graded up to 6/32 ... may be more

The final score will sum the grade of the written exam and the grade of the home project.

You can find here one example of the exam text to get a flavor of what to expect in the written examination.

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.

Course Slides

Slides from the lectures by Matteo Matteucci

• [2020/2021] Course Introduction: introductory slides of the course with useful information about the course syllabus, grading, and the course logistics.
• [2020/2021] Machine Learning vs Deep Learning: introduction to machine learning paradigms and definition of deep learning with examples
• [2020/2021] From Perceptrons to Feed Forward Neural Networks: the original Perceptron model, Hebbian learning, feed-forward architecture, backpropagation and gradient descent, error functions and maximum likelihood estimation
• [2020/2021] Neural Networks Training: dealing with overfitting (weight decay, early stopping, dropout), vanishing gradient (ReLU and friends), batch normalization
• [2020/2021] Recurrent Neural Networks: learning with sequences, Recurrent Neural Networks, vanishing gradient, Long Short-Term Memories (LSTM).
• Sequence 2 Sequence Learning: sequence to sequence learning as an encoder-decoder problem, Neural Turing Machines, attention mechanisms, the Transformer.
• [2020/2021] Word Embedding: neural autoencoders, language models, word embedding, word2vec, glove.

Slides from the lectures by Giacomo Boracchi are available in his webpage, for you

• Image Classification: Image classification and related issues, template matching, image classification via nearest neighbors methods, image classification via linear classifiers, image classification via hand-crafted features.
• Convolutional Neural Networks: From hand-crafted features to convolutional neural networks.
• Training Convolutional Neural Networks: How to train CNNs, famous architectures, data augmentation, and the like.
• Concolutional Neural Networks for Image Segmentation: CNN architectures for segmentation and detection.

Slides from the practicals by Francesco Lattari

• [2020/2021] Introduction to Keras: Introduction to Keras and Tensorflow2 (slides + notebook)
• [2020/2021] Facing overfitting in Keras: Techniques to limit overfitting in Keras and Tensorboard use (slides + notebook)
• [2020/2021] Convolutional architectures in Keras: How to build, train, and evaluate convolutional models for classification and segmentation in Keras and Tensorflow2 (slides + notebook)
• [2020/2021] Recurrent architectures in Keras: How to build, train, and evaluate recurrent neural architectures in Keras and Tensorflow2 (slides + notebook)

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: Not yet published
• ...: Not yet published

[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.