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

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The following are last-minute news you should be aware of ;-)
 
The following are last-minute news you should be aware of ;-)
 +
06/03/2021: Grades for the [[Media:AN2DL2122Homeworks.pdf|2021/2021 homeworks are available here!]]
 +
24/11/2021: Lecture on 09/12/2021 moved to 16/12/2021
 +
24/11/2021: Change of classroom on the 15/12 from T2.1 to B4.3
 +
12/11/2021: [https://codalab.lisn.upsaclay.fr/competitions/226 Here is the link to the First Homework!!!] (read the registration rules, they have been updated)
 +
12/11/2021: First homework is coming out! [https://docs.google.com/document/d/1T0EXf5I8knJQjK4wveewKsOxkV1IyVRlLrKs7S7yO0M/edit?usp=sharing Register to submit your solutions!]
 +
07/11/2021: We have restructured the notebooks from the labs, please check the new organization and material
 +
13/10/2021: Final grade for 2020/2021 year are [[Media:AN2DL_Grades_20210830.pdf|here]]
 
  30/09/2021: Video and slides updated + notebooks published
 
  30/09/2021: Video and slides updated + notebooks published
 
  24/09/2021: Removed last year practicals, this year they will be different!!! Python crash course moved at the end of the material
 
  24/09/2021: Removed last year practicals, this year they will be different!!! Python crash course moved at the end of the material
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|07/10/2021 || Thursday || --- || --- || --- || No Lectures (Graduation)
 
|07/10/2021 || Thursday || --- || --- || --- || No Lectures (Graduation)
 
|-
 
|-
|13/10/2021 || Wednesday || 15:15 - 17:00 || T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | KERAS: FFNN and Overfitting
+
|13/10/2021 || Wednesday || 15:15 - 17:00 || T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=3c4494b4865929a682d8b7773deeb06b KERAS: FFNN and Overfitting]
 
|-
 
|-
 
|13/10/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)   
 
|13/10/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)   
 
|-
 
|-
|14/10/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || Training tricks: activation functions, network initialization, and other stuff...
+
|14/10/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=bfb3b4478f89834a3fe8908ef38a9a20 Training tricks: activation functions, network initialization, and other stuff...]
 
|-
 
|-
|20/10/2021 || Wednesday || 15:15 - 17:00 || T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || rowspan="2" | The Image Classification Problem
+
|20/10/2021 || Wednesday || 15:15 - 17:00 || T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || rowspan="2" | [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3D001aa81af4155e482b36449d5bf740a2&sa=D&source=calendar&usd=2&usg=AOvVaw27hVczu4cbwbSFkbbWSUHm The Image Classification Problem]
 
|-
 
|-
 
|20/10/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|20/10/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|-
 
|-
|21/10/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || Convolutional Neural Networks
+
|21/10/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3D7ce22e50e0e54055ba5c767ae7139032&sa=D&source=calendar&usd=2&usg=AOvVaw3mgWsYDB-P59366MxvLunC Convolutional Neural Networks]
 
|-
 
|-
|27/10/2021 || Wednesday || 15:15 - 17:00 || T.2.1 (Team 1) || rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | KERAS: Convolutional Neural Networks
+
|27/10/2021 || Wednesday || 15:15 - 17:00 || T.2.1 (Team 1) || rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=b25749461c701879e3814e26b937177d KERAS: Convolutional Neural Networks]
 
|-
 
|-
 
|27/10/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|27/10/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|-
 
|-
|28/10/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || Training with data scarsity
+
|28/10/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3De2757215c853530566cd3cf46833e75c&sa=D&source=calendar&usd=2&usg=AOvVaw3vsxZOjD-e_iXzVTPT0k88 Training with data scarcity]
 
|-
 
|-
|03/11/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | KERAS: Convolutional Neural Networks
+
|03/11/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=495e5d7661d0849fb027a9451ffc4f26 KERAS: Convolutional Neural Networks]
 
|-
 
|-
 
|03/11/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|03/11/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|-
 
|-
|04/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || Famous CNN architectures
+
|04/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3Dfdfcf0d638238f240ebbd7aed018d318&sa=D&source=calendar&usd=2&usg=AOvVaw0zahxjvCI_hEMxKktO_sQ- Famous CNN architectures]
 
|-
 
|-
 
|10/11/2021 || Wednesday || --- || --- || --- || -- No Lecture (Prove in Itinere) --
 
|10/11/2021 || Wednesday || --- || --- || --- || -- No Lecture (Prove in Itinere) --
 
|-
 
|-
|11/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || Fully Convolutional CNN, CNN for image segmentation
+
|11/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3D9bfed46fbc5949d7618be69c2fa50457&sa=D&source=calendar&usd=2&usg=AOvVaw34G9ChAVef06M6iBpN9mXQ Fully Convolutional CNN, CNN for image segmentation (part 1)][https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3Dfa883d62a806513b461540a98830152c&sa=D&source=calendar&usd=2&usg=AOvVaw1A9z3vpzEAkQAO_Bjq0o8p (part 2)]
 
|-
 
|-
|17/11/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || rowspan="2" | CNN for localization and detction
+
|17/11/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || rowspan="2" | [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3D997f9eb198cd2897a4f7171638a7380e&sa=D&source=calendar&usd=2&usg=AOvVaw1Wyc4KPyTxm40V8-jvU0sd GANs]
 
|-
 
|-
 
|17/11/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|17/11/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|-
 
|-
|18/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || GANs
+
|18/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/giacomo.boracchi Giacomo Boracchi] || [https://www.google.com/url?q=https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID%3D8879f77fc8c8d6de57149a6716c9ecc5&sa=D&source=calendar&usd=2&usg=AOvVaw2oVvMyZwpZQAnoz5D-cr2Y CNN for localization and detection]
 
|-
 
|-
|24/11/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | KERAS: Autoencoder, classification, segmentation
+
|24/11/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=ee50aa7ae9d63a876746701aec8d1947 KERAS: reconstruction and  segmentation]
 
|-
 
|-
 
|24/11/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|24/11/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
 
|-
 
|-
|25/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || Recurrent neural networks + LSTM
+
|25/11/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=dbc4844003d98778777ca3e06e9cb961 Recurrent neural networks + LSTM]
 
|-
 
|-
|01/12/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | KERAS: learning with time series
+
|01/12/2021 || Wednesday || 15:15 - 17:00 || E.Gatti - ed.20 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=fb8a6ed405eb1812e92783631d1b9de9 KERAS: learning with time series]
 
|-
 
|-
|01/12/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
+
|01/12/2021 || Wednesday || 17:30 - 19:15 || E.Gatti - ed.20 (Team 2)
 
|-
 
|-
|02/12/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || Sequence to sequence learning and Word Embedding
+
|02/12/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=8f5ee66f75748e65b463160f2a06091a Sequence to sequence learning and Word Embedding]
 
|-
 
|-
 
|08/12/2021 || Wednesday || --- || --- || --- || -- No Lecture (Holiday) --
 
|08/12/2021 || Wednesday || --- || --- || --- || -- No Lecture (Holiday) --
 
|-
 
|-
|09/12/2021 || Thursday || 16:30 - 19:15 || Virtual Room ||[https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci] || Attention Mechanism and Transformer
+
|09/12/2021 || Thursday || --- || --- || --- || -- No Lecture ---
 
|-
 
|-
|15/12/2021 || Wednesday || 15:15 - 17:00 ||T.2.1 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | KERAS: learning with text
+
|15/12/2021 || Wednesday || 15:15 - 17:00 ||B.4.3 (Team 1)|| rowspan="2" | [https://politecnicomilano.webex.com/join/francesco.lattari Francesco Lattari] || rowspan="2" | [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=8c0a4d5119dfc174e6eacbfa96fd3361 KERAS: learning with text]
 
|-
 
|-
|15/12/2021 || Wednesday || 17:30 - 19:15 || T.2.1 (Team 2)
+
|15/12/2021 || Wednesday || 17:30 - 19:15 || B4.3 (Team 2)
 
|-
 
|-
|16/12/2021 || Thursday || 16:30 - 19:15 || Virtual Room || --- || -- Spare Lecture --
+
|16/12/2021 || Thursday || 16:30 - 19:15 || Virtual Room || [https://politecnicomilano.webex.com/join/matteo.matteucci Matteo Matteucci]  || [https://politecnicomilano.webex.com/politecnicomilano/ldr.php?RCID=87be019018382204732ff85b47e64ba8 Attention Mechanism and Transformer]
 
|-
 
|-
 
|}
 
|}
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Course evaluation is composed of two parts:
 
Course evaluation is composed of two parts:
  
* A written examination covering the whole program graded up to 22/30
+
* A written examination covering the whole program graded up to 20/30
* 2 home projects in the form of a "Kaggle style" challenge practicing the topics of the course graded up to 4/30 each
+
* 2 home projects in the form of a "Kaggle style" challenge practicing the topics of the course graded up to 5/30 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.
 
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.
Line 383: Line 390:
 
*[[Media:AN2DL_01_2122_Deep_Learning_Intro.pdf|[2021/2022] Machine Learning vs Deep Learning]]: introduction to machine learning paradigms and definition of deep learning with examples
 
*[[Media:AN2DL_01_2122_Deep_Learning_Intro.pdf|[2021/2022] Machine Learning vs Deep Learning]]: introduction to machine learning paradigms and definition of deep learning with examples
 
*[[Media:AN2DL_02_2122_Perceptron_2_FeedForward.pdf|[2021/2022] 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   
 
*[[Media:AN2DL_02_2122_Perceptron_2_FeedForward.pdf|[2021/2022] 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   
*[[Media:AN2DL_03_2021_NeuralNetwroksTraining_tmp2.pdf|[2020/2021] Neural Networks Training]]: dealing with overfitting (weight decay, early stopping, dropout), vanishing gradient (ReLU and friends), batch normalization  
+
*[[Media:AN2DL_03_2122_NeuralNetwroksTraining.pdf|[2021/2022] Neural Networks Training]]: dealing with overfitting (weight decay, early stopping, dropout), vanishing gradient (ReLU and friends), batch normalization  
*[[Media:AN2DL_04_2021_RecurrentNeuralNetworks.pdf|[2020/2021] Recurrent Neural Networks]]: learning with sequences, Recurrent Neural Networks, vanishing gradient, Long Short-Term Memories (LSTM), seq2seq model.   
+
*[[Media:AN2DL_04_2122_RecurrentNeuralNetworks.pdf|[2021/2022] Recurrent Neural Networks]]: learning with sequences, Recurrent Neural Networks, vanishing gradient, Long Short-Term Memories (LSTM), seq2seq model.   
*[[Media:AN2DL_06_2021_WordEmbedding.pdf|[2020/2021] Word Embedding]]: neural autoencoders, language models, word embedding, word2vec, glove.
+
*[[Media:AN2DL_05_2021_WordEmbedding.pdf|[2021/2022] Word Embedding]]: neural autoencoders, language models, word embedding, word2vec, glove.
*[[Media:AN2DL_07_2021_BeyondSeq2Seq.pdf|[2020/2021] Beyond Sequence 2 Sequence Learning]]: Neural Turing Machines, attention mechanisms, the Transformer.
+
*[[Media:AN2DL_06_2021_BeyondSeq2Seq.pdf|[2021/2022] Beyond Sequence 2 Sequence Learning]]: Neural Turing Machines, attention mechanisms, the Transformer.
 
+
Slides from the lectures by Giacomo Boracchi are available in [https://boracchi.faculty.polimi.it/teaching/AN2DL.htm his webpage], for you
+
* [https://boracchi.faculty.polimi.it/teaching/AN2DL/2020_AN2DL_Lez1_ImageClassification.pdf 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.
+
* [https://boracchi.faculty.polimi.it/teaching/AN2DL/2020_AN2DL_Lez2_CNN.pdf Convolutional Neural Networks]: From hand-crafted features to convolutional neural networks.
+
* [https://boracchi.faculty.polimi.it/teaching/AN2DL/2020_AN2DL_Lez3_4_CNN_TL_Data_Scarcity.pdf Training Convolutional Neural Networks]: How to train CNNs, famous architectures, data augmentation, and the like.
+
* [https://boracchi.faculty.polimi.it/teaching/AN2DL/2020_AN2DL_Lez5_CNN_Architectures_CNN_for_segmentation.pdf Convolutional Neural Networks for Image Segmentation]: CNN architectures for segmentation and detection.
+
* [https://boracchi.faculty.polimi.it/teaching/AN2DL/2020_AN2DL_Lez6_CNN_Architectures_CNN_for_object_detection.pdf Convolutional Neural Networks for Localization and Object Detection]
+
* [https://boracchi.faculty.polimi.it/teaching/AN2DL/2020_AN2DL_Lez7_Generative_Models.pdf Generative Adversarial Networks]
+
  
 +
Slides from the lectures by Giacomo Boracchi are available in [https://boracchi.faculty.polimi.it/teaching/AN2DL.htm his webpage]; for your convenience I am giving pointers to the slide here for you (in case you note discrepancies please notify me)
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez1_ImageClassification.pdf The Image Classification Problem]
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez2_CNN.pdf Convolutional Neural Networks]
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez3_CNN_TL_Data_Scarcity.pdf CNN Parameters and Training with Data Scarcity]
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez4_CNN_for_segmentation.pdf Fully Convolutional CNN and CNN for Semantic Segmentation]
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez5_CNN_Localization_Explanations_Famous_Architectures.pdf CNN for Localization, CNN Explanations and Famous Architectures]
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez7_Generative_Models.pdf Autoencoders and Generative Adversarial Networks]
 +
* [2021/2022] [https://boracchi.faculty.polimi.it/teaching/AN2DL/2021_AN2DL_Lez6_CNN_for_object_detection_Metric_Learning.pdf Object Detction Networks and Metric Learning]
  
 
Slides from the practicals by Francesco Lattari and Eugenio Lomurno
 
Slides from the practicals by Francesco Lattari and Eugenio Lomurno
* [https://colab.research.google.com/drive/13hoe0sHZ8ZOOfWV0wN0Q8SYmLj_oz6Vh?usp=sharing#scrollTo=UO1ENESOKXmf A Crash on Python]: a Colab notebook with a crash course on Python 3 to prepare for the practicals!
+
* [https://drive.google.com/drive/folders/1zO_E89H3mjc7nysBkGUt_MmCn1Mb2DzP?usp=sharing Python and convolutions]: a Colab notebook with a crash course on Python 3 to prepare for the practicals and extra material about convolutions!
* [https://drive.google.com/file/d/1-qNsIRvllmhetxX8Nmns3OYJwqn4KFhZ/view?usp=sharing Notebook 1:] NumPy Arrays vs TensorFlow2 Tensors
+
* [https://drive.google.com/drive/folders/1-WFu2S-MBOrNsFRy1IWEptF_hTMLmic9?usp=sharing Feed Forward Neural Networks]: NumPy Arrays, TensorFlow2 Tensors, and Feed Forward Neural Network
* [https://drive.google.com/file/d/12ODGsMkMPxFykDXp0w1dWj_F1FDU65sh/view?usp=sharing Notebook 2:] FeedForward Neural Network
+
* [https://drive.google.com/drive/folders/1-gaItYI8i01STVPrUcqZ8vxK27yGFENn?usp=sharing Dealing with Overfitting]: Hold Out, Early Stopping and Cross-Validation
 +
* [https://drive.google.com/drive/folders/10H60DRdcFCbwCKHExZdPfLdrtbnmCaRE?usp=sharing Convolutional Neural Networks]: 2D Convolutions and their meaning, Convolutional Neural Networks with Keras
 +
* [https://drive.google.com/drive/folders/10D876SqauR27hB5BUIcckKf3v4CrA_x4?usp=sharing Augmentation and Fine Tuning]: Data Augmentation, Transfer Learning, and Fine Tuning
 +
* [https://drive.google.com/drive/folders/1dxfaXo68dWnrS6xHY0yLvUDjTwDPFN6h?usp=sharing Autoencoders and Segmentation]: Exercises on Autoencoders and Multiclass Segmentation
 +
* [https://drive.google.com/drive/folders/1rZQUrs6Rb1dGg5-KLg-y9tT1yw09gr7G?usp=sharing Dealing with Time Series]: Recurrent Neural Networks, Long Short-Term Memories, and other models.
 +
 
 +
=== External Sources ===
 +
* [https://towardsdatascience.com/back-to-basics-deriving-back-propagation-on-simple-rnn-lstm-feat-aidan-gomez-c7f286ba973d Deriving Back Propagation on simple RNN/LSTM]: a tutorial step by step to derive the backpropagation formulas
 +
* [https://karpathy.github.io/2019/04/25/recipe/ A Recipe for Training Neural Networks]: a neat post on Andrej Karpathy blog about do's and don'ts in training neural networks
  
  

Latest revision as of 01:43, 7 February 2022


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

06/03/2021: Grades for the 2021/2021 homeworks are available here!
24/11/2021: Lecture on 09/12/2021 moved to 16/12/2021
24/11/2021: Change of classroom on the 15/12 from T2.1 to B4.3
12/11/2021: Here is the link to the First Homework!!! (read the registration rules, they have been updated)
12/11/2021: First homework is coming out! Register to submit your solutions!
07/11/2021: We have restructured the notebooks from the labs, please check the new organization and material
13/10/2021: Final grade for 2020/2021 year are here
30/09/2021: Video and slides updated + notebooks published
24/09/2021: Removed last year practicals, this year they will be different!!! Python crash course moved at the end of the material
22/09/2021: Added colab crash course on Python 3 in the material session
18/09/2021: We skip exercising on 22/09 and added exercising on 15/12 I updated the schedule!
16/09/2021: Updated material on the first lectures + added reference to python tutorials in the material section
15/09/2021: Lectures start today!
14/09/2021: Website under maintenance ... come back later


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.

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
For a google calendar you might look here!
Date Day Time Room Teacher Topic
15/09/2021 Wednesday 15:15 - 17:00 T.2.1 (Team1 ) Matteo Matteucci Course Introduction + Deep Learning Intro
15/09/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
16/09/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci From Perceptrons to Feed Forward Neural Networks
22/09/2021 Wednesday -- -- -- -- No Lecture --
22/09/2021 Wednesday -- --
23/09/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci Feed forward neural networks and Backpropagation
29/09/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Francesco Lattari KERAS: Numpy, Tensorflow and FNN
29/09/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
30/09/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci Error Functions Design
06/10/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Matteo Matteucci Overfitting, cross-validation, and Early Stopping
06/10/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
07/10/2021 Thursday --- --- --- No Lectures (Graduation)
13/10/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Francesco Lattari KERAS: FFNN and Overfitting
13/10/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
14/10/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci Training tricks: activation functions, network initialization, and other stuff...
20/10/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Giacomo Boracchi The Image Classification Problem
20/10/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
21/10/2021 Thursday 16:30 - 19:15 Virtual Room Giacomo Boracchi Convolutional Neural Networks
27/10/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Francesco Lattari KERAS: Convolutional Neural Networks
27/10/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
28/10/2021 Thursday 16:30 - 19:15 Virtual Room Giacomo Boracchi Training with data scarcity
03/11/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Francesco Lattari KERAS: Convolutional Neural Networks
03/11/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
04/11/2021 Thursday 16:30 - 19:15 Virtual Room Giacomo Boracchi Famous CNN architectures
10/11/2021 Wednesday --- --- --- -- No Lecture (Prove in Itinere) --
11/11/2021 Thursday 16:30 - 19:15 Virtual Room Giacomo Boracchi Fully Convolutional CNN, CNN for image segmentation (part 1)(part 2)
17/11/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Giacomo Boracchi GANs
17/11/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
18/11/2021 Thursday 16:30 - 19:15 Virtual Room Giacomo Boracchi CNN for localization and detection
24/11/2021 Wednesday 15:15 - 17:00 T.2.1 (Team 1) Francesco Lattari KERAS: reconstruction and segmentation
24/11/2021 Wednesday 17:30 - 19:15 T.2.1 (Team 2)
25/11/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci Recurrent neural networks + LSTM
01/12/2021 Wednesday 15:15 - 17:00 E.Gatti - ed.20 (Team 1) Francesco Lattari KERAS: learning with time series
01/12/2021 Wednesday 17:30 - 19:15 E.Gatti - ed.20 (Team 2)
02/12/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci Sequence to sequence learning and Word Embedding
08/12/2021 Wednesday --- --- --- -- No Lecture (Holiday) --
09/12/2021 Thursday --- --- --- -- No Lecture ---
15/12/2021 Wednesday 15:15 - 17:00 B.4.3 (Team 1) Francesco Lattari KERAS: learning with text
15/12/2021 Wednesday 17:30 - 19:15 B4.3 (Team 2)
16/12/2021 Thursday 16:30 - 19:15 Virtual Room Matteo Matteucci Attention Mechanism and Transformer


Course Evaluation

Course evaluation is composed of two parts:

  • A written examination covering the whole program graded up to 20/30
  • 2 home projects in the form of a "Kaggle style" challenge practicing the topics of the course graded up to 5/30 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.

Regarding the python programming language, we will provide you the basics about numpy and python scripting in case you want some introductory material you can check here

  • Python tutorials: these are the official python tutorials, we suggest 3.An Informal Introduction to Python (Numbers, Strings, Lists), 4.More Control Flow Tools (if, for, range, functions), 5.Data Structures (More on lists, Dictionaries), 9.Classes.

Course Slides

Slides from the lectures by Matteo Matteucci

Slides from the lectures by Giacomo Boracchi are available in his webpage; for your convenience I am giving pointers to the slide here for you (in case you note discrepancies please notify me)

Slides from the practicals by Francesco Lattari and Eugenio Lomurno

External Sources