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# Deep Learning – Summer 2018/19

In recent years, deep neural networks have been used to solve complex machine-learning problems. They have achieved significant state-of-the-art results in many areas.

The goal of the course is to introduce deep neural networks, from the basics to the latest advances. The course will focus both on theory as well as on practical aspects (students will implement and train several deep neural networks capable of achieving state-of-the-art results, for example in named entity recognition, dependency parsing, machine translation, image labeling or in playing video games). No previous knowledge of artificial neural networks is required, but basic understanding of machine learning is advisable.

SIS code: NPFL114
Semester: summer
E-credits: 7
Examination: 3/2 C+Ex
Guarantor: Milan Straka

### Timespace Coordinates

• lectures: Czech lecture is held on Monday 14:50 in S9, English lecture on Monday 12:20 in S9; first lecture is on Mar 04
• practicals: there are three parallel practicals, on Monday 17:20 in S9, on Tuesday 9:00 in SU1, and on Tuesday 12:20 in SU1; first practicals are on Mar 04/05

### Requirements

To pass the practicals, you need to obtain at least 80 points, which are awarded for home assignments. Note that up to 40 points above 80 will be transfered to the exam.

To pass the exam, you need to obtain at least 60, 75 and 90 out of 100 points for the written exam (plus up to 40 points from the practicals), to obtain grades 3, 2 and 1, respectively.

The lecture content, including references to study materials. The main study material is the Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville, (referred to as DLB).

References to study materials cover all theory required at the exam, and sometimes even more – the references in italics cover topics not required for the exam.

### 1. Introduction to Deep Learning

• Random variables, probability distributions, expectation, variance, Bernoulli distribution, Categorical distribution [Sections 3.2, 3.3, 3.8, 3.9.1 and 3.9.2 of DLB]
• Self-information, entropy, cross-entropy, KL-divergence [Section 3.13 of DBL]
• Gaussian distribution [Section 3.9.3 of DLB]
• Machine Learning Basics [Section 5.1-5.1.3 of DLB]
• History of Deep Learning [Section 1.2 of DLB]
• Linear regression [Section 5.1.4 of DLB]
• Brief description of Logistic Regression, Maximum Entropy models and SVM [Sections 5.7.1 and 5.7.2 of DLB]
• Challenges Motivating Deep Learning [Section 5.11 of DLB]
• Neural network basics (this topic is treated in detail withing the lecture NAIL002)
• Neural networks as graphs [Chapter 6 before Section 6.1 of DLB]
• Output activation functions [Section 6.2.2 of DLB, excluding Section 6.2.2.4]
• Hidden activation functions [Section 6.3 of DLB, excluding Section 6.3.3]
• Basic network architectures [Section 6.4 of DLB, excluding Section 6.4.2]

### 2. Training Neural Networks

• Capacity, overfitting, underfitting, regularization [Section 5.2 of DLB]
• Hyperparameters and validation sets [Section 5.3 of DLB]
• Maximum Likelihood Estimation [Section 5.5 of DLB]
• Neural network training (this topic is treated in detail withing the lecture NAIL002)
• Gradient Descent and Stochastic Gradient Descent [Sections 4.3 and 5.9 of DLB]
• Backpropagation algorithm [Section 6.5 to 6.5.3 of DLB, especially Algorithms 6.2 and 6.3; note that Algorithms 6.5 and 6.6 are used in practice]
• SGD algorithm [Section 8.3.1 and Algorithm 8.1 of DLB]
• SGD with Momentum algorithm [Section 8.3.2 and Algorithm 8.2 of DLB]
• SGD with Nestorov Momentum algorithm [Section 8.3.3 and Algorithm 8.3 of DLB]
• RMSProp algorithm [Section 8.5.2 and Algorithm 8.5 of DLB]
• Adam algorithm [Section 8.5.3 and Algorithm 8.7 of DLB]

### 3. Training Neural Networks II

• Training neural network with a single hidden layer
• Softmax with NLL (negative log likelihood) as a loss function [Section 6.2.2.3 of DLB, notably equation (6.30); plus slides 10-12]
• Regularization [Chapter 7 until Section 7.1 of DLB]
• Early stopping [Section 7.8 of DLB, without the How early stopping acts as a regularizer part]
• L2 and L1 regularization [Sections 7.1 and 5.6.1 of DLB; plus slides 17-18]
• Dataset augmentation [Section 7.4 of DLB]
• Ensembling [Section 7.11 of DLB]
• Dropout [Section 7.12 of DLB]
• Label smoothing [Section 7.5.1 of DLB]
• Saturating non-linearities [Section 6.3.2 and second half of Section 6.2.2.2 of DLB]
• Parameter initialization strategies [Section 8.4 of DLB]

Easter Monday

### 10. Deep Generative Models

• Autoencoders (undercomplete, sparse, denoising) [Chapter 14, Sections 14-14.2.3 of DLB]
• Deep Generative Models using Differentiable Generator Nets [Section 20.10.2 of DLB]
• Variational Autoencoders [Section 20.10.3 plus Reparametrization trick from Section 20.9 (but not Section 20.9.1) of DLB, Diederik P Kingma, Max Welling: Auto-Encoding Variational Bayes]

### 11. Speech Synthesis, Reinforcement Learning

Study material for Reinforcement Learning is the Reinforcement Learning: An Introduction; second edition by Richard S. Sutton and Andrew G. Barto (reffered to as RLB), available online.

### 12. Transformer, External Memory Networks

May 20 Slides PDF Slides

The tasks are evaluated automatically using the ReCodEx Code Examiner. The evaluation is performed using Python 3.6, TensorFlow 2.0.0a0, NumPy 1.16.1 and OpenAI Gym 0.9.5.

You can install all required packages either to user packages using pip3 install --user tensorflow==2.0.0a0 gym==0.9.5, or create a virtual environment using python3 -m venv VENV_DIR and then installing the packages inside it by running VENV_DIR/bin/pip3 install tensorflow==2.0.0a0 gym==0.9.5. If you have a GPU, you can install GPU-enabled TensorFlow by using tensorflow-gpu instead of tensorflow.

### Teamwork

Working in teams of size 2 (or at most 3) is encouraged. All members of the team must submit in ReCodEx individually, but can have exactly the same sources/models/results. However, each such solution must explicitly list all members of the team to allow plagiarism detection using this template.

### numpy_entropy

Deadline: Mar 17, 23:59  3 points

The goal of this exercise is to famirialize with Python, NumPy and ReCodEx submission system. Start with the numpy_entropy.py.

Load a file numpy_entropy_data.txt, whose lines consist of data points of our dataset, and load numpy_entropy_model.txt, which describes a model probability distribution, with each line being a tab-separated pair of (data point, probability). Example files are in the labs/01.

Then compute the following quantities using NumPy, and print them each on a separate line rounded on two decimal places (or inf for positive infinity, which happens when an element of data distribution has zero probability under the model distribution):

• entropy H(data distribution)
• cross-entropy H(data distribution, model distribution)
• KL-divergence DKL(data distribution, model distribution)

Use natural logarithms to compute the entropies and the divergence.

### mnist_layers_activations

Deadline: Mar 17, 23:59  3 points

The templates changed on Mar 11 because of the upgrade to TF 2.0.0a0, be sure to use the updated ones when submitting!

In order to familiarize with TensorFlow and TensorBoard, start by playing with example_keras_tensorboard.py. Run it, and when it finishes, run TensorBoard using tensorboard --logdir logs. Then open http://localhost:6006 in a browser and explore the active tabs.

Your goal is to modify the mnist_layers_activations.py template and implement the following:

• A number of hidden layers (including zero) can be specified on the command line using parameter layers.
• Activation function of these hidden layers can be also specified as a command line parameter activation, with supported values of none, relu, tanh and sigmoid.
• Print the final accuracy on the test set.

In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard:

• 0 layers, activation none
• 1 layer, activation none, relu, tanh, sigmoid
• 10 layers, activation sigmoid, relu

### mnist_training

Deadline: Mar 24, 23:59  4 points

This exercise should teach you using different optimizers, learning rates, and learning rate decays. Your goal is to modify the mnist_training.py template and implement the following:

• Using specified optimizer (either SGD or Adam).
• Optionally using momentum for the SGD optimizer.
• Using specified learning rate for the optimizer.
• Optionally use a given learning rate schedule. The schedule can be either exponential or polynomial (with degree 1, so inverse time decay). Additionally, the final learning rate is given and the decay should gradually decrease the learning rate to reach the final learning rate just after the training.

In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard:

• SGD optimizer, learning_rate 0.01;
• SGD optimizer, learning_rate 0.01, momentum 0.9;
• SGD optimizer, learning_rate 0.1;
• Adam optimizer, learning_rate 0.001;
• Adam optimizer, learning_rate 0.01;
• Adam optimizer, exponential decay, learning_rate 0.01 and learning_rate_final 0.001;
• Adam optimizer, polynomial decay, learning_rate 0.01 and learning_rate_final 0.0001.

### gym_cartpole

Deadline: Mar 24, 23:59  4 points

Solve the CartPole-v1 environment from the OpenAI Gym, utilizing only provided supervised training data. The data is available in gym_cartpole-data.txt file, each line containing one observation (four space separated floats) and a corresponding action (the last space separated integer). Start with the gym_cartpole.py.

The solution to this task should be a model which passes evaluation on random inputs. This evaluation is performed by running the gym_cartpole_evaluate.py, which loads a model and then evaluates it on 100 random episodes (optionally rendering if --render option is provided). In order to pass, you must achieve an average reward of at least 475 on 100 episodes. Your model should have either one or two outputs (i.e., using either sigmoid of softmax output function).

The size of the training data is very small and you should consider it when designing the model.

When submitting your model to ReCodEx, submit:

• one file with the model itself (with h5 suffix),
• the source code (or multiple sources) used to train the model (with py suffix), and possibly indicating teams.

### mnist_regularization

Deadline: Mar 31, 23:59  6 points

You will learn how to implement three regularization methods in this assignment. Start with the mnist_regularization.py template and implement the following:

• Allow using dropout with rate args.dropout. Add a dropout layer after the first Flatten and also after all Dense hidden layers (but not after the output layer).
• Allow using L2 regularization with weight args.l2. Use tf.keras.regularizers.L1L2 as a regularizer for all kernels and biases of all Dense layers (including the last one).
• Allow using label smoothing with weight args.label_smoothing. Instead of SparseCategoricalCrossentropy, you will need to use CategoricalCrossentropy which offers label_smoothing argument.

In ReCodEx, there will be three tests (one for each regularization methods) and you will get 2 points for passing each one.

In addition to submitting the task in ReCodEx, also run the following variations and observe the results in TensorBoard (notably training, development and test set accuracy and loss):

• dropout rate 0, 0.3, 0.5, 0.6, 0.8;
• l2 regularization 0, 0.001, 0.0001, 0.00001;
• label smoothing 0, 0.1, 0.3, 0.5.

### mnist_ensemble

Deadline: Mar 31, 23:59  2 points

Your goal in this assignment is to implement model ensembling. The mnist_ensemble.py template trains args.models individual models, and your goal is to perform an ensemble of the first model, first two models, first three models, …, all models, and evaluate their accuracy on the development set.

In addition to submitting the task in ReCodEx, run the script with args.models=7 and look at the results in mnist_ensemble.out file.

### uppercase

Deadline: Mar 31, 23:59  4-9 points

This assignment introduces first NLP task. Your goal is to implement a model which is given Czech lowercased text and tries to uppercase appropriate letters. To load the dataset, use uppercase_data.py module which loads (and if required also downloads) the data. While the training and the development sets are in correct case, the test set is lowercased.

This is an open-data task, where you submit only the uppercased test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 96.5% accuracy will get 4 points; the rest 5 points will be distributed depending on relative ordering of your solutions, i.e., the best solution will get total 9 points, the worst solution (but at least with 96.5% accuracy) will get total 4 points. The accuracy is computed per-character and can be evaluated by uppercase_eval.py script.

You may want to start with the uppercase.py template, which uses the uppercase_data.py to load the data, generate an alphabet of given size containing most frequent characters, and generate sliding window view on the data. The template also comments on possibilities of character representation.

Do not use RNNs or CNNs in this task (if you have doubts, contact me).

### mnist_cnn

Deadline: Apr 07, 23:59  5 points

To pass this assignment, you will learn to construct basic convolutional neural network layers. Start with the mnist_cnn.py template and assume the requested architecture is described by the cnn argument, which contains comma-separated specifications of the following layers:

• C-filters-kernel_size-stride-padding: Add a convolutional layer with ReLU activation and specified number of filters, kernel size, stride and padding. Example: C-10-3-1-same
• CB-filters-kernel_size-stride-padding: Same as C-filters-kernel_size-stride-padding, but use batch normalization. In detail, start with a convolutional layer without bias and activation, then add batch normalization layer, and finally ReLU activation. Example: CB-10-3-1-same
• M-kernel_size-stride: Add max pooling with specified size and stride. Example: M-3-2
• R-[layers]: Add a residual connection. The layers contain a specification of at least one convolutional layer (but not a recursive residual connection R). The input to the specified layers is then added to their output. Example: R-[C-16-3-1-same,C-16-3-1-same]
• F: Flatten inputs. Must appear exactly once in the architecture.
• D-hidden_layer_size: Add a dense layer with ReLU activation and specified size. Example: D-100

An example architecture might be --cnn=CB-16-5-2-same,M-3-2,F,D-100.

After a successful ReCodEx submission, you can try obtaining the best accuracy on MNIST and then advance to cifar_competition.

### cifar_competition

Deadline: Apr 07, 23:59  5-10 points

The goal of this assignment is to devise the best possible model for CIFAR-10. You can load the data using the cifar10.py module. Note that the test set is different than that of official CIFAR-10.

This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 60% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Note that my solutions usually need to achieve at least ~73% on the development set to score 60% on the test set.

### mnist_multiple

Deadline: Apr 14, 23:59  4 points

In this assignment you will implement a model with multiple inputs, multiple outputs, manual batch preparation, and manual evaluation. Start with the mnist_multiple.py template and:

• The goal is to create a model, which given two input MNIST images predicts if the digit on the first one is larger than on the second one.
• The model has three outputs:
• direct prediction of the required value,
• label prediction for the first image,
• label prediction for the second image.
• In addition to direct prediction, you can predict labels for both images and compare them -- an indirect prediction.
• You need to implement:
• the model, using multiple inputs, outputs, losses, and metrics;
• generation of two-image batches using regular MNIST batches,
• computation of direct and indirect prediction accuracy.

Deadline: Apr 14, 23:59  5-11 points

This assignment is a simple image segmentation task. The data for this task is available through the fashion_masks_data.py The inputs consist of 28×28 greyscale images of ten classes of clothing, while the outputs consist of the correct class and a pixel bit mask.

This is an open-data task, where you submit only the test set annotations together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

Performance is evaluated using mean IoU, where IoU for a single example is defined as an intersection of the gold and system mask divided by their union (assuming the predicted label is correct; if not, IoU is 0). The evaluation (using for example development data) can be performed by fashion_masks_eval.py script.

The task is a competition and the points will be awarded depending on your test set score. If your test set score surpasses 75%, you will be awarded 5 points; the rest 6 points will be distributed depending on relative ordering of your solutions. Note that quite a straightfoward model surpasses 80% on development set after an hour of computation (and 90% after several hours), so reaching 75% is not that difficult.

You may want to start with the fashion_masks.py template, which loads the data and generates test set annotations in the required format (one example per line containing space separated label and mask, the mask stored as zeros and ones, rows first).

### caltech42_competition

Deadline: Apr 21, 23:59 Apr 22, 23:59  5-10 points

The goal of this assignment is to try transfer learning approach to train image recognition on a small dataset with 42 classes. You can load the data using the caltech42.py module. In addition to the training data, you should use a MobileNet v2 pretrained network (details in caltech42_competition.py).

This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 94% test set accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions.

### sequence_classification

Deadline: Apr 21, 23:59 Apr 22, 23:59  6 points

The goal of this assignment is to introduce recurrent neural networks, manual TensorBoard log collection, and manual gradient clipping. Considering recurrent neural network, the assignment shows convergence speed and illustrates exploding gradient issue. The network should process sequences of 50 small integers and compute parity for each prefix of the sequence. The inputs are either 0/1, or vectors with one-hot representation of small integer.

Your goal is to modify the sequence_classification.py template and implement the following:

• Use specified RNN cell type (SimpleRNN, GRU and LSTM) and dimensionality.
• Process the sequence using the required RNN.
• Use additional hidden layer on the RNN outputs if requested.
• Implement gradient clipping if requested.

In addition to submitting the task in ReCodEx, please also run the following variations and observe the results in TensorBoard. Concentrate on the way how the RNNs converge, convergence speed, exploding gradient issues and how gradient clipping helps:

• --rnn_cell=SimpleRNN --sequence_dim=1, --rnn_cell=GRU --sequence_dim=1, --rnn_cell=LSTM --sequence_dim=1
• the same as above but with --sequence_dim=2
• the same as above but with --sequence_dim=10
• --rnn_cell=LSTM --hidden_layer=50 --rnn_cell_dim=30 --sequence_dim=30 and the same with --clip_gradient=1
• the same as above but with --rnn_cell=SimpleRNN
• the same as above but with --rnn_cell=GRU --hidden_layer=150

### tagger_we

Deadline: Apr 28, 23:59  3 points

In this assignment you will create a simple part-of-speech tagger. For training and evaluation, we will use Czech dataset containing tokenized sentences, each word annotated by gold lemma and part-of-speech tag. The morpho_dataset.py module (down)loads the dataset and can generate batches.

Your goal is to modify the tagger_we.py template and implement the following:

• Use specified RNN cell type (GRU and LSTM) and dimensionality.
• Create word embeddings for training vocabulary.
• Process the sentences using bidirectional RNN.
• Predict part-of-speech tags. Note that you need to properly handle sentences of different lengths in one batch using masking.

After submitting the task to ReCodEx, continue with tagger_cle_rnn assignment.

### tagger_cle_rnn

Deadline: Apr 28, 23:59  3 points

This task is a continuation of tagger_we assignment. Using the tagger_cle_rnn.py template, implement the following features in addition to tagger_we:

• Create character embeddings for training alphabet.
• Process unique words with a bidirectional character-level RNN, concatenating the results.
• Properly distribute the CLEs of unique words into the batches of sentences.
• Generate overall embeddings by concatenating word-level embeddings and CLEs.

Once submitted to ReCodEx, continue with tagger_cle_cnn assignment. Additionaly, you should experiment with the effect of CLEs compared to plain tagger_we, and the influence of their dimensionality. Note that tagger_we has by default word embeddings twice the size of word embeddings in tagger_cle_rnn.

### tagger_cle_cnn

Deadline: Apr 28, 23:59  2 points

This task is a continuation of tagger_cle_rnn assignment. Using the tagger_cle_cnn.py template, implement the following features compared to tagger_cle_rnn:

• Instead of using RNNs to generate character-level embeddings, process embedded unique words with 1D convolutional filters with kernel sizes of 2 to some given maximum. To obtain a fixed-size representation, perform global max-pooling over the whole word.

### speech_recognition

Deadline: Apr 28, 23:59  7-12 points

This assignment is a competition task in speech recognition area. Specifically, your goal is to predict a sequence of letters given a spoken utterance. We will be using TIMIT corpus, with input sound waves passed through the usual preprocessing – computing Mel-frequency cepstral coefficients (MFCCs). You can repeat exactly this preprocessing on a given audio using the timit_mfcc_preprocess.py script.

Because the data is not publicly available, you can download it only through ReCodEx. Please do not distribute it. To load the dataset using the timit_mfcc.py module.

This is an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. The evaluation is performed by computing edit distance to the gold letter sequence, normalized by its length (i.e., exactly as tf.edit_distance). Everyone who submits a solution which achieves at most 50% test set edit distance will get 7 points; the rest 5 points will be distributed depending on relative ordering of your solutions. An evaluation (using for example development data) can be performed by speech_recognition_eval.py.

• To perform speech recognition, you should use CTC loss for training and CTC beam search decoder for prediction. Both the CTC loss and CTC decoder employ sparse tensor – therefore, start by studying them.
• The basic architecture:
• converts target letters into sparse representation,
• use a bidirectional RNN and an output linear layer without activation,
• compute CTC loss (tf.nn.ctc_loss),
• if required, perform decoding by a CTC decoder (tf.nn.ctc_beam_search_decoder) and possibly evaluate results using normalized edit distance (tf.edit_distance).

### tagger_competition

Deadline: May 5, 23:59  5-13 points

In this assignment, you should extend tagger_we/tagger_cle_rnn/tagger_cle_cnn into a real-world Czech part-of-speech tagger. We will use Czech PDT dataset loadable using the morpho_dataset.py module. Note that the dataset contains more than 1500 unique POS tags and that the POS tags have a fixed structure of 15 positions (so it is possible to generate the POS tag characters independently).

You can use the following additional data in this assignment:

• You can use outputs of a morphological analyzer loadable with morpho_analyzer.py. If a word form in train, dev or test PDT data is known to the analyzer, all its (lemma, POS tag) pairs are returned.
• You can use any unannotated text data (Wikipedia, Czech National Corpus, …).

The assignment is again an open-data task, where you submit only the annotated test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. Everyone who submits a solution which achieves at least 92% label accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Lastly, 3 bonus points will be given to anyone surpassing pre-neural-network state-of-the-art of 95.89% from Spoustová et al., 2009. You can evaluate generated file against a golden text file using the morpho_evaluator.py module.

You can start with the tagger_competition.py template, which among others generates test set annotations in the required format.

### 3d_recognition

Deadline: May 5, 23:59  5-10 points

Your goal in this assignment is to perform 3D object recognition. The input is voxelized representation of an object, stored as a 3D grid of either empty or occupied voxels, and your goal is to classify the object into one of 10 classes. The data is available in two resolutions, either as 20×20×20 data or 32×32×32 data. To load the dataset, use the modelnet.py module.

The official dataset offers only train and test sets, with the test set having a different distributions of labels. Our dataset contains also a development set, which has nearly the same label distribution as the test set.

The assignment is again an open-data task, where you submit only the test set labels together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file.

The task is also a competition. Everyone who submits a solution which achieves at least 85% label accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions.

You can start with the 3d_recognition.py template, which among others generates test set annotations in the required format.

### lemmatizer_noattn

Deadline: May 12, 23:59  4 points

The goal of this assignment is to create a simple lemmatizer. For training and evaluation, we will use Czech dataset containing tokenized sentences, each word annotated by gold lemma and part-of-speech tag. The morpho_dataset.py module (down)loads the dataset and can generate batches.

Your goal is to modify the lemmatizer_noattn.py template and implement the following:

• Embed characters of source forms and run a bidirectional GRU encoder.
• Embed characters of target lemmas.
• Implement a training time decoder which uses gold target characters as inputs.
• Implement an inference time decoder which uses previous predictions as inputs.
• The initial state of both decoders is the output state of the corresponding GRU encoded form.

After submitting the task to ReCodEx, continue with lemmatizer_attn assignment.

### lemmatizer_attn

Deadline: May 12, 23:59  3 points

This task is a continuation of lemmatizer_noattn assignment. Using the lemmatizer_attn.py template, implement the following features in addition to lemmatizer_noattn:

• The bidirectional GRU encoder returns outputs for all input characters, not just the last.
• Implement attention in the decoders. Notably, project the encoder outputs and current state into same dimensionality vectors, apply non-linearity, and generate weights for every encoder output. Finally sum the encoder outputs using these weights and concatenate the computed attention to the decoder inputs.

Once submitted to ReCodEx, you should experiment with the effect of using the attention, and the influence of RNN dimensionality on network performance.

### lemmatizer_competition

Deadline: May 12, 23:59  5-13 points

In this assignment, you should extend lemmatizer_noattn/lemmatizer_attn into a real-world Czech lemmatizer. We will again use Czech PDT dataset loadable using the morpho_dataset.py module.

You can use the following additional data in this assignment:

• You can use outputs of a morphological analyzer loadable with morpho_analyzer.py. If a word form in train, dev or test PDT data is known to the analyzer, all its (lemma, POS tag) pairs are returned.
• You can use any unannotated text data (Wikipedia, Czech National Corpus, …).

The assignment is again an open-data task, where you submit only the annotated test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. Everyone who submits a solution which achieves at least 92% accuracy will get 5 points; the rest 5 points will be distributed depending on relative ordering of your solutions. Lastly, 3 bonus points will be given to anyone surpassing pre-neural-network state-of-the-art of 97.86%. You can evaluate generated file against a golden text file using the morpho_evaluator.py module.

You can start with the lemmatizer_competition.py template, which among others generates test set annotations in the required format.

### vae

Deadline: May 19, 23:59  3 points

In this assignment you will implement a simple Variational Autoencoder for three datasets in the MNIST format. Your goal is to modify the vae.py template and implement a VAE.

After submitting the assignment to ReCodEx, you can experiment with the three available datasets (mnist, mnist-fashion, and mnist-cifarcars) and different latent variable dimensionality (z_dim=2 and z_dim=100). The generated images are available in TensorBoard logs.

### gan

Deadline: May 19, 23:59  3 points

In this assignment you will implement a simple Generative Adversarion Network for three datasets in the MNIST format. Your goal is to modify the gan.py template and implement a GAN.

After submitting the assignment to ReCodEx, you can experiment with the three available datasets (mnist, mnist-fashion, and mnist-cifarcars) and maybe try different latent variable dimensionality. The generated images are available in TensorBoard logs.

You can also continue with dcgan assignment.

### dcgan

Deadline: May 19, 23:59  1 points

This task is a continuation of gan assignment, which you will modify to implement the Deep Convolutional GAN (DCGAN).

Start with the dcgan.py template and implement a DCGAN. Note that most of the TODO notes are from the gan assignment.

After submitting the assignment to ReCodEx, you can experiment with the three available datasets (mnist, mnist-fashion, and mnist-cifarcars). However, note that you will need a lot of computational power (preferably a GPU) to generate the images.

### nli_competition

Deadline: May 19, 23:59  6-10 points

In this competition you will be solving the Native Language Identification task. In that task, you get an English essay writen by a non-native individual and your goal is to identify their native language.

We will be using NLI Shared Task 2013 data, which contains documents in 11 languages. For each language, the train, development and test sets contain 900, 100 and 100 documents, respectively. Particularly interesting is the fact that humans are quite bad in this task (in a simplified settings, human professionals achieve 40-50% accuracy), while machine learning models can achive high performance. Notably, the 2013 shared tasks winners achieved 83.6% accuracy, while current state-of-the-art is at least 87.1% (Malmasi and Dras, 2017).

Because the data is not publicly available, you can download it only through ReCodEx. Please do not distribute it. To load the dataset, use nli_dataset.py script.

The assignment is again an open-data task, where you submit only the annotated test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. If your test set accuracy surpasses 60%, you will be awarded 6 points; the rest 4 points will be distributed depending on relative ordering of your solutions.

You can start with the nli_competition.py template, which loads the data and generates predictions in the required format (language of each essay on a line).

### omr_competition

Deadline: May 26, 23:59  7-15 points

You should implement optical music recognition in your final competition assignment. The inputs are PNG images of monophonic scores starting with a clef, key signature, and a time signature, followed by several staves. The dataset is loadable using the omr_dataset.py module and is downloaded automatically if missing (note that is has 185MB). No other data or pretrained models are allowed for training.

The assignment is again an open-data task, where you submit only the annotated test set together with the training script (which will not be executed, it will be only used to understand the approach you took, and to indicate teams). Explicitly, submit exactly one .txt file and at least one .py file. Note that all .zip files you submit will be extracted first.

The task is also a competition. The evaluation is performed by computing edit distance to the gold mark sequence, normalized by its length (i.e., exactly as tf.edit_distance). Everyone who submits a solution which achieves at most 10% test set edit distance will get 7 points; the rest 4 points will be distributed depending on relative ordering of your solutions. Furthermore, 4 bonus points will be given to anyone surpassing current state-of-the-art of 0.80%. An evaluation (using for example development data) can be performed by speech_recognition_eval.py.

You can start with the omr_competition.py template, which among others generates test set annotations in the required format.

### monte_carlo

Deadline: May 26, 23:59  2 points

Solve the discretized CartPole-v1 environment environment from the OpenAI Gym using the Monte Carlo reinforcement learning algorithm.

Use the supplied cart_pole_evaluator.py module (depending on gym_evaluator.py) to interact with the discretized environment. The environment has the following methods and properties:

• states: number of states of the environment
• actions: number of actions of the environment
• episode: number of the current episode (zero-based)
• reset(start_evaluate=False) → new_state: starts a new episode
• step(action) → new_state, reward, done, info: perform the chosen action in the environment, returning the new state, obtained reward, a boolean flag indicating an end of episode, and additional environment-specific information
• render(): render current environment state

Once you finish training (which you indicate by passing start_evaluate=True to reset), your goal is to reach an average return of 475 during 100 evaluation episodes. Note that the environment prints your 100-episode average return each 10 episodes even during training.

You can start with the monte_carlo.py template, which parses several useful parameters, creates the environment and illustrates the overall usage.

During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.

### reinforce

Deadline: May 26, 23:59  2 points

Solve the continuous CartPole-v1 environment environment from the OpenAI Gym using the REINFORCE algorithm.

The supplied cart_pole_evaluator.py module (depending on gym_evaluator.py) can create a continuous environment using environment(discrete=False). The continuous environment is very similar to the discrete environment, except that the states are vectors of real-valued observations with shape environment.state_shape.

Your goal is to reach an average return of 475 during 100 evaluation episodes. Start with the reinforce.py template.

During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.

### reinforce_baseline

Deadline: May 26, 23:59  2 points

This is a continuation of reinforce assignment.

Using the reinforce_baseline.py template, solve the CartPole-v1 environment environment using the REINFORCE with baseline algorithm.

Using a baseline lowers the variance of the value function gradient estimator, which allows faster training and decreases sensitivity to hyperparameter values. To reflect this effect in ReCodEx, note that the evaluation phase will automatically start after 200 episodes. Using only 200 episodes for training in this setting is probably too little for the REINFORCE algorithm, but suffices for the variant with a baseline.

Your goal is to reach an average return of 475 during 100 evaluation episodes.

During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.

### reinforce_pixels

Deadline: May 26, 23:59  2 points

This is a continuation of reinforce or reinforce_baseline assignment.

The supplied cart_pole_pixels_evaluator.py module (depending on gym_evaluator.py) generates a pixel representation of the CartPole environment as an $80×80$ image with three channels, with each channel representing one time step (i.e., the current observation and the two previous ones).

To pass the assignment, you need to reach an average return of 250 during 100 evaluation episodes. During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.

• Training Neural Network
Assume the artificial neural network on the right, with mean square error loss and gold output of 3. Compute the values of all weights $w_i$ after performing an SGD update with learning rate 0.1.

Different networks architectures, activation functions (tanh, sigmoid, softmax) and losses (MSE, NLL) may appear in the exam.

• Maximum Likelihood Estimation
Formulate maximum likelihood estimator for neural network parameters and derive the following two losses:

• NLL (negative log likelihood) loss for networks returning a probability distribution
• MSE (mean square error) loss for networks returning a real number with a normal distribution with a fixed variance
• Backpropagation Algorithm, SGD with Momentum
Write down the backpropagation algorithm. Then, write down the SGD algorithm with momentum. Finally, formulate SGD with Nestorov momentum and explain the difference to SGD with regular momentum.

Write down the AdaGrad algorithm and show that it tends to internally decay learning rate by a factor of $1/\sqrt{t}$ in step $t$. Furthermore, write down RMSProp algorithm and compare it to Adagrad.

Write down the Adam algorithm and explain the bias-correction terms $(1-\beta^t)$.

• Regularization
Define overfitting and sketch what a regularization is. Then describe basic regularization methods like early stopping, L2 and L1 regularization, dataset augmentation, ensembling and label smoothing.

• Dropout
Describe the dropout method and write down exactly how is it used during training and during inference. Then explain why it cannot be used on RNN state, describe the variational dropout variant, and also describe layer normalization.

• Network Convergence
Describe factors influencing network convergence, namely:

• Parameter initialization strategies (explain also why batch normalization helps with this issue).
• Problems with saturating non-linearities (and again, why batch normalization helps; you can also discuss why NLL helps with saturating non-linearities on the output layer).
• Gradient clipping (and the difference between clipping individual gradient elements or the gradient as a whole).
• Convolution
Write down equations of how convolution of a given image is computed. Assume the input is an image $I$ of size $H \times W$ with $C$ channels, the kernel $K$ has size $N \times M$, the stride is $T \times S$, the operation performed is n fact cross-correlation (as usual in convolutional neural networks) and that $O$ output channels are computed. Explain both $\textit{SAME}$ and $\textit{VALID}$ padding schemes and write down output size of the operation for both these padding schemes.

• Batch Normalization
Describe the batch normalization method and explain how it is used during training and during inference. Explicitly write over what is being normalized in case of fully connected layers, and in case of convolutional layers. Compare batch normalization to layer normalization.

• VGG and ResNet
Describe overall architecture of VGG and ResNet (you do not need to remember exact number of layers/filters, but you should know when a BatchNorm is executed, when ReLU, and how residual connections work when the number of channels increases). Then describe two ResNet extensions (WideNet, DenseNet, PyramidNet, ResNeXt).

• Object Detection and Segmentation
Describe object detection and image segmentation tasks, and sketch Fast-RCNN, Faster-RCNN and Mask-RCNN architectures. Notably, show what the overall architectures of the networks are, explain the RoI-pooling and RoI-align layers, show how the network predicts RoI sizes, how do the losses looks like, how are RoI chosen during training and prediction, and what region proposal network does.

• Object Detection
Describe object detection task, and sketch Fast-RCNN, Faster-RCNN and RetinaNet architectures. Notably, show the overall architectures of the networks, explain the RoI-pooling layer, show how the network predicts RoI sizes, how do the losses looks like (classification loss, boundary prediction loss, focal loss for RetinaNet), and what a feature pyramid network is.

• LSTM
Write down how the Long Short-Term Memory cell operates.

• GRU and Highway Networks
Show a basic RNN cell (using just one hidden layer) and then write down how it is extended using gating into the Gated Recurrent Unit. Finally, describe highway networks and compare them to RNN.

• Sequence classification and CRF
Describe how RNNs, bidirectional RNNs and multi-layer RNNs can be used to classify every element of a given sequence (i.e., what the architecture of a tagger might be; include also residual connections and suitable places for dropout layers). Then, explain how a CRF layer works, define score computation for a given sequence of inputs and sequence of labels, describe the loss computation during training, and sketch the inference algorithm.

• CTC Loss
Describe CTC loss and the whole settings which can be solved utilizing CTC loss. Then show how CTC loss can be computed. Finally, describe greedy and beam search CTC decoding.

• Word2vec and Hierarchical and Negative Sampling
Explain how can word embeddings be precomputed using the CBOW and Skip-gram models. First start with the variants where full softmax is performed, and then describe how hierarchical softmax and negative sampling is used to speedup training of word embeddings.

• Character-level word embeddings
Describe why are character-level word embeddings useful. Then describe the two following methods:

• RNN: using bidirectional recurrent neural networks
• CNN: describe how convolutional networks (CNNs) can be used to compute character-level word embeddings. Write down the exact equation computing the embedding, assuming that the input word consists of characters $\{x_1, \ldots, x_N\}$ represented by embeddings $\{e_1, \ldots, e_N\}$ for $e_i \in \mathbb R^D$, and we use $F$ filters of widths $w_1, \ldots, w_F$. Also explicitly count the number of parameters.
• Neural Machine Translation and BPE
Draw/write how an encoder-decoder architecture is used for machine translation, both during training and during inference, including attention. Furthermore, elaborate on how subword units are used to reduce out-of-vocabulary problem and sketch BPE algorithm for constructing fixed number of subword units.

• Variational Autoencoders
Describe deep generative modelling using variational autoencoders – show VAE architecture, devise training algorithm, write training loss, and propose sampling procedure.

Describe deep generative modelling using generative adversarial networks -- show GAN architecture and describe training procedure and training loss. Mention also CGAN (conditional GAN) and sketch generator and discriminator architecture in a DCGAN.

• Speech Synthesis
Describe the WaveNet network (what a dilated convolution and gated activations are, how the residual block looks like, what the overall architecture is, and how global and local conditioning work). Discuss parallelizability of training and inference, show how Parallel WaveNet can speedup inference, and sketch how it is trained.

• Reinforcement learning
Describe the general reinforcement learning settings and describe the Monte Carlo algorithm. Then, formulate the policy gradient theorem (proof not needed), write down the REINFORCE algorithm, the REINFORCE with baseline algorithm, and sketch now it can be used to design the NasNet.

• Transformer
Describe Transformer architecture, namely the self-attention layer, multi-head self-attention layer, and overall architecture of an encoder and a decoder. Also discuss the positional embeddings.

• Neural Turing Machines
Sketch an overall architecture of a Neural Turing Machine with an LSTM controller, assuming $R$ reading heads and one write head. Describe the addressing mechanism (content addressing and its combination with previous weights, shifts, and sharpening), and reading and writing operations.