Sequence-to-sequence (Seq2Seq)

Convolutional neural network (CNN)

Computer Vision

Graph neural network (GNN)

Recurrent neural network (RNN)

Long Short-Term Memory (LSTM)

Attention (machine learning)

Self-supervision

Autoencoders

Transformer (machine learning model)

Meta-learning

Fine-tuning

Generative Models

Transfer learning

CS 182/282A: Deep Neural Networks UC Berkeley Fall 2022 An advanced course dealing with deep networks in the fields of computer vision, language technology, robotics, and control. It delves into the themes of deep learning, model families, and real-world applications. A strong mathematical background in calculus, linear algebra, probability, optimization, and statistical learning is necessary. Deep Networks have revolutionized computer vision, language technology, robotics and control. They have a growing impact in many other areas of science and engineering, and increasingly, on commerce and society. They do not however, follow any currently known compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This is a fancy way of saying “we don’t understand this stuff nearly well enough, but we have no choice but to muddle through anyway.” This course attempts to cover that ground and show you how to muddle through even as we aspire to do more. The goal is to teach a principled course in Deep Learning that serves the diverse needs of our students while also codifying the present understanding of the field. Topics covered may include, but are not limited to:
- Underlying themes of deep learning, including building beyond underlying machine learning concepts like supervised vs unsupervised learning, regression and classification, training/validation/testing, distribution shifts, regularization, the fundamental underlying tradeoffs;
- Defining and training neural networks: features, computation graphs, backpropagation, iterative optimization (SGD, Newton’s Method, Momentum, RMSProp, AdaGrad, Adam), strategies for training (explicit and implicit regularization, batch and layer normalization, weight initialization, gradient clipping, ensembles, dropout), hyperparameter tuning
- Families of contemporary models: fully connected networks, convolutional nets, graph neural nets, recurrent neural nets, transformers
- Problems that utilize neural networks: computer vision, natural language processing, generative models, and others.
- Conducting experiments in a systematic, repeatable way, leveraging and presenting data from experiments to reason about network behavior. This is a graduate-level/advanced undergraduate course about a particular approach to information processing using (simulated) analog circuits where the desired circuit behavior is tuned via optimization involving data since we have no idea how to do hand-tuning at scale. Probabilistic frames are useful to understand what is going on, as well as how we navigate certain design choices. Overall, we expect students to have a strong mathematical background in calculus, linear algebra, probability, optimization, and statistical learning. Berkeley undergraduate courses that can help build maturity include:
- **Calculus**: Math 53 (note: Math 1B or AP Math is not enough)
- **Linear Algebra and Optimization**: EECS 16B and EECS 127/227A is ideal, but EECS 16B alone might be enough if students have complete mastery of that material. Math 110 is also helpful. (note: Math 54 or EECS 16A is required as a minimum, but are not nearly enough.)
- **Probability**: EECS 126, Stat 134, or Stat 140 (note: CS 70 is required at a minimum, but might not be enough for everyone)
- **Statistical Learning**: CS 189/289A or Stat 154 (note: Data 102 is insufficient, even when combined with Data 100.)

*Math 53 and EECS 126 and EECS 127 and CS 189 is the recommended background.*

Prerequisites are not enforced for enrollment, but we encourage you to consider taking some of the classes listed above and save this course for a future semester if you feel shaky on the fundamentals.

The course assumes familiarity with programming in a high-level language with data structures. Homeworks and projects will typically use Python. We encourage you to check out this [tutorial](https://docs.python.org/3/tutorial/) if you haven’t used it before. Students who have taken Berkeley courses like CS 61A and CS 61B are well-prepared for the programming components of the class.

We do not have the staff bandwidth to help students with material that they should have understood before taking this course. If you choose to proceed with this course, you are accepting full responsibility to teach yourself anything in your background that you are missing. We will not be slowing down to accommodate you, and questions pertaining to background material will always have the lowest priority in all course forums. 

CS 182/282A: Deep Neural Networks

Deep Learning

Text classification

Language Modeling

Vector Embeddings

PyTorch

Finite-state machine

Sequence Labelling

Machine Translation

Morphology

Syntax

Conditional Random Fields (CRF)

Semantics

Linguistic Structure Prediction

Neural Machine Translation

Multinomial Logistic Regression

Weighted Finite-State Transducers

CSE 447 and 517 Natural Language Processing University of Washington Winter 2022 This course provides a comprehensive overview of Natural Language Processing (NLP), including core components like text classification, machine translation, and syntax analysis. It offers two project types: implementation problem-solving for CSE 447, and reproducing experiments from recent NLP papers for CSE 517.  Natural language processing (NLP) seeks to endow computers with the ability to intelligently process human language. NLP components are used in conversational agents and other systems that engage in dialogue with humans, automatic translation between human languages, automatic answering of questions using large text collections, the extraction of structured information from text, tools that help human authors, and many, many more. This course will teach you the fundamental ideas used in key NLP components. It is organized into several “greatest hits” topics, each with a more-or-less self-contained lecture and associated readings, problems, and implementation exercises.  The courses are similar in breadth and use the same lecture content. The projects are quite different; 447’s project is a predefined implementation problem that gives teams freedom in developing a solution. It is designed to encourage iterative improvement and an understanding of inherent tradeoffs in building an NLP system. 517’s project is more research-oriented; it asks teams to reproduce experiments in recently published NLP papers. Teams have great flexibility in the choice of a paper to reproduce.

Additionally, there will be differences in the assignments.

CSE 447 and 517 Natural Language Processing

Natural Language Processing

Neural network

Python

NumPy

Logistic Regression

Batch Normalization

Generative adversarial network (GAN)

Shallow Neural Network

Deep Neural Network

Optimization Algorithms

Initialization

Regularization (mathematics)

Gradient Checking

Optimization

Hyperparameter Tuning

TensorFlow

Deep Convolutional Models

Residual Networks

Detection Algorithms

Face Recognition

Neural Style Transfer

You Only Look Once (YOLO)

ConvNets

Image Segmentation

U-Net

Natural Language Processing (NLP)

Word Embeddings

Deep Reinforcement Learning

Trigger Word Detection

CS 230 Deep Learning Stanford University Fall 2022 An in-depth course focused on building neural networks and leading successful machine learning projects. It covers Convolutional Networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. Students are expected to have basic computer science skills, probability theory knowledge, and linear algebra familiarity.  Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.  Students are expected to have the following background, and are invited to take the [Workera](http://www.workera.ai/) technical assessments prior to the class to self-assess themselves prior to taking the class:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. This corresponds to a Developing level (or badge) in the “Algorithmic Coding” section on [Workera](http://www.workera.ai/).
- Familiarity with the probability theory (CS 109 or STATS 116), which students can assess by taking the “Data Science” section on [Workera](http://www.workera.ai/).
- Familiarity with linear algebra (MATH 51), which students can assess by taking the “Mathematics” section on [Workera](http://www.workera.ai/). 

CS 230 Deep Learning

Word Vectors

Word Window Classification

Language Models

Backpropagation

Dependency Parsing

Subword Models

Self-Attention

Natural Language Generation

Pretraining

Reinforcement Learning from Human Feedback (RLHF)

Question Answering (QA)

Tree Recursive Neural Networks

Constituency Parsing

Code Generation

Multimodal Deep Learning

Coreference Resolution

CS 224N: Natural Language Processing with Deep Learning Stanford University Winter 2023 CS 224N provides an in-depth introduction to neural networks for NLP, focusing on end-to-end neural models. The course covers topics such as word vectors, recurrent neural networks, and transformer models, among others. Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural networks for NLP. ## What is this course about?

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.

>  “Take it. CS221 taught me algorithms. CS229 taught me math. CS224N taught me how to write machine learning models.” – A CS224N student on Carta - **Proficiency in Python**
    All class assignments will be in Python (using NumPy and PyTorch). If you need to remind yourself of Python, or you're not very familiar with NumPy, you can come to the Python review session in week 1 (listed in the schedule). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine.
- **College Calculus, Linear Algebra** (e.g. MATH 51, CME 100)
    You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.

- **Basic Probability and Statistics** (e.g. CS 109 or equivalent)
    You should know the basics of probabilities, gaussian distributions, mean, standard deviation, etc.

- **Foundations of Machine Learning** (e.g. CS221, CS229, CS230, or CS124)
    We will be formulating cost functions, taking derivatives and performing optimization with gradient descent. If you already have basic machine learning and/or deep learning knowledge, the course will be easier; however it is possible to take CS224n without it. There are many introductions to ML, in webpage, book, and video form. One approachable introduction is Hal Daumé’s in-progress A Course in Machine Learning. Reading the first 5 chapters of that book would be good background. Knowing the first 7 chapters would be even better! ### Reference Texts

The following texts are useful, but none are required. All of them can be read free online.

- Dan Jurafsky and James H. Martin. [Speech and Language Processing (3rd ed. draft)](https://web.stanford.edu/~jurafsky/slp3/)
- Jacob Eisenstein. [Natural Language Processing](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)
- Yoav Goldberg. [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf)
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. [Deep Learning](http://www.deeplearningbook.org/)
- Delip Rao and Brian McMahan. [Natural Language Processing with PyTorch](http://library.stanford.edu/sfx?genre=book&atitle=&title=Natural%20language%20processing%20with%20PyTorch%20:%20build%20intelligent%20language%20applications%20using%20deep%20learning%20/&isbn=9781491978207&volume=&issue=&date=20190101&aulast=Rao,%20Delip,,%20author.&spage=&pages=&sid=EBSCO:VLeBooks:edsvle.AH35866319) (requires Stanford login).
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. [Natural Language Processing with Transformers](https://transformersbook.com/)

If you have no background in neural networks but would like to take the course anyway, you might well find one of these books helpful to give you more background:

- Michael A. Nielsen. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
- Eugene Charniak. [Introduction to Deep Learning](https://mitpress.mit.edu/books/introduction-deep-learning)

CS 224N: Natural Language Processing with Deep Learning

Connectionist Machines

McCullough and Pitt model

Hebb’s learning rule

Rosenblatt’s perceptron

Universal Approximator

Perceptron learning rule

Empirical risk minimization

Gradient descent

Back-propagation

Momentum

Nestorov

Convergence

Learning Rates

RMSProp

Stochastic gradient descent (SGD)

Acceleration

Overfitting

Translation Invariance

Cascade Correlation Filters

Bidirectional RNNs

Sequence Prediction

Connectionist Temporal Classification (CTC)

Representations

Hopfield Networks

Boltzmann Machines

Normalizing Flows

Variational autoencoder (VAE)

Reinforcement learning (RL)

Multi-layer Perceptron

AdaGrad

11-785 Introduction to Deep Learning Carnegie Mellon University Spring 2020 This course provides a comprehensive introduction to deep learning, starting from foundational concepts and moving towards complex topics such as sequence-to-sequence models. Students gain hands-on experience with PyTorch and can fine-tune models through practical assignments. A basic understanding of calculus, linear algebra, and Python programming is required. “Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market.

In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.

If you are only interested in the lectures, you can watch them on the YouTube channel listed below.

### Course description from student point of view

The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.  1. We will be using one of several toolkits (the primary toolkit for recitations/instruction is PyTorch). The toolkits are largely programmed in Python. You will need to be able to program in at least one of these languages. Alternately, you will be responsible for finding and learning a toolkit that requires programming in a language you are comfortable with,
1. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability. ### Course description from student point of view

The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.

11-785 Introduction to Deep Learning

K-Nearest Neighbors

Linear Classifiers

Adam

Learning Rate Schedules

Support Vector Machine (SVM)

Softmax Loss

Higher-level representations

Image Features

Pooling

AlexNet, VGG, GoogLeNet, ResNet

Activation Functions

Data Processing

Weight initialization

Data Augmentation

Feature visualization and inversion

Adversarial Examples

DeepDream and Style Transfer

Single-stage detectors

Two-stage detectors

Semantic/Instance/Panoptic segmentation

Gated recurrent unit (GRU)

Image Captioning

Object Detection

Video classification

3D CNNs

Two-stream networks

Multimodal Video Understanding

Supervised learning

Unsupervised learning

Pixel RNN, Pixel CNN

Pretext Tasks

Contrastive Learning

Multisensory Supervision

Optical Flow

Stereo Vision

3D Shape Representations

Shape Reconstruction

Neural Implicit Representations

Convolution

CS231n: Deep Learning for Computer Vision Stanford University Spring 2022 This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics. Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.  - Proficiency in Python  
    All class assignments will be in Python (and use numpy) (we provide a tutorial [here](http://cs231n.github.io/python-numpy-tutorial/) for those who aren't as familiar with Python). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.
- College Calculus, Linear Algebra (e.g. MATH 19 or 41, MATH 51)  
     You should be comfortable taking derivatives and understanding matrix vector operations and notation.
- Basic Probability and Statistics (e.g. CS 109 or other stats course)  
    You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc. 

CS231n: Deep Learning for Computer Vision

Seq2seq