Variational autoencoder (VAE)

Connectionist Machines

McCullough and Pitt model

Hebb’s learning rule

Rosenblatt’s perceptron

Universal Approximator

Neural network

Perceptron learning rule

Empirical risk minimization

Optimization

Gradient descent

Back-propagation

Momentum

Nestorov

Convergence

Learning Rates

Optimization Algorithms

RMSProp

Stochastic gradient descent (SGD)

Acceleration

Overfitting

Regularization (mathematics)

Convolutional neural network (CNN)

Translation Invariance

Cascade Correlation Filters

Recurrent neural network (RNN)

Bidirectional RNNs

Sequence Prediction

Long Short-Term Memory (LSTM)

Connectionist Temporal Classification (CTC)

Representations

Autoencoders

Hopfield Networks

Boltzmann Machines

Normalizing Flows

Generative adversarial network (GAN)

Reinforcement learning (RL)

Multi-layer Perceptron

Sequence-to-sequence (Seq2Seq)

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

Deep Learning

K-Nearest Neighbors

Linear Classifiers

Adam

Learning Rate Schedules

Support Vector Machine (SVM)

Softmax Loss

Backpropagation

Higher-level representations

Image Features

Pooling

Batch Normalization

Transfer learning

AlexNet, VGG, GoogLeNet, ResNet

Activation Functions

Data Processing

Weight initialization

Hyperparameter Tuning

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)

Language Modeling

Image Captioning

Object Detection

Self-Attention

Transformer (machine learning model)

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

Computer Vision

Classification

Linear regression

Training/Validation/Testing

Perceptron

MNIST

Loss function

Automatic differentiation

Matrix representation of Neural Networks

Graphics Processor (GPU)

TensorFlow

Multilayer perceptron

Language Models

Word Embeddings

Feedforward neural network

Machine Translation

Attention (machine learning)

Scaling Deep Learning Systems

Interpretation of Neural Networks

Deepfake

Value iteration

Deep Q-learning

Policy Gradient Methods

Actor-Critic Methods

Graph neural network (GNN)

CSCI 1470/2470 Deep Learning Brown University Spring 2022 Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts. Over the past few years, Deep Learning has become a popular area, with deep neural network methods obtaining state-of-the-art results on applications in computer vision (Self-Driving Cars), natural language processing (Google Translate), and reinforcement learning (AlphaGo). These technologies are having transformative effects on our society, including some undesirable ones (e.g. deep fakes).

This course is there to give students a practical understanding of how Deep Learning works, how to implement neural networks, and how to apply them ethically. We introduce students to the core concepts of deep neural networks and survey the techniques used to model complex processes within the contexts of computer vision and natural language processing.

Throughout the course, we emphasize and require students to think critically about potential ethical pitfalls that can result from mis-application of these powerful models. The course is taught using the Tensorflow deep learning framework.
 By the end of this course, you will be able to:

- Learn about the fundamental algorithms that underly all modern deep learning models.
- Implement different types of deep learning models in Tensorflow.
- Think critically about using a deep learning model for a task and its potential societal impact.
- Collaborate with classmates on a team project to apply deep learning models to task of your
choice.
- Communicate your findings (both positive and negative results are encouraged) through pre-
sentations. - A basic programming course: (CSCI 0150, 0170 or 0190)
- A linear algebra course: (CSCI 0530, MATH 0520 or 0540)
- A stats / probability course: (CSCI 0220, 1450, 0450, MATH 1610, APMA 1650 or 1655)

Exceptions may be possible for those missing one of these prerequisites if (a) the student has taken
another course which covers similar material, or if (b) the student will be concurrently taking the
prerequisite. If either of these situations applies to you, use the “Request Override” feature in
Courses@Brown to request an override code (and explain why you believe your situation merits
one). ### Textbook

None required. Students are encouraged to refer to the following textbook, which is available online:

- [Deep Learning](https://www.deeplearningbook.org/), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

CSCI 1470/2470 Deep Learning