Initialization

Neural network

Python

NumPy

Logistic Regression

Batch Normalization

Generative adversarial network (GAN)

Shallow Neural Network

Deep Neural Network

Optimization Algorithms

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

Recurrent neural network (RNN)

Long Short-Term Memory (LSTM)

Natural Language Processing (NLP)

Word Embeddings

Attention (machine learning)

Transformer (machine learning model)

Deep Reinforcement Learning

Neural Machine Translation

Trigger Word Detection

Transfer learning

Sequence-to-sequence (Seq2Seq)

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

Deep Learning

Types

struct (C programming language)

References

Streams

Containers

Iterators

Pointer (computer programming)

Class (computer programming)

Template Classes

Const Correctness

Template Functions

Function (computer programming)

Lambda

Operators

Special Member Functions

Move Semantics

`std::optional`

Type Safety

Resource acquisition is initialization (RAII)

Smart pointer

Building C++ Projects

CS 106L Standard C++ Programming Stanford University Winter 2023 This is a companion course to CS106B/CS106X, diving into the modern C++ language. It covers some of the most exciting features of C++, including modern patterns that give it beauty and power. It is an intermediate-level course assuming familiarity with basic programming concepts. **CS 106L** is a companion class to CS106B/CS106X that explores the modern C++ language in depth. We'll cover some of the most exciting features of C++, including modern patterns that give it beauty and power.   

CS 106L Standard C++ Programming

Computer Programming

Initialization (programming)

Initialization

2 courses cover this concept

CS 230 Deep Learning

CS 106L Standard C++ Programming