人工智能 公开课

Stanford

CS 20SI: Tensorflow for Deep Learning Research

introduce： Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.

This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.

URL： http://web.stanford.edu/class/cs20si/index.html

CS229: Machine Learning

introduce: This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
URL: http://cs229.stanford.edu

CS231n: Convolutional Neural Networks for Visual Recognition

introduce: This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.
URL: http://cs231n.github.io

CS224d:Deep Learning for Natural Language Processing

introduce: The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models.
URL: http://cs224d.stanford.edu

MIT

6.S094: Deep Learning for Self-Driving Cars

introduce: This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.
URL: http://selfdrivingcars.mit.edu

UC Berkeley

CS 294: Deep Reinforcement Learning, Spring 2017

introduce:
URL: http://rll.berkeley.edu/deeprlcourse/

Oxford

Deep Learning for Natural Language Processing

*introduce:*This will be an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We will introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course will cover a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics will be organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware will also be discussed.
URL: https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/

Udacity

Intro to Parallel Programming

URL: https://classroom.udacity.com/courses/cs344

Self-Driving Car Engineer

URL: https://www.udacity.com/drive

Artificial Intelligence Engineer

URL: https://www.udacity.com/ai

Robotics

URL: https://www.udacity.com/robotics