Graduate Computer Science
Use this Library Research Guide to find resources on computer science scholarly articles, books, websites, and more.
AI News
Loading ...
-
The Batch by Andrew Ng This link opens in a new windowWeekly artificial intelligence and machine learning updates. DeepLearning.AI was founded in 2017 by machine learning and education pioneer Andrew Ng.
-
Google AI Blog This link opens in a new windowInsights and projects from Google Research's AI team.
-
Import AI by Jack Clark This link opens in a new windowWeekly updates on artificial intelligence research and policy from Jack Clark, co-founder of Anthropic
-
MIT Technology Review: AI Section This link opens in a new windowFocuses on the latest advancements in artificial intelligence, including developments in machine learning, reasoning, and intelligent action.
-
OpenAI Blog This link opens in a new windowUpdates on research, tools, and advancements from OpenAI.
-
Towards Data Science This link opens in a new windowArticles on artificial intelligence, machine learning, and data science applications and tutorials.
-
The Gradient This link opens in a new windowArtificial intelligence research summaries, essays, and discussions written by the Stanford AI Lab.
AI Tools and Tutorials
This is a sample of tools and notebooks available.
-
Google Colab This link opens in a new windowFree cloud-based Python environment for machine learning experiments
-
TensorFlow Tutorials This link opens in a new windowHands-on tutorials for building artificial intelligence and machine learning models
-
PyTorch Tutorials This link opens in a new windowGuides for implementing deep learning models
-
Fast.ai This link opens in a new windowPractical machine learning courses and tools for beginners and practitioners
-
Introduction to Artificial Intelligence From Stanford This link opens in a new windowStanford's CS221 course lectures
-
Deep Learning Specialization by Andrew Ng This link opens in a new windowComprehensive deep learning course on Coursera
-
Machine Learning with Python by freeCodeCamp This link opens in a new windowBeginner-friendly Python machine learning tutorial
-
DALLE-3 Masterclass: Everything You Didn’t Know (Complete DALLE 3 Tutorial) This link opens in a new windowComprehensive DALL-E 3 tutorial covering AI image prompting, DALL-E Vision, image re-imagination and analysis, using GPTs with Dalle-3, and more
-
Machine Learning Crash Course From Google This link opens in a new windowA free machine learning course with interactive lessons
-
"The AI Revolution: Road to Superintelligence" From Wait But Why This link opens in a new windowA long-form article with visuals explaining AI concepts
-
AI Alignment Problem This link opens in a new windowThis is a podcast featuring Brian Christian discussing the difficulties in making artificial intelligence match our human values. Brian is the bestselling author of "Algorithms to Live By" and "The Alignment Problem."
-
"The Ethics of Artificial Intelligence: Understanding Power and Responsibility" from Medium This link opens in a new windowArticle on AI's ethical implications
-
Distill Journal This link opens in a new windowThis is a peer-reviewed journal of interactive articles explaining deep learning concepts that operated from 2016 to 2021.
-
"A Beginner's Guide to AI and Machine Learning" from IBM Developer This link opens in a new windowThis page explains artificial intelligence and machine learning concepts with beginner-friendly examples.
Explore eBooks & Library Databases
-
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
by
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Call Number: Available OnlineISBN: 9781098125974Publication Date: 2022
-
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville This link opens in a new windowThe Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. Free online version of book. MIT Press, 2016
-
Neural Networks and Deep Learning by Michael Nielsen This link opens in a new windowNeural Networks and Deep Learning is a free online book. The book will teach you about:
Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
Deep learning, a powerful set of techniques for learning in neural networks
Determination Press, 2015.
This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. -
"Dive into Deep Learning" by Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J. This link opens in a new windowThis book's content makes deep learning approachable, teaching you the concepts, the context, and the code. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. 2023
-
Think Stats, 3rd edition By Allen B. Downey This link opens in a new windowThink Stats is an introduction to Probability and Statistics for Python programmers. If you have basic skills in Python, you can use them to learn concepts in probability and statistics and practical skills for working with data. 2023, This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .