This section is by no means comprehensive yet, and I intend to expand it more. 2. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. Yoshua Bengio, Aaron Courville, Pascal Vincent, Representation Learning: A Review and New Perspectives, Arxiv, 2012. Hamid Palangi, hpalangi@microsoft.com Here is my reading list for deep learning. Source : NVIDIA. In this paper we outline our approach to incrementally building complete intelligent Creatures. Readings. This is a curated list of resources for picking up deep learning for business. The book will teach you about: Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, In preparation.. Survey Papers on Deep Learning. Deep Learning in C# - Free source code and tutorials for Software developers and Architects. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. My Reading List for Deep Learning! A good book to accompany Andrew Ng’s course is François Chollet’s Deep Learning with Python. While most people might dismiss as this “too theoretical”, there are important implications to be learned by understanding how neural networks retain what information. Deep Learning Reading List: The Essentials, Deep Learning (Adaptive Computation and Machine Learning Series), Ian Goodfellow and Yoshua Benigo, Hands-On Machine Learning with Scikit-Learn & TensorFlow , Aurelien Geron, TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python, Antonio Gulli, Amita Kapoor, Deep Learning: A Practitioner's Approach, Adam Gibson and Josh Patterson, Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Neural Networks and Deep Learning,  Antonio Gulli and Sujit Pal, Deep Learning with Python, Francois Chollet, Artificial Intelligence – A Modern Approach and Machine Learning – An Algorithmic Perspective, Stephen Marsland, 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric, Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen, Imagination Machines: A New Challenge for Artificial Intelligence, Sridhar Mahadevan, Intelligence without representation, Rodney A. Brooks, Register for any upcoming RE•WORK Summit with the code SUMMER, Change Detection and ATR using Similarity Search in Satellites, Fairness in Machine Learning - The Case of Juvenile Criminal Justice in Catalonia, 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. By Matthew Mayo , KDnuggets. Deep Learning Resources for Beginners (Updated Mid 2018 - Outdated! Sat by the pool, or in your garden with a book in one hand and drink in the other, but this year we’re making it our mission at RE•WORK to keep reading throughout the winter months, and we’d like you to join us. However, I am a firm believer of developing a good foundation: given how expansive the current state of deep learning is, if you’re starting from scratch there is a lot you have to catch up with. Deep Learning Reading List. This Github repository provides paper highlights up until a few years ago, and covers the more seminal papers for a lot of the current state-of-the-art. Neural Networks and Deep Learning is a free online book. Chemistry, Physics, etc.). Deep Learning Weekly Reading List #1. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. So, they learn deeply about the images for accurate prediction. 1. This is accomplished when the algorithms analyze huge amounts of data and then take actions or perform a function based on the derived information. General Introduction to Deep Learning. We list 10 ways deep learning is used in practice. If you’re interested in applying AI and DL to your business, also check out RE•WORK’s white paper; Should you be using AI in your Business? Have a good understanding of Deep Learning. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. So if you are looking for a truly complete guide on Deep Learning , let’s get started. Science 350.6266 (2015): 1332-1338. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The basic gist is an encoder model produces an embedding that can be used by a decoder model to reproduce the inputs, and by doing so, learns to essentially compress the important parts of an input into a small feature vector. 16 One Shot Deep Learning [16.0] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. Machine Learnings. Please understand that this is not an exhaustive list by any means or even a complete list of what I have. This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Part 1: Fundamentals of Deep Learning. Thursday, October 1: Introduction to deep learning. I recommend finding something you’re interested in solving, and start working towards reading papers that provide solutions to those problems. Contents. My Deep Learning List (The below list does not represent articles and blogs I’ve “glanced over”, only those I’ve spend considerable amount of time reading and attempting to understand.) Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. ; Review Papers Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012.; The monograph or review paper Learning Deep Architectures for AI (Foundations & Trends in Machine Learning… This reading list is relatively long, and I don’t proclaim to have read every single word on every single page. Topics: Deep neural networks (DNNs) Keras. Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, In preparation. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. This is a curated list of what I would recommend as resources for learning about various aspects of deep learning, heavily inspired by this Github repository, although based on my own personal experience. While variational autoencoders are cool, they are typically limited by the fact that diagonal Gaussians do not make very good approximations to true posteriors in many (maybe most) cases. 1995 – Support vector machines Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. ; Review Papers Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012.; The monograph or review paper Learning Deep Architectures for AI (Foundations & Trends in Machine Learning… Deep Learning has probably been the single-most discussed topic in the academia and industry in rece n t times. Deep learning is a subcategory of machine learning. Kelvin Lee. They conclude their list with a list of three other machine learning reading lists and three other links to deep learning tutorials. Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. Take a look, Estimating Information Flow in Neural Networks, The Capacity of Feedforward Neural Networks, On the Expressive Power of Deep Neural Networks, Modular learning in neural networks, 1987, An Introduction to Variational Autoencoders, β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Vector Quantized Variational Autoencoders, Temporal Difference Variational Auto-Encoder, that diagonal Gaussians do not make very good approximations to true posteriors in many (maybe most) cases, Variational Inference with Normalizing Flows, Masked Autoregressive Flow for Density Estimation, Normalizing Flows: An Introduction and Review of Current Methods, Improving Out-of-Distribution Detection in Machine Learning Models, Robust Out-of-Distribution Detection for Neural Networks, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, Apprenticeship learning via inverse reinforcement learning, Image Augmentation is All You Need: Regularlizing Deep Reinforcement Learning from Pixels, Inverse Reinforcement Learning from Failure, The OpenAI gym for reinforcement learning, Semi-Supervised Classification with Graph Convolutional Networks, Graph Neural Networks: A Review of Method and Applications, Topology Adaptive Graph Convolutional Networks, Pooling in Graph Convolutional Neural Networks, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Ten Deep Learning Concepts You Should Know for Data Science Interviews, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Top 10 Python GUI Frameworks for Developers.