Dive into Deep Learning

Abstract

This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; (v) be complemented by a forum for interactive discussion of technical details and to answer questions.

arXiv comment(HTML) https://D2L.ai (GitHub) https://github.com/d2l-ai/d2l-en/

References in corpus (16)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Efficient Estimation of Word Representations in Vector Space
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• ADADELTA: An Adaptive Learning Rate Method
• Natural Language Processing (almost) from Scratch
• Gaussian Error Linear Units (GELUs)
• On the Convergence of Adam and Beyond
• A Structured Self-attentive Sentence Embedding
• The Microsoft 2017 Conversational Speech Recognition System
• Device Placement Optimization with Reinforcement Learning
• A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation
• From Averaging to Acceleration, There is Only a Step-size
• ControlVAE: Controllable Variational Autoencoder

Cited by in corpus (20)

• Learning to Detect Malicious Clients for Robust Federated Learning
• Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial
• A Survey of Deep Learning for Scientific Discovery
• Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks
• Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures
• Long Short-term Memory RNN
• Deep learning for photoacoustic imaging: a survey
• Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
• Emergency Vehicles Audio Detection and Localization in Autonomous Driving
• Fast Evaluation of Smooth Distance Constraints on Co-Dimensional Geometry
• Understanding Continual Learning Settings with Data Distribution Drift Analysis
• Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation
• Latent Space Arc Therapy Optimization
• A fast asynchronous MCMC sampler for sparse Bayesian inference
• Universal Average-Case Optimality of Polyak Momentum
• Deep Predictive Learning of Carotid Stenosis Severity
• 3D-MOV: Audio-Visual LSTM Autoencoder for 3D Reconstruction of Multiple Objects from Video
• A survey on deep learning approaches for breast cancer diagnosis
• Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers