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.

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

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• 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
• On the Convergence of Adam and Beyond
• A Structured Self-attentive Sentence Embedding
• Device Placement Optimization with Reinforcement Learning
• From Averaging to Acceleration, There is Only a Step-size

Cited by in corpus (15)

• Learning to Detect Malicious Clients for Robust Federated Learning
• 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
• 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
• A fast asynchronous MCMC sampler for sparse Bayesian inference
• Latent Space Arc Therapy Optimization
• 3D-MOV: Audio-Visual LSTM Autoencoder for 3D Reconstruction of Multiple Objects from Video
• Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers
• Deep Predictive Learning of Carotid Stenosis Severity
• A survey on deep learning approaches for breast cancer diagnosis