# d2l-ai/berkeley-stat-157

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3,977 stars · 1,506 forks · Jupyter Notebook · Apache-2.0

## Links

- GitHub: https://github.com/d2l-ai/berkeley-stat-157
- Homepage: https://courses.d2l.ai/berkeley-stat-157/index.html
- awesome-repositories: https://awesome-repositories.com/repository/d2l-ai-berkeley-stat-157.md

## Description

This project is an academic curriculum repository and educational resource center for studying probability, statistics, and machine learning. It serves as a deep learning course website and a hub for instructional materials, providing a structured collection of content designed to teach neural network architectures.

The repository distinguishes itself by combining a comprehensive educational resource with a machine learning project archive. It provides a curated set of research examples and implementation guides for a wide range of models, including multilayer perceptrons, convolutional networks, and recurrent sequence models.

The project covers a broad range of capabilities, including computer vision research for object detection and image classification, the implementation of sequence-to-sequence modeling, and neural network optimization techniques. It also provides administrative tools for academic course management, such as project milestone tracking and assignment distribution.

Instructional materials are delivered through a combination of interactive notebooks, lecture slides, and video recordings.

## Tags

### Education & Learning Resources

- [Deep Learning Education](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education.md) — Serves as a comprehensive educational resource for studying neural network architectures and statistics.
- [Educational Curriculum Repositories](https://awesome-repositories.com/f/education-learning-resources/educational-resources/courses-training-certifications/courses-structured-learning/computer-science-curricula/educational-curriculum-repositories.md) — Serves as a comprehensive hub providing structured collections of learning materials for machine learning.
- [AI Curriculum Materials](https://awesome-repositories.com/f/education-learning-resources/ai-curriculum-materials.md) — Provides access to instructional assets such as slides, video lectures, and notes for teaching deep learning. ([source](https://courses.d2l.ai/berkeley-stat-157/units/adam.html))
- [Curriculum Structures](https://awesome-repositories.com/f/education-learning-resources/curriculum-structures.md) — Organizes educational content into structured thematic modules for deep learning and statistics. ([source](https://courses.d2l.ai/berkeley-stat-157/units/index.html))
- [Deep Learning Courses](https://awesome-repositories.com/f/education-learning-resources/deep-learning-courses.md) — Provides a dedicated website for instructional materials and notebooks on neural network architectures.
- [Academic Course Materials](https://awesome-repositories.com/f/education-learning-resources/educational-resources/algorithms-theory-academics/academic-curricula-resources/academic-course-materials.md) — Organizes curriculum modules, homework distribution, and student project tracking for a university course.
- [Instructional Delivery Frameworks](https://awesome-repositories.com/f/education-learning-resources/instructional-delivery-frameworks.md) — Provides a delivery framework combining slides, videos, and interactive notebooks to guide students. ([source](https://courses.d2l.ai/berkeley-stat-157/units/optimization.html))
- [Interactive Notebook Environments](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula/interactive-notebook-environments.md) — Delivers curriculum through interactive notebook environments combining executable code, math, and instructional text.
- [Statistical Lecture Materials](https://awesome-repositories.com/f/education-learning-resources/statistical-lecture-materials.md) — Provides access to lecture slides and notebooks specifically focused on probability and statistics. ([source](https://courses.d2l.ai/berkeley-stat-157/units/probability.html))
- [Neural Network Architectures](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/neural-network-architectures.md) — Provides educational content focused on the structural design of residual networks and densely connected networks. ([source](https://courses.d2l.ai/berkeley-stat-157/units/resnet.html))
- [Neural Network Implementations](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/neural-network-architectures/linear-neural-networks/neural-network-implementations.md) — Provides instructional guides for building perceptrons, convolutional networks, and recurrent networks from scratch.
- [Assignment Delivery](https://awesome-repositories.com/f/education-learning-resources/course-material-managers/coursework-listings/assignment-delivery.md) — Implements a utility to deliver homework assignments in multiple formats with specified submission tools. ([source](https://courses.d2l.ai/berkeley-stat-157/units/loss.html))
- [Curated Reading Lists](https://awesome-repositories.com/f/education-learning-resources/curated-reading-lists.md) — Organizes a curated collection of extended guides and external resources for technical prerequisites. ([source](https://courses.d2l.ai/berkeley-stat-157/units/introduction.html))
- [Model Selection Guidance](https://awesome-repositories.com/f/education-learning-resources/model-selection-guidance.md) — Offers guidance on model selection, weight decay, and dropout via instructional slides and notebooks. ([source](https://courses.d2l.ai/berkeley-stat-157/units/capacity.html))
- [Technical Concepts](https://awesome-repositories.com/f/education-learning-resources/technical-concepts.md) — Distributes slides and notebooks to support the learning of core theoretical deep learning concepts. ([source](https://courses.d2l.ai/berkeley-stat-157/units/introduction.html))
- [Convolutional Neural Network Tutorials](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/neural-network-architectures/convolutional-neural-network-tutorials.md) — Offers educational overviews of convolutions, padding, strides, and pooling in CNN architectures. ([source](https://courses.d2l.ai/berkeley-stat-157/units/convnet.html))
- [Deep Neural Network Training Optimization](https://awesome-repositories.com/f/education-learning-resources/technical-interview-preparation/ml-interview-preparation/deep-learning-review/deep-neural-network-training-optimization.md) — Teaches optimization techniques such as weight decay, dropout, and gradient stabilization to improve model performance.

### Artificial Intelligence & ML

- [Computer Vision Research](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-research.md) — Develops and evaluates models for image classification, object detection, and style transfer.
- [Custom Neural Network Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-neural-network-layers.md) — Provides the capability to define specialized neural network layers and components for mathematical operations. ([source](https://courses.d2l.ai/berkeley-stat-157/units/layers.html))
- [Model Parameter Management](https://awesome-repositories.com/f/artificial-intelligence-ml/model-parameter-management.md) — Includes systems for initializing and updating network weights and biases to optimize performance. ([source](https://courses.d2l.ai/berkeley-stat-157/units/layers.html))
- [Modular Neural Network Design](https://awesome-repositories.com/f/artificial-intelligence-ml/modular-neural-network-design.md) — Provides a modular approach to designing neural network layers and components for complex architectures.
- [Neural Network Model Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-model-implementations.md) — Provides practical implementations of recurrent neural network architectures including LSTMs and GRUs for sequence processing. ([source](https://courses.d2l.ai/berkeley-stat-157/units/rnn.html))
- [Bounding Box Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection/bounding-box-implementations.md) — Implements theoretical concepts like anchor boxes and multiscale detection for object identification. ([source](https://courses.d2l.ai/berkeley-stat-157/units/detection.html))
- [Deconvolutional Image Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/deconvolutional-image-generation.md) — Includes capabilities to create visual content using deconvolutional networks to transform latent representations. ([source](https://courses.d2l.ai/berkeley-stat-157/projects/15.html))
- [GPU-Accelerated Training](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-backends/cuda-mining-backends/gpu-accelerated-training.md) — Implements GPU-accelerated training to reduce processing time for large-scale deep learning datasets.
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-training.md) — Covers techniques for distributing model training across multiple GPUs to reduce computation time. ([source](https://courses.d2l.ai/berkeley-stat-157/units/parallel.html))
- [Bidirectional Recurrent Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-concepts/network-architectures-and-layers/bidirectional-recurrent-neural-networks.md) — Includes studies and implementation guides for bidirectional recurrent neural networks and sequence analysis algorithms. ([source](https://courses.d2l.ai/berkeley-stat-157/units/blstm.html))
- [Educational Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-datasets/educational-datasets.md) — Supplies curated datasets used for training and evaluating language models within an educational context. ([source](https://courses.d2l.ai/berkeley-stat-157/units/sequence.html))
- [Multilayer Perceptrons](https://awesome-repositories.com/f/artificial-intelligence-ml/multilayer-perceptrons.md) — Implements multilayer perceptrons using interactive code examples to demonstrate non-linear mapping. ([source](https://courses.d2l.ai/berkeley-stat-157/units/mlp.html))
- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/object-detection.md) — Provides a framework for comparing object detection architectures like SSD and Region-based CNNs. ([source](https://courses.d2l.ai/berkeley-stat-157/units/detection.html))
- [Satellite Imagery Land Use Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/satellite-imagery-land-use-detection.md) — Provides a capability to detect and locate oil palm plantations in satellite imagery to identify land use. ([source](https://courses.d2l.ai/berkeley-stat-157/projects/10.html))
- [Sequence-to-Sequence Models](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-to-sequence-models.md) — Implements and studies recurrent neural networks, LSTMs, and encoder-decoder architectures for sequential data.

### Part of an Awesome List

- [Machine Learning Projects](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning-projects.md) — Maintains a curated archive of machine learning research examples including object detection and sequence modeling.
- [Image Attribute Classification](https://awesome-repositories.com/f/awesome-lists/ai/image-classification/image-attribute-classification.md) — Implements research for classifying image attributes using disentangled embeddings on multimodal datasets. ([source](https://courses.d2l.ai/berkeley-stat-157/projects/2.html))
- [Academic Courses](https://awesome-repositories.com/f/awesome-lists/learning/academic-courses.md) — Introductory deep learning course materials from UC Berkeley.

### Scientific & Mathematical Computing

- [Matrix Numerical Computations](https://awesome-repositories.com/f/scientific-mathematical-computing/matrix-numerical-computations.md) — Provides tensor-based numerical computation for the linear algebra required in neural network training.
- [GPU-Accelerated Computation](https://awesome-repositories.com/f/scientific-mathematical-computing/gpu-accelerated-computation.md) — Provides configurations for leveraging GPU hardware to accelerate large-scale tensor operations. ([source](https://courses.d2l.ai/berkeley-stat-157/units/layers.html))

### Business & Productivity Software

- [Student Assignments](https://awesome-repositories.com/f/business-productivity-software/task-workflow-automation/productivity-task-management/task-management/tasks-frameworks/agent-task-assignment/student-assignments.md) — Provides a utility for distributing homework exercises and submission instructions to students via interactive notebooks. ([source](https://courses.d2l.ai/berkeley-stat-157/units/arrays.html))

### Content Management & Publishing

- [Instructional Media Distributions](https://awesome-repositories.com/f/content-management-publishing/multi-format-document-exports/instructional-media-distributions.md) — Distributes course materials through a combination of interactive notebooks, lecture slides, and video recordings.