Projects sharing features with Berkeley Stat 157

This project is a deep learning educational course and technical study guide. It provides a comprehensive set of AI curriculum materials, including slides, notes, and assignments designed to teach neural network fundamentals and generative models. The content focuses on the mathematical foundations of deep learning, featuring detailed step-by-step formula derivations and explanations of model architecture basics. It covers both foundational concepts and advanced research topics, such as self-supervised learning and adversarial attacks. The repository includes applied technical exercises that

This project is a PyTorch deep learning tutorial and educational resource. It provides a structured curriculum and step-by-step guides for designing, training, and validating neural networks from scratch. The resource includes specific guides on computer vision implementation, focusing on object detection and image classification using convolutional neural networks. It also provides instructions for optimizing model performance through hardware acceleration to reduce training time. The materials cover the full model development lifecycle, including tensor operations, image dataset preparatio

This project is a collection of educational resources and reference implementations for neural network development using TensorFlow. It serves as a comprehensive learning course, machine learning curriculum, and practical implementation guide for building deep learning architectures. The codebase provides instructional materials and examples covering a wide range of model types, including convolutional neural networks for image classification, recurrent networks and long short-term memory cells for sequential data, and autoencoders for generative modeling. It also includes implementations for

This project is an educational resource and comprehensive guide for implementing and deploying deep learning models using the PyTorch framework. It provides a structured learning curriculum consisting of tutorials and notebooks that cover neural network architectures, data pipelines, and model optimization across multiple AI domains. The curriculum includes practical implementation guides for building convolutional networks, transformers, and recurrent models. It specifically focuses on workflows for computer vision, including image classification, object detection, and segmentation, as well

This repository is a deep learning for natural language processing course and curriculum. It provides educational material and guides focused on neural network architectures used for processing natural language, speech signals, and text classification. The content includes instructional tutorials on sequence modeling and neural language modeling, covering the implementation of n-gram and recurrent neural networks. It also provides a framework for studying word embeddings to map linguistic meanings into numerical representations. The curriculum covers a broad range of capabilities, including

This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and tensor operations using the TensorFlow framework. It serves as a machine learning educational repository and implementation guide for deep learning students. The suite focuses on specific advanced architectures, including convolutional networks for image classification, residual networks with skip connections for training stability, and variational autoencoders for generative modeling and data synthesis. It also includes guides for building denoising and deep autoencoders to pe

This repository is an educational collection of implementations and research notes focused on deep learning architectures and optimization techniques. It provides modular code examples designed to demonstrate foundational and advanced concepts in machine learning, ranging from basic neural network structures to complex training strategies. The project distinguishes itself by offering practical implementations of specialized research methods, including capsule-based feature aggregation, gradient direction decoupling, and self-normalizing weight regularization. These materials allow for the stu

Chainer is an open-source deep learning framework built around define-by-run automatic differentiation, where computation graphs are constructed dynamically during forward execution. This imperative approach allows networks to be built using standard Python control flow, with gradients computed automatically through reverse-mode differentiation on the dynamically recorded graph. The framework supports GPU acceleration through a NumPy-compatible array backend with CUDA and cuDNN support, and provides a pluggable device abstraction that lets users switch between CPU and GPU computation without c

This repository is an educational collection of deep learning implementations designed to demonstrate the fundamental principles of neural network architecture and optimization. It provides a comprehensive resource for understanding machine learning through hands-on code examples, ranging from basic multilayer perceptrons to complex generative models. The project distinguishes itself by emphasizing the manual construction of models, including the implementation of backpropagation from scratch to illustrate core mathematical mechanics. It covers a wide array of architectural design patterns, s

This project is a comprehensive deep learning framework and educational platform designed for constructing, training, and evaluating neural network architectures. It provides a modular environment for building models through tensor operations and automatic differentiation, supporting a wide range of tasks from image classification and object detection to sequential data processing. Beyond its core technical capabilities, the project distinguishes itself by integrating professional career development resources directly into its learning ecosystem. It offers structured guidance, resume reviews,

This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners to master complex artificial intelligence concepts through hands-on experimentation. The platform distinguishes itself by integrating technical explanations with executable Jupyter notebooks. This design allows readers to modify code and hyperparameters in real-time, facilitati

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

This project is a comprehensive set of educational resources and structured curricula for learning artificial intelligence and deep learning. It provides a machine learning curriculum consisting of lecture materials and interactive notebooks centered on implementing models using the PyTorch framework. The instructional design follows a code-first approach, where students implement working models before studying the underlying theoretical mathematics. The curriculum is delivered via executable documents that combine live code, equations, and narrative text to guide the implementation and deplo

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen

This repository is a comprehensive educational program and deep learning framework designed to teach practical deep learning using PyTorch through notebooks and code examples. It serves as a high-level library for building, training, and deploying neural networks, acting as a model training orchestrator that coordinates PyTorch models, optimizers, and loss functions. The project provides specialized toolkits for computer vision, natural language processing, and tabular data preprocessing. It distinguishes itself through advanced training controls such as discriminative learning rates, a two-w

Grokking-Deep-Learning is a collection of educational resources and courseware designed to teach the construction of neural networks from scratch. It serves as a programming tutorial and implementation guide for understanding the internal mechanics of deep learning. The project focuses on building various network architectures, including convolutional, recurrent, and long short-term memory networks. It provides step-by-step implementations of fundamental mechanisms such as forward propagation, backpropagation, and gradient descent. The material covers a broad range of deep learning capabilit

Deep-Learning-Experiments is an educational resource providing a collection of structured notes and hands-on coding experiments focused on neural network theory and model development. The repository serves as a practical guide for building and optimizing machine learning architectures, ranging from basic perceptrons to modern generative models. The project utilizes interactive notebooks to combine live code with narrative text, allowing users to explore the mathematical principles and architectural concepts behind deep learning. It provides instructional materials that cover the end-to-end ma

This project is a deep learning curriculum and a collection of PyTorch tutorials designed for deep learning education. It provides a structured set of technical documents and runnable notebooks that translate theoretical machine learning concepts into executable code. The repository includes implementation guides for various neural network architectures, specifically covering convolutional, recurrent, and transformer-based models. It provides practical examples for building computer vision pipelines for object detection and semantic segmentation, as well as natural language processing tools f

This project is a deep learning educational course and implementation guide designed for building and training neural networks. It provides a curriculum for developing models that solve pattern recognition and generative tasks. The material includes specialized modules for computer vision training, natural language processing, and generative AI. It covers the practical application of transfer learning to classify new data and the creation of synthetic media. The project encompasses the design of network architectures, the construction of machine learning data pipelines, and the use of model

This project is a deep learning study resource and educational curriculum designed for mastering neural network architectures and theory. It serves as a learning platform that combines theoretical notes and mathematical formulas with practical code implementations. The curriculum is centered on the PyTorch framework, providing a structured path for building and training models through annotated code examples and technical reviews of mathematical foundations. The resource utilizes interactive notebooks for executing machine learning algorithms and experimenting with data models. Theoretical

This project is an educational resource consisting of a structured curriculum of interactive notebooks designed to teach deep learning concepts and neural network architectures. It focuses on providing hands-on experience with the TensorFlow 2 framework and the Keras API, guiding users through practical exercises to master machine learning techniques. The repository distinguishes itself by combining instructional content with the technical requirements for high-performance computing. It includes specific guides for configuring local development environments to support hardware-accelerated tra

This project is a collection of interactive notebooks for a TensorFlow deep learning course. It provides guided learning resources and practical tutorials for implementing neural network architectures, supervised learning, and transfer learning. The materials feature a computer vision learning path and specific guides for transfer learning, demonstrating how to adapt pre-trained models to new tasks. It includes tutorials for building regression models and image classifiers using the Keras high-level API. The scope covers supervised learning pipelines for binary and multiclass classification,

This project provides a structured computer science curriculum framework designed for self-directed learners. It organizes open-access academic resources, including textbooks, lectures, and assignments, into a cohesive path that mirrors the requirements of a formal undergraduate degree. By integrating theoretical study with practical software engineering methodologies, the platform enables students to master foundational concepts and advanced technical skills independently. The curriculum distinguishes itself by utilizing a version-control-based workflow to manage the educational experience.

This project is a deep learning educational resource consisting of PyTorch model implementations and code examples. It provides functional Python scripts and notebooks for building, training, and optimizing neural networks using tensor-based computation. The repository includes implementations for designing custom network layers and loss functions, as well as examples of transfer learning workflows that load pretrained model weights to accelerate development. The codebase covers a broad range of deep learning capabilities, including neural network training, custom model component design, and

This project is an educational resource and tutorial series designed to teach the principles of deep learning through interactive notebooks. It provides a structured curriculum that guides users through the implementation of artificial neural networks, focusing on both the practical construction of models and the underlying mechanics of machine learning workflows. The material emphasizes a hands-on approach, allowing users to build and train neural network architectures from scratch using standard programming patterns. By working through these examples, learners gain experience with the core