WGAN Implementation From Scratch With Gradient Penalty
Implement WGAN and WGAN-GP in PyTorch, focusing on improving GAN training stability through loss function modifications. Includes theoretical explanations and practical coding.
Train Mask R-CNN for Image Segmentation
Learn to train and implement Mask R-CNN for image segmentation using free online GPU resources. Covers dataset preparation, model training, and practical application for object detection and segmentation.
High Fidelity Neural Audio Compression - Paper & Code Explained
Explore neural audio compression techniques achieving 10x compression rates, with in-depth analysis of paper and code implementation, including VQ-VAE, VQ-GAN, and AudioGen applications.
AudioGen- Textually Guided Audio Generation - Paper Explained
Explore text-guided audio synthesis with AudioGen, delving into its architecture, audio representation, language modeling, and innovative approaches for generating diverse sounds from textual descriptions.
Diffusion Models Beat GANs on Image Synthesis - ML Coding Series - Part 2
Explore diffusion models for image synthesis, focusing on classifier guidance, training, and sampling techniques. Includes code walkthrough and paper analysis.
NeuroEvolution of Augmenting Topologies and Compositional Pattern Producing Networks
Explore NEAT and CPPN algorithms for evolving neural network architectures and weights, and their applications in developmental biology modeling and image generation.
Tensor Programs - Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
Explore μTransfer: a technique for tuning large neural networks via zero-shot hyperparameter transfer, making optimal hyperparameters stable with respect to width scaling.
VOS - Learning What You Don't Know By Virtual Outlier Synthesis
Explore virtual outlier synthesis for robust image classification and object detection, enhancing out-of-distribution awareness in AI models through innovative feature space sampling techniques.
ConvNeXt- A ConvNet for the 2020s - Paper Explained
Explore ConvNeXt, a modern ConvNet outperforming vision transformers in computer vision tasks. Learn about its design, training, and performance across various applications.
Machine Learning with Flax - From Zero to Hero
Comprehensive guide to Flax, a JAX-based ML library. Learn basics, custom models, optimization, and practical examples like linear regression and CNN on MNIST. Ideal for ML enthusiasts.
A Neural Network Solves and Generates Mathematics Problems by Program Synthesis - Paper Explained
Explore how OpenAI's Codex model solves university-level math problems through program synthesis, covering calculus, differential equations, and linear algebra. Learn about prompt engineering and problem generation techniques.
Efficient Geometry-Aware 3D Generative Adversarial Networks - GAN Paper Explained
Explore cutting-edge 3D generative models with a deep dive into efficient geometry-aware GANs, NeRF technology, and novel scene representation techniques for advanced computer vision applications.
Neural Descriptor Fields- SE(3)-Equivariant Object Representations for Manipulation Paper Explained
Explore novel SE(3)-equivariant object representations for robust robotic manipulation, enhancing generalization to unseen poses and shapes through neural descriptor fields.
Machine Learning with JAX - From Hero to HeroPro+
Advanced JAX tutorial covering PyTrees, parallelism, multi-device training, and advanced techniques for efficient machine learning model implementation and training.
The Sensory Neuron as a Transformer - Permutation-Invariant Neural Networks for Reinforcement Learning - Paper Explained
Explore Google Brain's "Attention Neuron" model for reinforcement learning, featuring permutation-invariant neural networks and their application in various experiments and visualizations.
Neural Search with Jina AI - Open-Source ML Tool Explained
Explore Jina AI, an open-source neural search engine. Learn core concepts, set up an MNIST fashion example, and understand indexing, encoding, and evaluation processes for efficient machine learning workflows.
Do Vision Transformers See Like Convolutional Neural Networks - Paper Explained
Explore the differences between Vision Transformers and CNNs, examining feature learning, receptive fields, data impact, and architectural elements for a deeper understanding of these AI models.
Generative Adversarial Networks and Stable Diffusion
Explore Stable Diffusion for text-to-image generation using Keras. Learn to create custom images with Python, understand GANs, and dive into neural networks and convolutional computations.
Generative Adversarial Networks and TF-GAN - ML Tech Talks
Explore Generative Adversarial Networks (GANs) and TF-GAN library. Learn GAN applications, metrics, self-attention GAN, and practical implementation tips from a TensorFlow Research Engineer.
Intro to Graph Neural Networks
Comprehensive introduction to graph neural networks, covering theory, applications, and hands-on practice with a Colab exercise. Ideal for those interested in advanced machine learning techniques.
Generative Adversarial Networks - Computerphile
Explore Generative Adversarial Networks (GANs) with Rob Miles, learning how neural networks compete to create new, realistic data like images and faces.