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Explore neural radiance fields for 3D scene representation and view synthesis, covering network architecture, key concepts, learning process, and advanced techniques.
Gentle introduction to Graph Neural Networks, covering key concepts, properties, and variants. Learn about graph representation, information propagation, and common tasks in this emerging field of machine learning.
Explore Alpaca and LLaMA models in real-time, gaining hands-on experience with cutting-edge language AI technologies and their practical applications.
Comprehensive overview of GPT-4's capabilities, improvements, and potential applications, exploring its advancements in language understanding and generation compared to previous models.
Explore LLaMA, ChatLLaMA, and other language models, understanding their capabilities, differences, and potential applications in AI and natural language processing.
Explore Latent Dirichlet Allocation in Python: Learn to implement this powerful topic modeling technique for text analysis and document classification.
Explore fine-tuning Whisper using Hugging Face and PyTorch Lightning for advanced speech recognition. Learn techniques to enhance model performance and adapt it to specific use cases.
Detailed walkthrough of the ESRGAN paper, exploring enhanced super-resolution techniques using generative adversarial networks for improved image upscaling and restoration.
Learn to implement ESRGAN, an advanced image super-resolution technique, from scratch. Gain hands-on experience in deep learning and computer vision through step-by-step coding.
Comprehensive walkthrough of the Variational AutoEncoder research paper, explaining key concepts, mathematical foundations, and practical applications in deep learning and generative modeling.
Learn to build a dog breed identifier app from scratch, covering data collection, model training, and deployment to the Google Play Store.
Develop a deep learning app for digit recognition using MNIST dataset, covering model creation, app development, and publishing on the Google Play Store.
Learn to calculate and implement Mean Average Precision (mAP) for evaluating object detection models, with a detailed explanation and hands-on PyTorch implementation from scratch.
Comprehensive guide to Intersection over Union (IoU) in object detection, covering theory and practical implementation in PyTorch. Ideal for understanding this crucial metric.
Learn to build flexible TensorFlow models using Keras subclassing, including a ResNet-like model with skip connections. Gain advanced model creation skills beyond Sequential and Functional APIs.
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