PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial
Learn to predict time sequences using PyTorch and LSTMs. Create sine waves, build an LSTM model, implement a training loop, and perform final testing for accurate forecasting.
Deep Learning With PyTorch - Full Course
Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.
Face Matching Application Using Deep Learning - End to End Implementation
Develop a Bollywood celebrity face matching application using deep learning, covering data collection, architecture design, environment setup, implementation stages, and web app creation.
Implementation of Perceptron in Deep Learning Using Python from Scratch
Comprehensive guide to implementing perceptrons in Python, covering theory and practical coding from scratch for deep learning enthusiasts and AI developers.
Deep Learning - All Optimizers in One Video - SGD with Momentum, Adagrad, Adadelta, RMSprop, Adam Optimizers
Comprehensive exploration of deep learning optimizers, covering gradient descent, SGD, momentum, Adagrad, Adadelta, RMSprop, and Adam, with detailed explanations and comparisons.
SPLADE - The First Search Model to Beat BM25
Explore SPLADE, a groundbreaking sparse embedding model for AI-powered search. Learn its advantages over traditional methods, implementation techniques, and potential future developments in vector search technology.
Inside TensorFlow - tf.Keras
Dive deeper into tf.Keras with creator Francois Chollet, exploring advanced topics like masking, dynamic layers, callbacks, and building successful models for enhanced deep learning capabilities.
Inside TensorFlow - tf.Keras - Part 1
Explore tf.Keras architecture, layers, and models with creator Francois Chollet. Learn about Functional API, eager execution, and automatic masking in this technical deep dive.
Inside TensorFlow - tf.distribute.Strategy
Explore tf.distribute.Strategy for efficient distributed TensorFlow training. Learn implementation details, data parallelism, and optimization techniques from a TensorFlow engineer.
Swift for TensorFlow - Google I/O 2019
Explore Swift for TensorFlow, a platform integrating deep neural networks with traditional software development. Learn its benefits for advanced machine learning research and why fast.ai chose it for their deep learning course.
Getting Started with TensorFlow 2.0 - Google I/O'19
Explore TensorFlow 2.0's user-friendly APIs for creating neural networks. Learn when to use Keras Sequential, Functional, and Subclassing APIs through practical code examples.
Deep Learning for Fundamental Sciences Using High-Performance Computing
Exploring deep learning applications in fundamental sciences using high-performance computing to uncover universe secrets and improve discovery sensitivity.
Tensor2Tensor - TensorFlow at O’Reilly AI Conference, San Francisco '18
Explore state-of-the-art deep learning models and datasets with Tensor2Tensor, accelerating ML applications like translation, parsing, and image captioning.
Distributed TensorFlow - TensorFlow at O'Reilly AI Conference, San Francisco '18
Learn distributed TensorFlow training using Keras APIs, Kubeflow, and Kubernetes. Explore TensorFlow's architecture, set up clusters, and distribute models for efficient multi-GPU and multi-node performance.
Distributed TensorFlow Training - Google I/O 2018
Learn to scale TensorFlow training across multiple GPUs and machines efficiently. Explore distribution strategies, performance optimization techniques, and practical implementation using Google Cloud.
Making Stunning AI Anime Animation on Local Stable Diffusion with Deforum and Anything
Learn to create stunning AI anime animations using Stable Diffusion, Deforum, and Anything. Master local installation, configuration, and rendering techniques for unique, high-quality anime-style animations.
Ultimate RunPod Tutorial for Stable Diffusion - Data Transfers, Extensions, CivitAI
Comprehensive guide to using RunPod for Stable Diffusion, covering setup, deployment, file transfers, extensions, and advanced techniques like DreamBooth and ControlNet.
StyleGAN Paper Explained
Comprehensive explanation of StyleGAN paper, covering architecture, image quality techniques, style properties, mixing, disentanglement, and training details for advanced AI image generation.
Introduction to Transfer Learning
Explore transfer learning to adapt pre-trained networks for microscopy applications, enhancing accuracy with limited labeled data and reducing training time.
Application of Autoencoders - Image Colorization
Learn to artificially colorize black and white images using autoencoders in Python. Explore the process, implementation, and practical application of this advanced image processing technique.
An Introduction to Autoencoders - In Python
Learn to build and implement autoencoders in Python for image compression, reconstruction, and various applications like denoising and anomaly detection.