YouTube Pytorch Courses List

This course provides an introduction to deep learning using PyTorch. It covers topics such as installation, tensor basics, autograd, backpropagation, gradient descent, training pipeline, linear and logistic regression, datasets and dataloaders, convolutional neural networks, recurrent neural networks, and more. It is designed to help learners understand the fundamentals of deep learning and how to apply them to real-world problems. View all

This tutorial introduces the core functionality of PyTorch, and demonstrates how to use it to solve a classification problem. It covers defining the network architecture, loss function and optimizer, setting up TensorBoard, and the training loop. It provides a comprehensive overview of the fundamentals of PyTorch, and how to use it to build a fashion recognizer. View all

This module explores the weird and wonderful aspects of Python programming. It covers topics such as the antigravity module, the walrus operator, string interning, chained operations, dictionary key hashing, and the all function. It also poses a challenge to readers to guess the answer to a mystery question. Python is a powerful and versatile language, and this module provides an interesting insight into its quirks. View all

PyTorch is a popular open-source deep learning framework that is used to build, train, and deploy models. This course covers the basics of PyTorch, including what makes it different from other frameworks, how it deals with data, and how to build and deploy models. Participants will also learn about the PyTorch community and resources available to them. View all

This tutorial provides an introduction to time sequence prediction using PyTorch and Long Short-Term Memory (LSTM) models. It begins by creating a sine wave, then moves on to constructing an LSTM model and training loop. Finally, the model is tested on unseen data to evaluate its performance. This tutorial provides a comprehensive overview of the process of time sequence prediction with LSTM models in PyTorch. View all

This course provides an overview of the Transformer model, a powerful deep learning architecture based on the Attention is All You Need paper. It covers the Attention Mechanism, TransformerBlock, Encoder, DecoderBlock, and Decoder, and how they work together to form the Transformer. It also includes a small example and tips on fixing errors. View all

This course provides an introduction to scaling ML workloads with PyTorch. It explains why large model training is necessary and how scaling can create training and model efficiency. It also discusses how larger models can learn with few shot learning, democratizing large-scale ML training and making it more accessible. Finally, it covers how to use PyTorch to scale ML workloads. View all

This course provides an introduction to Torch-TensorRT deep learning prediction for beginners. It covers the steps to clone Torch-TensorRT, install and setup Docker, install Nvidia Container Toolkit and Nvidia Docker 2, and two container options for Torch-TensorRT. Participants will learn how to import Pytorch, load a model, and run inference on CPU, CUDA, and TensorRT. This course is ideal for those looking to get started with deep learning prediction. View all

PyTorch Enterprise was announced on the AI Show Live livestream, hosted by Seth and featuring Alon Bochman from Microsoft. PyTorch Enterprise on Microsoft Azure provides users with access to a range of features, such as AI model development, deployment, and management. The livestream also included a Q&A session. View all

PyTorch Lightning is a deep learning library that simplifies the process of building and training models. In this course, we explore the GoEmotions dataset by Google and set up a Google Colab notebook. We then explore the data, tokenize a Reddit comment, and choose a sequence length. In the next lesson, we will continue to build our model. View all

This course provides a comprehensive introduction to PyTorch Tensors for beginners. It covers topics such as creating Tensors, different types of Tensors, operations on Tensors, slicing and indexing, reshaping, saving and loading Tensor data. It is a great resource for anyone looking to learn the basics of PyTorch Tensors. View all

This tutorial provides an overview of how to use Pytorch to create a sequence-to-sequence model for machine translation. It covers data processing using Torchtext, implementation of the encoder and decoder, putting it together to form a Seq2Seq model, setting up training, fixing errors, and evaluation. The tutorial provides a comprehensive guide to creating a machine translation model using Pytorch. View all

This tutorial provides a comprehensive guide to image segmentation using PyTorch and U-NET. It covers the entire process from scratch, including creating a dataset, building a model, training, and evaluation. It also provides useful utilities to help with the process. This tutorial is a great resource for anyone looking to get started with image segmentation. View all

This course provides an introduction to using Pytorch Transformers for Machine Translation. It covers data preprocessing, setting up a Transformer network, training the model, fixing errors, and evaluating the model with a BLEU score. It provides a comprehensive overview of the process for those interested in using Pytorch Transformers for Machine Translation. View all

This tutorial provides an overview of how to use TensorBoard with PyTorch. It covers initializing SummaryWriter, creating a loss and accuracy plot, doing hyperparameter search, visualizing dataset images and network weights, and using the Tensorboard Embedding Projector. This tutorial is a great resource for anyone looking to get started with TensorBoard and PyTorch. View all

This course provides an introduction to PyTorch and Python for beginners. It covers notebook setup, exploring data, creating a model, turning data into Tensors, making predictions from an untrained model, training, exploring predictions, and drawing conclusions. Learners will gain the skills to build their first model with PyTorch and Python. View all

This course provides an introduction to deep learning using PyTorch. It covers installation and overview, tensor basics, autograd, linear regression, autograd model, loss and optimizer, neural network, convolutional neural network, recurrent neural network, and transfer learning. It is designed to help learners understand the fundamentals of deep learning and how to use PyTorch to build and train models. View all

This course provides an introduction to Weights & Biases and PyTorch Lightning, two powerful tools for training models. Participants will learn how to install, import, and set up these tools, as well as how to build a model with Lightning and W&B. Additionally, they will explore next-level logging, such as model files, logits, and more, and how to use Lightning Callbacks to log media to W&B. View all

This tutorial provides an introduction to Q Learning, a reinforcement learning technique. It covers the training loop, policy, for loop, ord reward, and append reward. It explains how to use these components to create an effective training loop for Q Learning. The tutorial provides a comprehensive overview of the process, allowing users to understand and implement Q Learning in their own projects. View all

This tutorial provides an overview of data augmentation using Albumentations, with a focus on PyTorch. It covers augmentation techniques for classification, segmentation, and detection tasks, and provides a full PyTorch example. It is a comprehensive guide to help users get started with data augmentation. View all

This course provides an introduction to Autoencoders in PyTorch, covering both theory and implementation. It begins with data loading, followed by a simple Autoencoder and a training loop. The course then moves on to plotting images and a CNN Autoencoder, before concluding with an exercise for the student. This course is ideal for those looking to gain a comprehensive understanding of Autoencoders in PyTorch. View all

This tutorial provides an overview of the EfficientNet architecture and its implementation from scratch in Pytorch. It covers the imports, architecture configuration, implementation structure, CNNBlock, SqueezeExcitation, InvertedResidualBlock with Stochastic depth, and running a small test case. Finally, the tutorial concludes with a demonstration of the EfficientNet model. View all

This tutorial provides an introduction to Q Learning and explores the concept of Experience Replay. Preprocessing, testing and sample replay are discussed in detail. It provides a comprehensive overview of the process of using Experience Replay to improve the performance of Q Learning algorithms. View all

Discover the fundamentals of PyTorch Tutorial - Neural Networks & GPU View all

Explore the essentials of PyTorch Tutorials - Complete Beginner Course View all

Gain an introduction to Neural Network Programming - Deep Learning with PyTorch View all

Gain an introduction to Pytorch - Deep learning w& Python View all

Get a comprehensive overview of PyTorch Zero To All (in English) View all

Learn the basics of How to build custom Datasets for Text in Pytorch View all

Learn the basics of Pytorch Tutorial View all