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This video provides a tutorial on how to use PyTorch to perform deep learning tasks. It covers installing PyTorch, working with tensors, and using the autograd package to calculate gradients. It also provides an example of backpropagation in action.

**00:00:00**In this video, the author shows how to install PyTorch on a Mac, followed by how to create a virtual environment to install the library. They then proceed to show how to install PyTorch on a Windows machine.**00:05:00**In this video, the author demonstrates how to work with tensors in PyTorch. He first creates an empty tensor, and then creates a tensor with random values. He also demonstrates how to convert between numpy arrays and tensors in PyTorch.**00:10:00**In this video, the presenter discusses how to use PyTorch to work with deep learning. They cover basic operations such as addition, subtraction, multiplication, and division, as well as elementwise and in-place operations. They also discuss how to slice data sets.**00:15:00**This video covers how to use PyTorch to deep learning problems. The first part of the video covers how to create and print a PyTorch tensor. The second part of the video covers how to convert between PyTorch and numpy arrays.**00:20:00**In this video, the basics of deep learning with PyTorch are explained. First, a tensor is introduced, and then a numpy array is shown. The Tensor and Numpy arrays are compared, and it is shown that the Tensor adds 1 to each element of the Numpy array. Next, a torch tensor is created from a numpy array. The default data type for a torch tensor is float64, but the data type can be changed. Finally, the basics of using a GPU for deep learning are explained.**00:25:00**In this tutorial, we learn about the autograd package in PyTorch, and how we can calculate gradients with it. Gradients are essential for our models, and Pi Torch provides the autograd package which can do all the calculations for us. We just have to know how to use it. In the next tutorial, I will explain the concept of back propagation in detail. For now, it's fine to just know that we can use it to calculate the gradients of our functions.**00:30:00**The video discusses the gradient function attribute of tensors in PyTorch, and how to calculate it using the backward function.**00:35:00**The video lesson describes how to use PyTorch to perform deep learning tasks, including gradient-based training and optimization. The three options for stopping the PyTorch gradient function from tracking history are to disable the requires grad attribute, create a new vector without the gradient, or wrap the operation in a width statement. Care should be taken when running the training example, as incorrect gradients can lead to incorrect weights or gradient values being stored in the tensor.**00:40:00**In this video, the narrator explains the chain rule and computational graph, and explains how backpropagation works. He then provides an example of backpropagation in action, and explains how to calculate local gradients.**00:45:00**The PyTorch deep learning tutorial explains how to minimize a loss function using three steps: forward pass, calculation of local gradients, and backward pass. The tutorial provides a concrete example of minimizing a squared error loss on a two-dimensional training dataset.**00:50:00**In this video, PyTorch is used to learn a deep neural network. The first step is to create a vector, tensor, and weight. The gradient of the loss is then computed, and the final gradient is used to update the weights.**00:55:00**This tutorial covers how to optimize a model using the backpropagation algorithm in PyTorch. First, the linear regression algorithm is implemented manually. Then, the loss function and gradient are calculated. Finally, the backpropagation algorithm is used to optimize the parameters.

This video covers how to use PyTorch to train different types of deep learning models. The author starts by explaining the basics of deep learning and then goes into a tutorial on how to train a deep learning model using gradient descent. The video then covers how to apply the forward and backward passes of back propagation and how to avoid using numpy arrays during training. Finally, the video demonstrates how to create a Torch tensor and data type and how to use these for training.

**01:00:00**In this video, the author explains how to use PyTorch to train deep learning models. They cover the basics of deep learning, including how deep learning works and the different types of deep learning models. They then walk through a tutorial on how to train a deep learning model using gradient descent.**01:05:00**This YouTube video explains how to use PyTorch to train a deep learning model. The video starts by discussing how epoch modulo works in deep learning, and then goes on to show how to calculate the weight and loss in a PyTorch model. The video then shows how to apply the forward and backward passes of back propagation, and how to avoid using numpy arrays during training. Finally, the video demonstrates how to create a Torch tensor and data type, and how to use these for training.**01:10:00**In this video, the author discusses the general training pipeline in PyTorch, including the steps taken to design the model, the loss and optimizer, and the training loop. The author also covers the replacement of manually computed losses and parameter updates by using loss and optimizer classes in PyTorch.**01:15:00**In this video, the author walks through the steps of training a deep learning model with PyTorch. First, they define or design the model. Next, they obtain the gradients, update the weights, and iterate until the model is finished. Finally, they show how to replace the loss and the optimization function.**01:20:00**This video walks through the steps of training a deep learning model with PyTorch. The model is first initialized and then used to predict a test dataset. The video notes that the model might not be perfect, but it gets better over time.**01:25:00**This YouTube video demonstrates how to implement linear regression using PyTorch. First, the instructor imports necessary packages, and then shows how to generate a regression data set. Next, the instructor explains how to define a loss function and an optimizer, and then demonstrates how to train a model using a forward pass, a backward pass, and weight updates. Finally, the instructor shows how to plot the results of the training session.**01:30:00**This video explains how to train a linear regression model using PyTorch. The first step is to convert the data to a float32 data type. Next, the model is defined and the optimizer is set up. The training loop is then started, and each step is explained. The forward pass is performed, followed by the backward pass and the update. Finally, the results are calculated and shown.**01:35:00**In this video, a PyTorch tutorial explains how to train a logistic regression model. First, the model is set up, including input and output sizes, and the forward pass is performed. The loss is then calculated, and the optimizer is used to optimize the weights. Finally, the training loop is executed, and the model is predictions are displayed.**01:40:00**In this tutorial, the instructor covers how to use PyTorch to train a neural network. First, they import the necessary packages. Next, they create a model and set up the loss function. Finally, they go through the training process.**01:45:00**In this video, a deep learning model is created using PyTorch. The model is a linear combination of weights and a bias, and is applied using a sigmoid function. The model is then trained using a training set of data.**01:50:00**The video explains how to use PyTorch to train a logistic regression model. First, the loss function is different than in the linear regression case, and the optimizer is the same. Next, step two is to define some number of epochs and step three is to do the training loop. Finally, step four is to do the evaluation.**01:55:00**In this video, the author explains how to use PyTorch's built-in data set and data loader classes to batch train a logistic regression model. This allows for faster training on large data sets.

This video provides a tutorial on how to use PyTorch to train deep learning models. It covers the basics of deep learning with a linear layer and a softmax layer, and demonstrates how to use activation functions to perform digit classification.

**02:00:00**In this video, the presenter explains how to use PyTorch to train deep learning models. They first discuss the necessary modules, including Torch, Vision, and the PyTorch library for data handling. They then show how to load a custom data set and implement three custom methods. Finally, they show how to convert the data set to a tensor and use it in a deep learning model.**02:05:00**In this video, the instructor teaches how to use PyTorch to train deep learning models. They start by loading a data set and then creating a data set iterator. They then use a data loader to convert the data set into a iterate iterator, and then use a loop to train the model. Finally, they print the results of the training.**02:10:00**In this tutorial, the author discusses how to use transforms on data sets, providing an example of using the "tensor transform" on images.**02:15:00**In this video, the instructor demonstrates how to use PyTorch to perform transforms on data. First, they copy code from a previous tutorial and modify it to support transforms. Next, they create a custom transform class. Finally, they demonstrate how to use the transform class to transform data.**02:20:00**In this video, the author teaches how the soft mux function works and how it can be used to reduce the impact of variability in a dataset. He also explains how the cross entropy loss function works and how it can be used to determine how well a dataset classification model is performing.**02:25:00**In this video, the author explains how deep learning works with PyTorch. He covers the basics of deep learning with a linear layer and a softmax layer. He then shows how to calculate the probabilities of the outputs, and how to use the cross-entropy loss to measure the performance of the model. Finally, he demonstrates how to do one hot encoding and how to calculate the predicted values for a given class.**02:30:00**This video introduces the PyTorch deep learning framework and discusses the basics of deep learning with a focus on entropy and cross entropy loss. It demonstrates how to perform predictions using two different examples, one with good predictions and one with bad predictions.**02:35:00**In this video, the presenter explains how to use the cross entropy loss in PyTorch to make predictions in a multi-class classification problem. The presenter also shows how a typical neural network looks like in this type of problem.**02:40:00**This tutorial explains how to use activation functions in neural networks. The binary step function, sigmoid function, hyperbolic tangent function, relu function, and leaky relu function are all explained.**02:45:00**In this video, the different activation functions that are available in PyTorch are discussed, with particular focus on the sigmoid, relu, and leaky relu functions. The softmax function is also discussed, and its use as the final activation function in a multi-class classification problem is demonstrated.**02:50:00**In this PyTorch tutorial, the author demonstrates how to use activation functions to perform digit classification on the amnestic data set. First, they load the data set and apply a transform to it. Next, they create a neural network with three layers - an input layer, a hidden layer, and an output layer. They also apply activation functions to the network. Finally, they set up the loss and the optimizer, train the network using batch training, and evaluate the accuracy of the model. If the model is to be run on the GPU, the author also sets up device and hyperparameter configurations.**02:55:00**In this video, a deep learning tutorial on using PyTorch is given. First, the input size for a 1d tensor is determined, and then a hidden size is specified. The number of epochs and learning rate are also set. The mnist data set is imported, and the data loaders are created. The training data set is set to be the root folder of the data set, and the batch size is set to 100. The test data set is copied but not downloaded. The examples data set is converted to an iter object and next is called, printing the size of the samples and the labels. The data is saved and run.

This video introduces PyTorch, a tool for deep learning, and explains how to use it to train a model to classify images. The video starts by showing how to set up a basic model, and then goes on to show how to add layers and train the model. Finally, the video shows how to evaluate the model's performance.

**03:00:00**In this video, the author teaches how to use PyTorch to build a deep learning model to classify handwritten digits. First, they create a neural network classifier with one hidden layer. Next, they define the forward and loss methods, and then create an optimizer. Finally, they use the atom optimizer to train the model.**03:05:00**This video teaches how to use PyTorch to train deep learning models. The video starts by explaining the different components of a deep learning model, including input data, the neural network, and the loss function. The video then goes on to show how to train a deep learning model using a training loop and forward and backward passes. Finally, the video shows how to print information about the model's performance.**03:10:00**This video introduces Convolutional Neural Networks (CNNs), and explains how to create a model to classify images from the "Cipher 10" data set in PyTorch. The video finishes with a demonstration of the model's accuracy.**03:15:00**In this PyTorch video, the instructor outlines the basics of Convolutional Neural Networks (CNNs). He then goes on to show how to create a CNN model and implement the loss and optimizer. Finally, he demonstrates how to train a CNN model on a data set.**03:20:00**In this video, a deep learning course, the instructor shows how to work with PyTorch to perform forward and backward passes on a trained neural network. The instructor also explains the architecture of the network, and how to set up layers and calculate losses. Finally, the instructor implements a convolutional network and calculates the accuracy and accrual accuracy of the network.**03:25:00**In this video, the instructor explains how to apply deep learning to images using PyTorch. The first convolutional layer is applied to the images, and then a pooling layer is applied. The size of the images after these operations is then displayed. The second convolutional layer is then applied, and the images are again reduced in size.**03:30:00**In this video, the author shows how to apply a convolutional neural network (CNN) to a task of image recognition. First, they outline the steps necessary to define the network, and then they show how to apply the network to a training set of images. Finally, they show how to run the network on a test set of images, and evaluate the performance of the network.**03:35:00**In this PyTorch tutorial, we will discuss the concept of transfer learning, and how it can be used in deep learning. We will use a pre-trained ResNet 18 network to demonstrate the process.**03:40:00**In this video, the author walks through how to use PyTorch to train a deep learning model. They first import the necessary modules and then create a model using the torch visionmodels module. They then define a loss and optimizer, and train the model using a point-zero-one learning rate. They then use a scheduler to update the learning rate.**03:45:00**In this video, the author shows how to use PyTorch to train a deep learning model. They first set up the scapular and then called the training functions. The model was then trained using a technique called fine tuning. Both options for training the model were run, and the author then showed how to evaluate the model. Finally, the author showed how to save and run the model.**03:50:00**In this tutorial, the author explains how to use the TensorBoard toolkit to visualize and analyze the results of a deep learning model. The author also provides code for a digit classification task using the MNIST data set.**03:55:00**The video discusses how to use PyTorch to train deep learning models. The first thing the presenter does is import the necessary libraries. They then discuss how to create a summary writer to save the logs and the model's output. They then add a graph to the tensorboard to visualize the model's performance.

This video provides a detailed tutorial on how to use PyTorch to train deep learning models. It covers everything from loading the model into PyTorch, training the model, saving the model, and using PyTorch's optimizer to find the best learning rate.

**04:00:00**This video explains how to use PyTorch to train a deep learning model. The model is first initialized with a linear layer, then an actuation function, and then a second linear layer. The weights and biases for each layer are then shown, and the model's training and accuracy are calculated. Finally, the training loss and running correct predictions are added to the Tensorboard, and the script is run.**04:05:00**This video demonstrates how to create a precision recall curve using the at precision pr method in tensorflow.**04:10:00**In this video, the author shows how to use PyTorch to Deep Learn using a cross entropy loss. They then show how to calculate the softmax function and convert the results to probabilities. They also show how to generate an at-level pr curve using the class labels and predictions.**04:15:00**This video teaches how to use PyTorch to deep learn for classification. The video first shows how to script a model and how to save and load the model. Next, the video teaches how to use PyTorch's safe and load methods to save and load models. Finally, the video teaches how to use PyTorch's model.evo to save and load models with an evaluation method.**04:20:00**In this video, the author shows how to save and load a PyTorch model. The preferred way to do this is to use the "torch dot safe" command, which saves the state stick. The author also covers the lazy option, which is to load the model without initializing it. Finally, the author demonstrates how to compare the parameters of two models.**04:25:00**The video demonstrates how to save a model checkpoint and load it later, using the PyTorch library.**04:30:00**This 1-hour video tutorial explains how to use PyTorch to train deep learning models. The video walks through the process of loading a model into PyTorch, training the model on a set of data, and saving the model to disk. It also demonstrates how to use PyTorch's optimizer to find the best learning rate for the model.**04:35:00**This tutorial explains how to use a GPU to train a deep learning model.

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