AugMix() explained: no arguments, severity argument (1-3), mixture_width argument (2-3) and examples with OxfordIIITPet dataset.

AugMix() explained: no args & full arg, severity (2), mixture_width (1&2) and examples with OxfordIIITPet dataset.

RandAugment() explained: num_ops (default:2), magnitude (default:9), num_magnitude_bins (default:31), interpolation (default:NEAREST), fill (default:None) for image augmentation.

RandAugment() explained: num_ops (default:2), magnitude (default:9), num_magnitude_bins (default:31), interpolation (default:NEAREST), fill (default:None) for image augmentation.

FiveCrop() crops an image into 5 parts. Size argument can be int or tuple/list(int) with height and width.

CenterCrop() crops an image centering on it. The size argument can be a single value (int or tuple/list(int)) for [size, size] or a tuple/list with 1 or 2 elements for [height, width].

RandomInvert() randomly inverts images. It takes 2 args: img (PIL Image or tensor) & p (probability of inversion). p must be 0 <= x <= 1. OxfordIIITPet dataset is used to test RandomInvert().

Grayscale() converts images to grayscale. OxfordIIITPet() dataset requires 1 or 3 output channels. Use Grayscale(num_output_channels=1) for 1-channel images and num_output_channels=3 for 3-channel images.

CenterCrop() crops images centering on them. It takes a size argument which can be an int or tuple/list of ints. A tensor must be 2D or 3D. V1 is deprecated, use v2 instead.

RandomInvert() randomly inverts images. It takes 2 args: img (PIL Image or tensor) & p (probability of inversion). Use v2 for better performance.

Grayscale() converts images to grayscale. OxfordIIITPet() dataset requires 1 or 3 output channels. Use Grayscale(num_output_channels=1) for grayscale conversion.

FiveCrop() crops an image into 5 parts. Size can be int or tuple/list(int) with 1 <= x. Use OxfordIIITPet() for dataset and torchvision.transforms.v2.FiveCrop() for transformation.

JPEG compression applied to images with quality range [0, 100] using torchvision.transforms.v2.JPEG. Example usage: JPEG(quality=[50, 100])

ColorJitter(brightness=brightness_value, contrast=contrast_value, saturation=saturation_value, hue=hue_value)

RandomInvert() randomly inverts images. It takes 2 args: img (PIL Image or tensor) and p (probability of inversion). Use v2 for better performance.

RandomInvert() inverts images with a probability of 0 <= p <= 1. OxfordIIITPet() dataset uses RandomInvert() for data augmentation. Use v2 for better results.

RandomInvert() can randomly blur an image. It takes 2 args: img (PIL Image or tensor) & p (probability of inversion). p must be 0 <= x <= 1.

GaussianBlur() can randomly blur an image. It has kernel_size and sigma arguments. Use GaussianBlur(kernel_size=1) for a simple blur.

RandomCrop() without pad_if_needed argument explained. It randomly crops images with optional padding and fill values.

CenterCrop() crops images centering on them. It takes a size argument which can be an int or tuple/list of ints. The size must have at least one element and all elements must be >=1.

RandomRotation(degrees=d, interpolation=ip, expand=e, center=c, fill=f)

RandomHorizontalFlip() flips an image randomly and horizontally. It takes 2 args: img (PIL Image or tensor) & p (probability of flip). Example usage: RandomHorizontalFlip(p=0.5)

RandomVerticalFlip() flips an image randomly and vertically with a probability of 0 <= x <= 1. Use img as input and p for the flip probability. V2 is recommended over V1.

Use PyTorch's `RandomResizedCrop` transformation to randomly crop images. Define functions like `show_images` and `show_images2` to apply transformations with customizable size, scale, ratio, and interpolation.

FiveCrop() crops an image into 5 parts. Use size=(100, 100) or size=100 for initialization.

CenterCrop() crops images centering on them. Use size=(100, 100) or size=100 for square crops.

Update Ubuntu, check Python version (e.g. `python3 --version`), install python3.x-venv & create virtual env (`python3 -m venv venv && . venv/bin/activate`). Install PyTorch with CUDA 11.8 and JupyterLab.

FiveCrop() crops an image into 5 parts. Use FiveCrop(size=(100, 100)) or FiveCrop(size=100) for a square crop. For non-square crops, use FiveCrop(size=[500, 394]).

Resize images with torchvision.transforms.v2.Resize. Optional arguments: size (int or tuple/list), interpolation (InterpolationMode), max_size (int), antialias (bool).

CenterCrop() crops zero or more images, centering on them. Use size=(100, 100) for a square crop or size=600 for a rectangular crop.

Python script generates images using `show_images2` but lacks clarity & documentation. Redundant calls, magic numbers & unclear purpose hinder understanding. More context needed for a helpful answer.

Padding images is a technique used to make them consistent in size for tasks like image classification, object detection & segmentation. It adds zeros around edges to ensure entire objects are visible.

RandomPerspective() can do perspective transformation for zero or more images. It has 4 arguments: distortion_scale (default:0.5), p (default:0.5), interpolation (default:BILINEAR) and fill (default:0).

Update Ubuntu, check Python version (e.g. `python3 --version`), install python3.x-venv & create virtual env (`python3 -m venv venv && . venv/bin/activate`). Install PyTorch with CUDA 11.8 and JupyterLab.

Modified Python script fixes issue with accessing non-existent indices in datasets. Checks for index existence before displaying images, displaying default image if index doesn't exist.

Code displays images with annotations from various datasets using matplotlib & COCO API. Issues: undefined variables, missing imports, unclear data structure. Improve by defining data loading process, consistent imports & documenting data structure.

Exploring popular datasets in computer vision: ImageNet, LSUN, MS COCO, Fashion-MNIST & more. Learn about their features, uses & PyTorch implementations.

StanfordCars() dataset can be used with torchvision.datasets. It requires root path and split type (train/test). Download is optional but set to False due to broken URL.

CocoCaptions() explained using MS COCO dataset with train2017, val2017, and unlabeled2017. CocoDetection() also covered for train2014, val2014, test2017, train2017, and panoptic_train2017.

Consistent naming conventions, type hints, docstrings & error handling improve code readability & robustness. Updated `show_images1()` & `show_images2()` functions demonstrate these improvements.

pms_stf_train2017_data` & `pms_stf_val2017_data` undefined in code. Load COCO data with `pycocotools` library, assign to variables before accessing. Example: `coco = COCO('path_to_your_dataset/instances_train2017.json')

Modified code to display COCO dataset images with annotations: iterates over indices directly, removes unnecessary line. Works for cap_train2017_data, cap_val2017_data, test2017_data, testdev2017_data.

Displaying images with various types of annotations using matplotlib. The `show_images` function takes in a dataset and displays images along with their corresponding annotations, customizable to attribute, identity, bounding box or landmark data.

Learn how to use `torch.gcd()` and `torch.lcm()` functions in PyTorch for calculating greatest common divisors and least common multiples of tensors. Examples included.

Learn how to use `pow()` in PyTorch with tensors and scalars. Get the power of a tensor or scalar raised to another tensor or scalar's value. Examples included!

torch.square()` returns squared values for tensors with various dimensions & data types (ints, floats, complex nums, bools). Example: `torch.tensor([-3])` becomes `tensor(9)`.

argwhere() and nonzero() explained: get indices of non-zero elements in tensors with torch or PyTorch.

all() checks if all elements in a tensor are True. Returns 0D or more D tensor of zero or more elements. Example: `torch.all(input=torch.tensor(True))` returns `tensor(True)

any() checks if any elements of a tensor are True, returning the 0D or more D tensor of zero or more elements. It can be used with torch or a tensor, and has optional arguments for dim and keepdim. An empty tensor returns False.

atleast_2d() in PyTorch: Converts 0D or 1D tensors to 2D tensors. Returns a tuple of tensors if multiple inputs, otherwise returns a single tensor.

atleast_1d() in PyTorch: Ensures 1D or higher dimensionality for tensors. Returns a tensor or tuple of tensors with at least 1D structure, depending on input arguments.

mul() in PyTorch: element-wise multiplication of tensors/scalars with support for int, float, complex & bool data types.

sub() can perform subtraction with tensors or scalars, returning a 0D or more D tensor of zero or more elements. It supports torch and tensors as input, with optional alpha parameter for element-wise multiplication.

addition of tensors in PyTorch using torch.add() function.

remainder() in PyTorch performs modulo operation on tensors or scalars, returning the remainder of input divided by other. It can handle zero or more elements and supports torch or tensor inputs.

fmod() in PyTorch performs modulo operation on tensors or scalars, returning the remainder of division with same sign as original tensor. Example: `torch.fmod(input=tensor1, other=tensor2)` or `tensor1.fmod(other=tensor2)`.

Learn how to set and get device in PyTorch with tensor(), arange(), rand(), rand_like(), zeros() and zeros_like().

Learn how to use out argument in PyTorch functions like arange(), rand(), add(), mean(), median(), min(), max(), all(), any() and matmul().

arange() creates a 1D tensor of zero or integers or floating-point numbers between start and end-1. With torch, it has optional arguments: start, end, step, dtype, device, requires_grad, and out.

torch.linspace(start, end, steps) creates a 1D tensor of evenly spaced values between start & end (inclusive). Optional args: dtype, device, requires_grad, & out.

squeeze() removes 1D dimensions from tensors. Use torch.squeeze() or tensor.squeeze(). Specify dim to remove specific dimensions. Examples: `torch.squeeze(input=my_tensor)` and `my_tensor.squeeze(dim=(0, 3))`.

unsqueeze() adds 1D or more D tensor of zero or more elements with additional dimension whose size is 1 from the 0D or more D tensor. Used with torch or a tensor, it adds dimension at specific position.

range() creates a sequence of numbers. zip() pairs values from multiple sequences. enumerate() adds indices to iterables.

QMNIST dataset explained. QMNIST() loads 6 datasets: train, test, test10k, test50k, nist and FashionMNIST(). It returns images and labels.

MNIST dataset explained. MNIST() can use MNIST dataset with 5 arguments: root, train, transform, target_transform, download.

KMNIST() explained: a dataset for handwritten Kanji character recognition. Use KMNIST() with torchvision.datasets to load and preprocess data.

RandomVerticalFlip() flips images vertically with a probability of 0.5 by default. It can be used to augment datasets like OxfordIIITPet().

Fashion-MNIST dataset can be used with FashionMNIST() function. It requires root path and optional arguments for train data, transform, target transform, and download.

CIFAR100() loads CIFAR-100 dataset. Args: root(str/pathlib.Path), train(bool), transform(callable), target_transform(callable), download(bool). Default: train=True, transform=None, target_transform=None, download=False.

CIFAR10() uses CIFAR-10 dataset with 5 args: root(str), train(bool), transform(callable), target_transform(callable), download(bool).

select() can get the 0D or more D view tensor of elements selected with an index, removing one dimension from the tensor. It's used with torch or a tensor, requiring input, dim and index as arguments.

Batch Gradient Descent, Mini-Batch Gradient Descent and Stochastic Gradient Descent explained with PyTorch examples.

GELU() & Mish() mitigate Vanishing Gradient Problem & Dying ReLU Problem. They're computationally expensive but effective alternatives to traditional activation functions like ReLU. Used in PyTorch, Transformer models like BERT & ChatGPT.

Exploring popular loss functions in PyTorch: L1 Loss, L2 Loss, Huber Loss, BCE Loss & Cross Entropy Loss.

Explained: Batch, Mini-Batch & Stochastic Gradient Descent in PyTorch. Optimizers like SGD(), RMSprop() and Adam() accelerate convergence.

Huber Loss explained: a balance between L1 and MSE losses. Learn how to use it in PyTorch with examples.

BCELoss() computes binary cross-entropy loss between input and target tensors. It can be initialized with weight and reduction arguments.

Flatten() and ravel() explained: removing dimensions from tensors with optional start_dim and end_dim arguments. Examples in PyTorch.

Learn IPython magic commands for Unix/Linux and Git operations in this post! %pwd shows current directory, %ls lists files & folders, %cd changes dir, %rm removes items. Also, clone private repos with FGPAT or PAT from Github.

Learn PyTorch with practical examples. Save and load models, understand Linear Regression, Batch Gradient Descent and more. Clone private repos with PAT or SSH keys. Visualize data and predictions.

Learn PyTorch with Linear Regression, save & load models, and explore DataLoader() and IPython magic commands.

Exploring popular neural network layers: Recurrent, LSTM, GRU, Transformer, activation functions, loss functions, optimizers & more!

Adam() optimizer explained in 250 characters: "Adam() optimizes gradient descent with Momentum & RMSProp. Args: params, lr, betas, eps, weight_decay, amsgrad, foreach, maximize, capturable, differentiable, fused.

RMSProp explained: automatically adapts learning rate to parameters, uses 8 arguments for initialization and step() updates parameters. Example usage with PyTorch's RMSprop optimizer.

masked_select() gets elements from tensor with masks. It's used with torch or a tensor, taking input and mask as arguments. Example: `torch.masked_select(input=my_tensor, mask=torch.tensor([True, True, False]))` returns selected elements.

Exploring linalg.matrix_norm() in PyTorch: compute matrix norms with various options and examples.

Explained: linalg.vector_norm() in PyTorch for vector norms (L0, L1, L2, etc.) with optional arguments like dim and dtype.

Dropout() in PyTorch: - Randomly zeros or multiplies elements from input tensor. - p (default=0.5): probability of an element to be zeroed (0 <= x <= 1). - inplace (default=False): performs operation in-place, keep False for stability.

Layer Normalization explained: LayerNorm() computes mean & variance for each feature dimension, normalizing inputs to have 0 mean & unit variance.

Explaining Batch Normalization Layer in PyTorch: BatchNorm3d() explained with examples and code snippets.

Explaining PyTorch's BatchNorm2d: A 4D tensor normalization layer with 7 initialization arguments and a required input tensor of float values.

AvgPool3d() explained: 3D average pooling for tensors, kernel_size, stride, padding & more. Example usage with PyTorch and tensor manipulation.

Exploring PyTorch's Linear Layer: Initialization & Usage

Learn about expm1() and sigmoid() in PyTorch: calculate ex - 1 and Sigmoid function for tensors with examples and code snippets.

Learn how to use `exp()` and `exp2()` in PyTorch for element-wise exponentiation, returning float tensors unless input is complex.

Tanh() and Softsign() explained: compute zero or more values from input tensors using PyTorch nn modules.

Exploring popular activation functions in PyTorch: ReLU, LeakyReLU, PReLU & more. Learn how to use them with code examples and understand their behavior.

Exploring PyTorch's heaviside() and Identity() functions for tensor operations.

Torch.nan and torch.inf explained. Learn how to replace NaNs and infinities with zeros or specified values using nan_to_num() function.

mode() function in PyTorch returns the most frequently appeared elements and their indices from a tensor. It can handle tensors of different dimensions and data types.