Tutorials by Technology
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
PyTorch is an open source machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Meta AI. It is free and open-source software released under the Modified BSD license.
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected.
A quick, chronological list of every single published video
Specify PyTorch Tensor Minimum Value Threshold by using the PyTorch clamp operation
Use PyTorch clamp operation to clip PyTorch Tensor values to a specific range
Generate TensorFlow Tensor full of random numbers in a given range by using TensorFlow's random_uniform operation
Use the TensorFlow reshape operation to infer a tensor's new dimensions when reshaping a tensor
Calculate the biased standard deviation of all elements in a PyTorch Tensor by using the PyTorch std operation
Calculate the unbiased standard deviation of all elements in a PyTorch Tensor by using the PyTorch std operation
Calculate the power of each element in a PyTorch Tensor for a given exponent by using the PyTorch pow operation
Calculate the Sum of all elements in a tensor by using the PyTorch sum operation
Calculate the Mean value of all elements in a tensor by using the PyTorch mean operation
Use TensorFlow reshape to change the shape of a TensorFlow Tensor as long as the number of elements stay the same
Convert CIFAR10 Dataset from PIL Images to PyTorch Tensors by Using PyTorch's ToTensor Operation
Check for element wise equality between two PyTorch tensors using the PyTorch eq equality comparison operation
tf.matmul - Multiply two matricies by using TensorFlow's matmul operation
Create a PyTorch Tensor full of ones so that each element is a ones using the PyTorch Ones operation
Create a PyTorch Tensor full of zeros so that each element is a zero using the PyTorch Zeros operation
Examine the MNIST dataset from PyTorch Torchvision using Python and PIL, the Python Imaging Library
PyTorch MNIST - Load the MNIST dataset from PyTorch Torchvision and split it into a train data set and a test data set
TensorFlow Element Wise Multiply of Tensors to get the Hadamard product
tf.stack - How to use tf stack operation to stack a list of TensorFlow tensors
PyTorch CIFAR10 - Load CIFAR10 Dataset (torchvision.datasets.cifar10) from Torchvision and split into train and test data sets
TensorFlow Equal - Compare two tensors element wise for equality
tf.ones - How to use tf ones operation to create a TensorFlow ones Tensor
tf.zeros - How to use tf zeros operation to create a TensorFlow zeros Tensor
tf.constant_initializer - Use TensorFlow constant initializer operation to initialize a constant in TensorFlow
tf.variable - TensorFlow variable initialize with NumPy Values by using tf's get_variable operation
tf.constant - Create Tensorflow constant tensor with scalar value using tf constant operation.
TensorFlow Sum - Use TensorFlow's add_n (tf.add_n) to sum list of Tensors
Initialize TensorFlow variables with matrix of your choice. Example with identity matrix.
tf.placeholder - Create A TensorFlow Placeholder Tensor and then when it needs to be evaluated pass a NumPy multi-dimensional array into the feed_dict so that the values are used within the TensorFlow session
TensorFlow squeeze - Use tf.squeeze to remove a dimension from Tensor in order to transfer a 1-D Tensor to a Vector
PyTorch Element Wise Multiplication - Calculate the element wise multiplication to get the Hadamard Product
TensorFlow Add - Use TensorFlow's tf.add to add two Tensors together
get_tensor_by_name - TensorFlow get variable by name by using the TensorFlow get_default_graph operation and then the TensorFlow get_tensor_by_name operation
TensorFlow feed_dict example: Use feed_dict to feed values to TensorFlow placeholders so that you don't run into the error that says you must feed a value for placeholder tensors
PyTorch Tensor Shape - Get the PyTorch Tensor size as a PyTorch Size object and as a list of integers
PyTorch Print Tensor - Print full tensor in PyTorch so that you can see all of the elements rather than just seeing the truncated or shortened version
tf.reduce_mean - Use TensorFlow reduce_mean operation to calculate the mean of tensor elements along various dimensions of the tensor
TensorFlow Initialize Global Variables - Initialize TensorFlow Variables That Depend On Other TensorFlow Variables by using the TensorFlow initialized_value functionality
PyTorch Variable To NumPy - Transform a PyTorch autograd Variable to a NumPy Multidimensional Array by extracting the PyTorch Tensor from the Variable and converting the Tensor to the NumPy array
tf.reduce_max - Calculate the max of a TensorFlow tensor along a certain axis of the tensor using the TensorFlow reduce_max operation
Visualize the training results of running a neural net model with TensorFlow summary and TensorBoard
TensorFlow Max - Use tf.reduce_max to get max value of a TensorFlow Tensor
tf.reshape - Use TensorFlow reshape to convert a tensor to a vector by understanding the two arguments you must pass to the reshape operation and how the special value of negative one flattens the input tensor
MXNet NDArray - Convert A NumPy multidimensional array to an MXNet NDArray so that it retains the specific data type
Add Multiple Layers to a Neural Network in TensorFlow by working through an example where you add multiple ReLU layers and one convolutional layer
PyTorch Tensor to NumPy - Convert a PyTorch tensor to a NumPy multidimensional array so that it retains the specific data type
TensorFlow Print - Print the value of a tensor object in TensorFlow by understanding the difference between building the computational graph and running the computational graph
tf.concat - Use tf.concat, TensorFlow's concatenation operation, to concatenate TensorFlow tensors along a given dimension
Save The State Of A TensorFlow Model With Checkpointing Using The TensorFlow Saver Variable To Save The Session Into TensorFlow ckpt Files.
tf.random_uniform - Generate a random tensor in TensorFlow so that you can use it and maintain it for further use even if you call session run multiple times
Add Metrics Reporting to Improve Your TensorFlow Neural Network Model So You Can Monitor How Accuracy And Other Measures Evolve As You Change Your Model.
PyTorch Variable - create a PyTorch Variable which wraps a PyTorch Tensor and records operations applied to it
Train A One Layer Feed Forward Neural Network in TensorFlow With ReLU Activation, Softmax Cross Entropy with Logits, and the Gradient Descent Optimizer
PyTorch NumPy to tensor - Convert a NumPy Array into a PyTorch Tensor so that it retains the specific data type
Create a one layer feed forward neural network in TensorFlow with ReLU activation and understand the context of the shapes of the Tensors
PyTorch Concatenate - Use PyTorch cat to concatenate a list of PyTorch tensors along a given dimension
Import the MNIST data set from the TensorFlow Examples Tutorial Data Repository and encode it in one hot encoded format.
PyTorch change Tensor type - convert and change a PyTorch tensor to another type
PyTorch Tensor Type - print out the PyTorch tensor type without printing out the whole PyTorch tensor
Tensor to NumPy - Convert a NumPy array to a Tensorflow Tensor as well as convert a TensorFlow Tensor to a NumPy array
torch create tensor - Create an uninitialized PyTorch Tensor and an initialized PyTorch Tensor
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