AI Workbox
Python Tutorial Screencast Videos
Watch these 110 Python deep learning tutorials
-
How To Define A ReLU Layer In PyTorch
Use PyTorch's nn.ReLU and add_module operations to define a ReLU layer
2:30
-
How To Define A Convolutional Layer In PyTorch
Use PyTorch nn.Sequential and PyTorch nn.Conv2d to define a convolutional layer in PyTorch
3:10
-
tf.transpose: Transpose A Matrix in TensorFlow
tf.transpose - Use TensorFlow's transpose operation to transpose a TensorFlow matrix tensor
2:23
-
How To Define A Sequential Neural Network Container In PyTorch
Use PyTorch's nn.Sequential and add_module operations to define a sequential neural network container
1:38
-
tf.dtype: Print And Check TensorFlow Tensor Type
tf.dtype - Use TensorFlow's dtype operation to print and check a TensorFlow's Tensor data type
2:21
-
PyTorch item: Convert A 0-dim PyTorch Tensor To A Python Number
PyTorch item - Use PyTorch's item operation to convert a 0-dim PyTorch Tensor to a Python number
1:50
-
PyTorch Min: Get Minimum Value Of A PyTorch Tensor
PyTorch Min - Use PyTorch's min operation to calculate the min of a PyTorch tensor
1:55
-
PyTorch Max: Get Maximum Value Of A PyTorch Tensor
PyTorch Max - Use PyTorch's max operation to calculate the max of a PyTorch tensor
1:56
-
Print PyTorch Version
Find out which version of PyTorch is installed in your system by printing the PyTorch version
0:59
-
PyTorch Matrix Multiplication: How To Do A PyTorch Dot Product
PyTorch Matrix Multiplication - Use torch.mm to do a PyTorch Dot Product
3:26
-
tf.reduce_min: Get Minimum Value Of A TensorFlow Tensor
tf.reduce_min - Use TensorFlow's reduce_min operation to get the minimum value of a TensorFlow Tensor
1:42
-
tf.random_uniform: Create TensorFlow Tensor With Random Uniform Distribution
Use TensorFlow's random_uniform operation to create a TensorFlow Tensor with a random uniform distribution
2:33
-
PyTorch Tensor To List: How To Convert A PyTorch Tensor To A List
Use PyTorch's To List (tolist) operation to convert a PyTorch Tensor to a Python list
2:08
-
Flatten A TensorFlow Tensor
Use the TensorFlow reshape operation to flatten a TensorFlow Tensor
3:17
-
Print TensorFlow Tensor Shape
Use the TensorFlow get_shape operation to print the static shape of a TensorFlow tensor as a list
2:35
-
Print TensorFlow Version
Find out which version of TensorFlow is installed in your system by printing the TensorFlow version
1:16
-
List All Tensor Names In A TensorFlow Graph
Use the TensorFlow Get Operations Operation to list all Tensor names in a TensorFlow graph
4:40
-
PyTorch List to Tensor: Convert A Python List To A PyTorch Tensor
PyTorch List to Tensor - Use the PyTorch Tensor operation (torch.tensor) to convert a Python list object into a PyTorch Tensor
2:01
-
Augment the CIFAR10 Dataset Using the RandomHorizontalFlip and RandomCrop Transforms
Augment the CIFAR10 Dataset Using the TorchVision RandomHorizontalFlip (transforms.RandomHorizontalFlip) and RandomCrop (transforms.RandomCrop) Transforms
3:20
-
Use Torchvision CenterCrop Transform To Do A Rectangular Crop Of A PIL Image
Use Torchvision CenterCrop Transform (torchvision.transforms.CenterCrop) to do a rectangular crop of a PIL image
3:33
-
Use Torchvision CenterCrop Transform To Do A Square Crop Of A PIL Image
Use Torchvision CenterCrop Transform (torchvision.transforms.CenterCrop) to do a square crop of a PIL image
3:40
-
Convert List To TensorFlow Tensor
Convert a python list into a TensorFlow Tensor using the TensorFlow convert_to_tensor functionality
2:21
-
Get The Shape Of A PyTorch Tensor As A List Of Integers
Get the shape of a PyTorch Tensor as a list of integers by using the PyTorch Shape operation and the Python List constructor
2:28
-
PyTorch Stack: Turn A List Of PyTorch Tensors Into One Tensor
PyTorch Stack - Use the PyTorch Stack operation (torch.stack) to turn a list of PyTorch Tensors into one tensor
3:03
-
-
Create TensorFlow Name Scopes For TensorBoard
Use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible
6:04
-
Visualize TensorFlow Graph In TensorBoard
Use TensorFlow Summary File Writer (tf.summary.FileWriter) and the TensorBoard command line unitility to visualize a TensorFlow Graph in the TensorBoard web service
4:23
-
Apply Transforms To PyTorch Torchvision Datasets
Use the Torchvision Transforms Parameter in the initialization function to apply transforms to PyTorch Torchvision Datasets during the data import process
1:51
-
Launch TensorFlow TensorBoard
Use the TensorBoard command line utility to launch the TensorFlow TensorBoard web service
1:23
-
Normalize CIFAR10 Dataset Tensor
Use Torchvision Transforms Normalize (transforms.Normalize) to normalize CIFAR10 dataset tensors using the mean and standard deviation of the dataset
1:42
-
Create TensorFlow Summary File Writer For TensorBoard
Use TensorFlow Summary File Writer (tf.summary.FileWriter) to create a TensorFlow Summary Event File for TensorBoard
4:17
-
Add A New Dimension To The End Of A Tensor In PyTorch
Add a new dimension to the end of a PyTorch tensor by using None-style indexing
2:10
-
Add A New Dimension To The Middle Of A Tensor In PyTorch
Add a new dimension to the middle of a PyTorch tensor by using None-style indexing
2:12
-
Add A New Dimension To The Beginning Of A Tensor In PyTorch
Add a new dimension to the beginning of a PyTorch tensor by using None-style indexing
1:37
-
PyTorch numel: Calculate The Number Of Elements In A PyTorch Tensor
PyTorch numel - Calculate the number of elements in a PyTorch Tensor by using the PyTorch numel operation
1:22
-
Create A PyTorch Identity Matrix
Create a PyTorch identity matrix by using the PyTorch eye operation
1:03
-
Move PyTorch Tensor Data To A Contiguous Chunk Of Memory
Use the PyTorch contiguous operation to move a PyTorch Tensor's data to a contiguous chunk of memory
5:59
-
Infer Dimensions While Reshaping A PyTorch Tensor
Infer dimensions while reshaping a PyTorch tensor by using the PyTorch view operation
4:00
-
-
PyTorch View: Reshape A PyTorch Tensor
PyTorch View - how to use the PyTorch View (.view(...)) operation to reshape a PyTorch tensor
3:34
-
-
Fill A PyTorch Tensor With A Certain Scalar
Fill A PyTorch Tensor with a certain scalar by using the PyTorch fill operation
2:09
-
Tell PyTorch To Do An In Place Operation
Tell PyTorch to do an in-place operation by using an underscore after an operation's name
2:48
-
Add Two PyTorch Tensors Together
Add two PyTorch Tensors together by using the PyTorch add operation
2:00
-
Specify PyTorch Tensor Maximum Value Threshold
Specify PyTorch Tensor Maximum Value Threshold by using the PyTorch clamp operation
1:59
-
Specify PyTorch Tensor Minimum Value Threshold
Specify PyTorch Tensor Minimum Value Threshold by using the PyTorch clamp operation
2:06
-
PyTorch Clamp: Clip PyTorch Tensor Values To A Range
Use PyTorch clamp operation to clip PyTorch Tensor values to a specific range
1:48
-
Generate TensorFlow Tensor Full Of Random Numbers In A Given Range
Generate TensorFlow Tensor full of random numbers in a given range by using TensorFlow's random_uniform operation
3:03
-
Use TensorFlow reshape To Infer Reshaped Tensor's New Dimensions
Use the TensorFlow reshape operation to infer a tensor's new dimensions when reshaping a tensor
6:28
-
Get The PyTorch Variable Shape
Get the PyTorch Variable shape by using the PyTorch size operation
1:56
-
Calculate The Biased Standard Deviation Of All Elements In A PyTorch Tensor
Calculate the biased standard deviation of all elements in a PyTorch Tensor by using the PyTorch std operation
4:47
-
Calculate The Unbiased Standard Deviation Of All Elements In A PyTorch Tensor
Calculate the unbiased standard deviation of all elements in a PyTorch Tensor by using the PyTorch std operation
5:00
-
Calculate The Power Of Each Element In A PyTorch Tensor For A Given Exponent
Calculate the power of each element in a PyTorch Tensor for a given exponent by using the PyTorch pow operation
2:04
-
Calculate The Sum Of All Elements In A PyTorch Tensor
Calculate the Sum of all elements in a tensor by using the PyTorch sum operation
2:00
-
Calculate The Mean Value Of All Elements In A PyTorch Tensor
Calculate the Mean value of all elements in a tensor by using the PyTorch mean operation
2:01
-
Use TensorFlow reshape To Change The Shape Of A Tensor
Use TensorFlow reshape to change the shape of a TensorFlow Tensor as long as the number of elements stay the same
5:29
-
Convert CIFAR10 Dataset from PIL Images to PyTorch Tensors
Convert CIFAR10 Dataset from PIL Images to PyTorch Tensors by Using PyTorch's ToTensor Operation
1:15
-
Check For Element Wise Equality Between Two PyTorch Tensors
Check for element wise equality between two PyTorch tensors using the PyTorch eq equality comparison operation
3:00
-
tf.matmul: Multiply Two Matricies Using TensorFlow MatMul
tf.matmul - Multiply two matricies by using TensorFlow's matmul operation
3:35
-
Create A PyTorch Tensor Full Of Ones
Create a PyTorch Tensor full of ones so that each element is a ones using the PyTorch Ones operation
1:17
-
Create A PyTorch Tensor Full Of Zeros
Create a PyTorch Tensor full of zeros so that each element is a zero using the PyTorch Zeros operation
1:20
-
Examine MNIST Dataset from PyTorch Torchvision
Examine the MNIST dataset from PyTorch Torchvision using Python and PIL, the Python Imaging Library
2:57
-
PyTorch MNIST: Load MNIST Dataset from PyTorch Torchvision
PyTorch MNIST - Load the MNIST dataset from PyTorch Torchvision and split it into a train data set and a test data set
2:11
-
Calculate The Element-Wise Hadamard Multiplication Of Two TensorFlow Tensors
Calculate the element-wise Hadamard multiplication of two TensorFlow tensors by using tf.multiply
4:10
-
tf.stack: Stack A List of TensorFlow Tensors Into One Tensor
tf.stack - Stack a list of TensorFlow Tensors of the same rank into one tensor by using tf.stack
4:58
-
Load CIFAR10 Dataset From PyTorch Torchvision
Load the CIFAR10 dataset from PyTorch Torchvision and split it into a train data set and a test data set
1:59
-
Check For Element Wise Equality Between Two TensorFlow Tensors
Check for element wise equality between two TensorFlow Tensors by using the TensorFlow equal operator to do the comparison.
5:08
-
tf.ones: Create A TensorFlow Tensor Full of Ones
tf.ones - Create a TensorFlow Constant Tensor full of ones so that each element is a one using the TensorFlow Ones operation
2:40
-
tf.zeros: Create A TensorFlow Tensor Full of Zeros
tf.zeros - Create a TensorFlow Constant Tensor full of zeros so that each element is a zero using the TensorFlow Zeros operation
2:52
-
Calculate Column Sum In TensorFlow
Do a column sum in TensorFlow using tf.reduce_sum to get the sum of all of the elements in the columns of a Tensor
3:33
-
Use TensorFlow Constant Initializer To Do Simple Initialization
Use the TensorFlow constant_initializer operation to do a simple TensorFlow Variable creation such that the initialized values of the variable get the value that you pass into it.
2:58
-
Initialize A TensorFlow Variable With NumPy Values
Initialize a TensorFlow Variable with NumPy values by using TensorFlow's get_variable operation and setting the Variable initializer to the NumPy values
3:15
-
Create A TensorFlow Constant Tensor Populated With A Scalar Value
Create a TensorFlow Constant Tensor populated with a scalar value by using the TensorFlow Constant operation as well as defining the shape and data type
3:16
-
tf.add_n: Sum A List Of TensorFlow Tensors
Use TensorFlow's tf.add_n operation to sum a list of tensors so that you can add more than two TensorFlow Tensors together at the same time
3:55
-
Initialize TensorFlow Variable As Identity Matrix
Initialize a TensorFlow Variable as the identity matrix of the shape of your choosing using the TensorFlow Variable Functionality and the Tensorflow Eye Functionality
3:11
-
Create An Identity Matrix Using TensorFlow
Create An Identity Matrix Using The TensorFlow Eye Functionality
3:03
-
tf.placeholder: Create A TensorFlow Placeholder Tensor
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
4:39
-
Transfer A 1-D Tensor To A Vector Using TensorFlow
Transfer a 1-D Tensor to a Vector using the TensorFlow squeeze transformation to remove the dimension of size 1 from the shape of the tensor
2:48
-
PyTorch Element Wise Multiplication: Element-Wise Matrix Multiplication
PyTorch Element Wise Multiplication - Calculate the Element-Wise multiplication of matrices in PyTorch to get the Hadamard Product
2:59
-
Add Two TensorFlow Tensors Together
Add two TensorFlow Tensors together by using the TensorFlow add operation
2:57
-
Get A TensorFlow Tensor By Name
Get A TensorFlow Tensor By Name by using the TensorFlow get_default_graph operation and then the TensorFlow get_tensor_by_name operation
2:32
-
Use feed_dict To Feed Values To TensorFlow Placeholders
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
3:35
-
PyTorch Tensor Shape: Get the PyTorch Tensor size
PyTorch Tensor Shape - Get the PyTorch Tensor size as a PyTorch Size object and as a list of integers
2:12
-
PyTorch Print Tensor: Print Full Tensor in PyTorch
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
2:27
-
tf.reduce_mean: Calculate Mean of A Tensor Along An Axis Using TensorFlow
tf.reduce_mean - Use TensorFlow reduce_mean operation to calculate the mean of tensor elements along various dimensions of the tensor
4:32
-
TensorFlow Initialize Global Variables: Initialize TensorFlow Variables That Depend On Other TensorFlow Variables
TensorFlow Initialize Global Variables - Initialize TensorFlow Variables That Depend On Other TensorFlow Variables by using the TensorFlow initialized_value functionality
5:00
-
Convert MXNet NDArray to NumPy Multidimensional Array
Convert an MXNet NDArray to a NumPy Multidimensional Array so that it retains the specific data type using the asnumpy MXNet function
2:40
-
PyTorch Variable To NumPy: Convert PyTorch autograd Variable To NumPy Multidimensional Array
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
3:30
-
tf.reduce_max: Calculate Max Of A Tensor Along An Axis Using TensorFlow
tf.reduce_max - Calculate the max of a TensorFlow tensor along a certain axis of the tensor using the TensorFlow reduce_max operation
6:24
-
Visualize Training Results With TensorFlow summary and TensorBoard
Visualize the training results of running a neural net model with TensorFlow summary and TensorBoard
4:09
-
TensorFlow Max: Use tf.reduce_max To Get Max Value Of A TensorFlow Tensor
TensorFlow Max - Use tf.reduce_max to get max value of a TensorFlow Tensor
2:34
-
tf.reshape: Use TensorFlow reshape To Convert A Tensor To A Vector
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
4:18
-
MXNet NDArray: Convert NumPy Array To MXNet NDArray
MXNet NDArray - Convert A NumPy multidimensional array to an MXNet NDArray so that it retains the specific data type
3:47
-
Add Layers To A Neural Network In TensorFlow
Add Multiple Layers to a Neural Network in TensorFlow by working through an example where you add multiple ReLU layers and one convolutional layer
4:19
-
PyTorch Tensor to NumPy: Convert A PyTorch Tensor To A Numpy Multidimensional Array
PyTorch Tensor to NumPy - Convert a PyTorch tensor to a NumPy multidimensional array so that it retains the specific data type
3:57
-
TensorFlow Print: Print The Value Of A Tensor Object In TensorFlow
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
3:36
-
tf.concat: Concatenate TensorFlow Tensors Along A Given Dimension
tf.concat - Use tf.concat, TensorFlow's concatenation operation, to concatenate TensorFlow tensors along a given dimension
4:55
-
Save The State Of A TensorFlow Model With Checkpointing
Save The State Of A TensorFlow Model With Checkpointing Using The TensorFlow Saver Variable To Save The Session Into TensorFlow ckpt Files.
3:27
-
tf.random_uniform: Generate A Random Tensor In Tensorflow
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
4:09
-
Add Metrics Reporting To Improve Your TensorFlow Neural Network Model
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.
4:38
-
PyTorch Variable: Create A PyTorch Variable
PyTorch Variable - create a PyTorch Variable which wraps a PyTorch Tensor and records operations applied to it
1:36
-
Train A One Layer Feed Forward Neural Network in TensorFlow With ReLU Activation
Train A One Layer Feed Forward Neural Network in TensorFlow With ReLU Activation, Softmax Cross Entropy with Logits, and the Gradient Descent Optimizer
3:00
-
PyTorch NumPy to tensor: Convert A NumPy Array To A PyTorch Tensor
PyTorch NumPy to tensor - Convert a NumPy Array into a PyTorch Tensor so that it retains the specific data type
1:53
-
Create A One Layer Feed Forward Neural Network In TensorFlow With ReLU Activation
Create a one layer feed forward neural network in TensorFlow with ReLU activation and understand the context of the shapes of the Tensors
2:04
-
PyTorch Concatenate: Concatenate PyTorch Tensors Along A Given Dimension With PyTorch cat
PyTorch Concatenate - Use PyTorch cat to concatenate a list of PyTorch tensors along a given dimension
4:45
-
Load The MNIST Data Set in TensorFlow So That It Is In One Hot Encoded Format
Import the MNIST data set from the Tensorflow Examples Tutorial Data Repository and encode it in one hot encoded format.
2:29
-
PyTorch Change Tensor Type: Cast A PyTorch Tensor To Another Type
PyTorch change Tensor type - convert and change a PyTorch tensor to another type
3:06
-
PyTorch Tensor Type: Print And Check PyTorch Tensor Type
PyTorch Tensor Type - print out the PyTorch tensor type without printing out the whole PyTorch tensor
1:42
-
Tensor to NumPy: NumPy Array To Tensorflow Tensor And Back
Tensor to NumPy - Convert a NumPy array to a Tensorflow Tensor as well as convert a TensorFlow Tensor to a NumPy array
1:30
-
torch create tensor: Construct a PyTorch Tensor
torch create tensor - Create an uninitialized PyTorch Tensor and an initialized PyTorch Tensor
1:49
AI Workbox