Deep Learning Tutorial Lessons
A quick, chronological list of every single published video

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

Sum A List Of TensorFlow Tensors
Sum a list of TensorFlow Tensors using the TensorFlow add_n operation 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

Create A TensorFlow Placeholder Tensor
Create A TensorFlow Placeholder Tensor and then when it needs to be evaluated pass a NumPy multidimensional array into the feed_dict so that the values are used within the TensorFlow session
4:39

Transfer A 1D Tensor To A Vector Using TensorFlow
Transfer a 1D Tensor to a Vector using the TensorFlow squeeze transformation to remove the dimension of size 1 from the shape of the tensor
2:48

Calculate The ElementWise Hadamard Multiplication Of Matrices In PyTorch
Calculate the ElementWise 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

Get The PyTorch Tensor Shape
Get the PyTorch Tensor shape as a PyTorch Size object and as a list of integers
2:12

Print A Verbose Version Of A PyTorch Tensor
Print a verbose version of a PyTorch tensor so that you can see all of the elements rather than just seeing the truncated or shortened version
2:27

Calculate Mean of A Tensor Along An Axis Using TensorFlow
Calculate the mean of tensor elements along various dimensions of the tensor using TensorFlow by using the reduce_mean operation
4:32

Initialize TensorFlow Variables That Depend On Other TensorFlow 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

Convert PyTorch autograd Variable To NumPy Multidimensional Array
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

Calculate Max Of A Tensor Along An Axis Using TensorFlow
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

Calculate Max Of A TensorFlow Tensor
Calculate the max of a TensorFlow tensor using the TensorFlow reduce_max operation
2:34

Use TensorFlow reshape To Convert A Tensor To A Vector
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

Convert NumPy Array To 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

Convert A PyTorch Tensor To A Numpy Multidimensional Array
Convert A PyTorch Tensor To A Numpy Multidimensional Array so that it retains the specific data type
3:57

Print The Value Of A Tensor Object In TensorFlow
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

Concatenate TensorFlow Tensors Along A Given Dimension
Concatenate TensorFlow tensors along a given dimension using the TensorFlow concatenation concat functionality and then check the shape of the concatenated tensor using the TensorFlow shape functionality
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

Generate A Random Tensor In Tensorflow
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

Create A 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

Convert A NumPy Array To A PyTorch 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

Concatenate PyTorch Tensors Along A Given Dimension
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


Print And Check PyTorch Tensor Type
Print out the PyTorch Tensor type without printing out the whole PyTorch Tensor.
1:42

NumPy Array To Tensorflow Tensor And Back
Convert a NumPy array to a Tensorflow Tensor as well as convert a TensorFlow Tensor to a NumPy array.
1:30

Construct a PyTorch Tensor
Create an uninitialized PyTorch Tensor and an initialized PyTorch Tensor.
1:49