Sebastian Gutierrez

Sebastian Gutierrez is a Data Entrepreneur who has founded three data-related companies: DataYou (AI/Deep Learning, data science, & data visualization - consulting and education), LetsWombat (data-driven product sampling), and Acheevmo (athletic performance statistics).

https://www.sebgnotes.com

Browse Sebastian Gutierrez's Lessons

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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
Launch TensorFlow TensorBoard

Use the TensorBoard command line utility to launch the TensorFlow TensorBoard web service

1:23
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
Calculate The Number Of Elements In A PyTorch Tensor

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
Flatten A PyTorch Tensor

Flatten A PyTorch Tensor by using the PyTorch view operation

2:22
Reshape A PyTorch Tensor

Reshape A PyTorch Tensor by using the PyTorch view operation

3:34
Transpose A Matrix In PyTorch

Transpose A Matrix In PyTorch by using the PyTorch T operation

1:59
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
Clip PyTorch Tensor Values To A Range

Clip PyTorch Tensor values to a range by using the PyTorch clamp operation

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
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
Multiply Two Matrices Using TensorFlow MatMul

Multiply two matricies by using TensorFlow's matmul operation

3:35
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
Stack A List of TensorFlow Tensors Into One Tensor

Stack a list of TensorFlow Tensors of the same rank into one tensor by using tf.stack

4:58
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
Create A TensorFlow Tensor Full of Ones

Create a TensorFlow Constant Tensor full of ones so that each element is a one using the TensorFlow Ones operation.

2:40
Create A TensorFlow Tensor Full of 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
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 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
Calculate The Element-Wise Hadamard Multiplication Of Matrices In PyTorch

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
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

Use TensorFlow reduce_mean operation (tf.reduce_mean) to calculate the mean of tensor elements along various dimensions of the tensor

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
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
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
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
Create A PyTorch Variable

Create A PyTorch Variable which wraps a PyTorch Tensor and records operations applied to it.

1:36
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
Concatenate PyTorch Tensors Along A Given Dimension

Concatenate A List of PyTorch Tensors Along A Given Dimension

4:45
Cast A PyTorch Tensor To Another Type

Convert and Change a PyTorch Tensor To Another Type

3:06
Print And Check PyTorch Tensor Type

Print out the PyTorch Tensor type without printing out the whole PyTorch Tensor.

1:42
Construct a PyTorch Tensor

Create an uninitialized PyTorch Tensor and an initialized PyTorch Tensor.

1:49

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