Monthly Archives: August 2023

26. Learning XOR Let’s begin with a question which I’d like you to keep in mind as you go through the video. Learning or reverse engineering the XOR function requires more than one neuron. Each of those neurons, each of those multiple neurons that we are going to use can suffice with only an affine […]

24. Individual Neuron Here is a question that I’d like us to consider as we learn the contents of the video. Neural networks consist of complex building blocks which have been interconnected in simple ways. Is this statement true or false? Let’s now really hone in on the role that neurons play in the learning […]

20. One-hot Notation and L1 Distance As we go through this video, here is a question that I’d like you to think about. We’ve discussed the one hot notation. The question is, can this one hot notation or representation be used to hold continuous data items such as heights or weights? Or is this only […]

22. Lab: K-Nearest-Neighbors How can a single array of size 784 represent a two dimensional image in the MNIST data set? If you remember the MNIST data set, each image is 28 pixels by 28 pixels. In this lecture, we’ll see an implementation of the Key Nearest Neighbors algorithm in TensorFlow. The MNIST data set […]

15. Images As Tensors Here is a question that I’d like you to keep in mind as we go through the contents of this video. There are different color formats out there. RGB and grayscale are two very common ones. Another common choice is CMYK. That’s an acronym for cyan, magenta, yellow and Key. Key […]

17. Lab: Image Transformations This lecture, we’ll learn what exactly the TF stack method does when it’s applied to a list of tensors. The last practical demo we read in a bunch of images and then resize them to all be of the same size. In this demo, we’ll perform some further transformations on images […]

10. Placeholders and Variables Here is a question that I’d like you to think about as we go through the contents of this video. Let’s say we have a TensorFlow program which is seeking to learn linear regression I e. This is a TensorFlow implementation of linear regression. Are the regression parameters going to be […]

12. Lab: Variables How would you initialize the variables that you set up in your TensorFlow program? This lecture will help you find the answer to this question. We are continuing to work on very simple math operations in TensorFlow. This time we’ll introduce a new concept variables. Variables in TensorFlow, just like variables in […]

7. Tensors Concepts of the rank and the shape of a tensor are both really important ones. So here is a question that I’d like you to keep in mind as we process this video. The rank of a tensor is an integer. For instance, a scalar has ranked zero, and so on. The shape […]

5. Lab: Simple Math Operations At the end of this lecture, you should be able to clearly define the two phases in any TensorFlow program. TensorFlow is a very powerful numerical computation framework that was open sourced by Google in November 2015. Representation of all computations as a directed acyclic graph makes it very suitable […]