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In this video, John Robinson and "Robinsn" share beautiful videos of movement between seasons. Sebderhy also shared an amazing video of an update that is no longer than the original unconditional update would have been. John Robinson explains how he created the video by mistake, using the principles of reinforcement learning.

**00:00:00**In this video, John Robinson and "Robinsn" share beautiful videos of movement between seasons. Sebderhy also shared an amazing video of an update that is no longer than the original unconditional update would have been.**00:05:00**In this video, John Robinson explains how he created a beautiful video by mistake using the principles of reinforcement learning. In the first version, he used an unconditioned prompt plus the guidance times the difference between the conditional and unconditioned. The next version used rescaling the difference and rescaling the update to change the direction the prediction went. Finally, he shows how another user's idea of modifying gradients based on additional loss functions is similar to his own.**00:10:00**In this video, Johno teaches students how to read and use a paper called DiffEdit. DiffEdit is a new paper that was just released and is related to deep learning. The paper is available for download on arXiv, and after downloading it, students can open it in Zotero.**00:15:00**This paper discusses a new method for semantic image editing, which uses text condition diffusion models to generate a mask that can be used to edit an image. The results of the paper are impressive, and it is recommended that readers skip it if they do not wish to learn more about the method.**00:20:00**The video introduces the idea of an academic paper being full of citations, and goes on to discuss the different techniques that are used in academic papers. It finishes with a brief overview of DiffEdit, which is described as "amazing."**00:25:00**The authors of this paper discuss how diffusion-based image editing can be difficult to understand without knowledge of the Greek alphabet. They go on to say that, even if you do understand the equations, it is still important to read the background material last in order to understand the problem the author is trying to solve.**00:30:00**This lesson provides information on mathematical symbols, including their meanings and uses. The first technique to learn is how to find the meaning of a symbol using a program, MathPix. The second technique is to look for a symbol in a glossary or definition. Finally, the lesson discusses the meaning of a symbol, specifically the root sum of squares and the weird E symbol.**00:35:00**In this video, the LaTeX conversion process is explained, and the expected value operator is introduced. The process of calculating the mean and probability of various outcomes is also discussed. Finally, a practical application is given in the form of calculating the expected score of a die roll.**00:40:00**This video explains how expected value works, and how it is used to calculate the loss function of a neural network. The equations explained are related to background and noise, but are not actually used in the loss function calculation.**00:45:00**In this lesson, we learned about diffusion models and how they work. We covered the basics of DDPM and DDIM encoding, as well as denoising. We then went on to discuss DiffEdit and how it works. Finally, we looked at how diffusion from a fuzzy object is done, and how the background is preserved during inference.**00:50:00**This video describes a technique for generating images that are similar to those found in existing images, but with changes that are easy to make. The video provides examples of how the technique works and discusses its limitations. The video ends with a conclusion.**00:55:00**In this lesson, Diego demonstrates how to generate a mask of a horse that does not look like a zebra using code from Lesson 9 of the 2019 course. Step two of the paper walkthrough is to try and implement a call interface to the paper's forward pass.

This video explains how to perform matrix multiplication, which is a key operation in many mathematical and scientific disciplines. The video first demonstrates how to calculate the sizes of the matrices and vectors, and then shows how to perform the matrix multiplication. The video then explains how to speed up matrix multiplication using Numba, which is a system that takes Python and turns it into machine code.

**01:00:00**This video explains how to perform matrix multiplication, which is used to calculate the resultant tensor. The video first demonstrates how to calculate the sizes of the matrices and vectors, and then shows how to perform the matrix multiplication.**01:05:00**The video explains how to speed up matrix multiplication using Numba, which is a system that takes Python and turns it into machine code.**01:10:00**In this video, Fred discusses how NumPy arrays, Tensors, and PyTorch arrays are all equivalent and how to do element-wise addition on them with the prefix notation in each language. In Python, he demonstrates the same concept by replacing a loop with a call to dot and seeing the resulting performance difference. PyTorch is more verbose than Python, but this extra information is often helpful when performing mathematical operations.**01:15:00**In this lesson, the author discusses the idea of cognitive load, which is the total amount of information a person has to process at one time. He then goes on to explain how to do the same thing in APL, a programming language that is known for its concise and easy to read syntax.**01:20:00**In this video, the author demonstrates how to do simple arithmetic in APL, using dot products and element-wise multiplication.**01:25:00**This video tutorial explains how broadcasting works in NumPy, and shows how it can be used to combine Tensors of different shapes.**01:30:00**In this video, the author explains how to broadcast a vector across every row of a matrix using the expand_as() function. This function creates a new thing called t which contains the same thing as c but expanded or copied over so it has the same shape as m. t can then be added to m to match shapes, and m plus t can be used to play m plus t.**01:35:00**This video explains the notation c[:, None], which is used to indicate that the vector or matrix being expanded is to be applied to each column or row, respectively, of the array c[:, None]. This can be helpful when performing an outer product without the need for special functions.**01:40:00**In this video, the author explains how to normalize an image using the outer product. This normalization process is done by multiplying the image by a 1D array with 3 values, and then adding the resulting vector to the weight matrix. This allows for the fastai library to perform matrix multiplication much more quickly.**01:45:00**In this video, the author explains how matrix multiplication works, and shows how to do it using broadcasting. This is particularly helpful for deep learning and machine learning, as it is a foundational operation.

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