First experiment of the season with a neural network. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
«Machine intelligence is the last invention that humanity
will ever need to make.» Nick Bostrom.
I am going to start a series of videos focused on experiments with machine learning, in this one I want to show you another approach to calculate the smartvector, solving some of the problems we have in the edges, and getting overscan.
I will also show you the result by applying deep learning techniques.
I want to show you the benefits of using a neural network that allows us to perform the same process of inpaint, but in a much more accurate way and with much better results.
In this case I am going to use this flow-edge neural network, I will leave you all the links but you have it available on their GitHub to download and run directly on your computers.
But I’m going to use a much simpler option, which is a google collab notebook, these notebooks allow us to run python code, in this case our neural network without having to install anything. You can use this link to use the google collab notebook.
in my github repository you can find the nuke templates that I have used in the explanation of the video, and this is what you will find.
AI (Artificial Intelligence) refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as learning, problem solving, decision making, and language comprehension. There are different types of AI, including narrow AI (also called weak AI), which is designed to perform a specific task, and general AI (also called strong AI), which is designed to be able to perform any intellectual task that a human can. AI has the potential to revolutionize many industries and transform the way we live and work, but it also raises ethical and social questions that need to be carefully considered.
Deep learning is a subfield of machine learning that is inspired by the structure and function of the human brain, specifically the neural networks that make up the brain. It involves the use of artificial neural networks (ANNs) to learn patterns and relationships in data and make decisions based on that data. Deep learning algorithms are designed to learn and improve their performance over time through the use of large amounts of labeled data and computational power. They are able to learn and make decisions on their own, without the need for explicit programming or human intervention. Deep learning has been applied to a wide range of applications, including image and speech recognition, natural language processing, and self-driving cars.
Deep learning can be used to track objects in a video stream by training a model to recognize and locate the objects of interest in each frame. This is typically done using a type of deep learning model called a convolutional neural network (CNN), which is particularly well-suited for image recognition tasks.
To train a CNN to track objects, the model is fed a large dataset of video frames labeled with the location of the objects in each frame. The model then learns to recognize the objects and predict their location in each frame.
Once the model has been trained, it can be used to track objects in real-time by processing each frame of the video stream and predicting the location of the objects. The model’s predictions can be used to draw bounding boxes around the objects and follow their movements as they appear in successive frames.
There are many techniques and approaches for object tracking using deep learning, and the specific method used will depend on the specific requirements and constraints of the application.
Deep learning can be used to perform image inpainting, which is the process of filling in missing or damaged parts of an image. This can be useful for a variety of applications, including restoring old or damaged photographs, removing objects from images, and filling in missing pixels in images that have been resized or distorted.
To perform image inpainting using deep learning, a model is trained on a dataset of images with missing or damaged areas. The model is then fed an input image with a missing or damaged area and is trained to predict what the missing or damaged pixels should look like based on the context of the surrounding pixels.
There are several different approaches to deep learning-based image inpainting, including using generative adversarial networks (GANs) or encoder-decoder architectures. The specific approach used will depend on the specific requirements and constraints of the application.
Image inpainting using deep learning can produce highly realistic results, but it can also be computationally intensive and may require a lot of training data to achieve good performance.