That's interesting and novel. Deep learning neural networks learn from large numbers of examples. If you have 100,000 example pictures of people plus 100,000 examples of how the corresponding alien pictures would look, then you can use those as training data to train a neural network.btb4198 said:I have a picture of a guy's face and I had an artist draw him as an alien. How do I make a neural network that would take in the original picture and learn how to draw the picture the that artist make?
I want to make a filter, that if you can enter in any guy's face, and the program would draw is like if it was the artist.
I hope you really mean that. A quarter million really is a lot. If you mean a few dozen, it won't work well.btb4198 said:I have a lot of pictures
I don't know about Prequel, but it is possible that they did it by some means other than deep learning trained neural networks.btb4198 said:Question,
How does you think Prequel does it's cartoon me?
Do you think they have a quarter Million of face pictures, because I really did not think it would take that many. That seems excessive.
Right now I have 139 different face Pictures redrawing by the same artist.
Jarvis323 said:Here is a video explaining how it works.
Deep learning is a subset of machine learning that uses artificial neural networks to process and analyze large amounts of data. It differs from other types of neural networks in that it has multiple hidden layers, which allows it to learn and make decisions on its own without being explicitly programmed.
A deep learning neural network works by taking in a large amount of data and using multiple layers of interconnected nodes to process and extract features from the data. These features are then combined to make predictions or decisions based on the input data. The network is trained through a process called backpropagation, where the weights of the connections between nodes are adjusted to minimize the error in its predictions.
Deep learning has a wide range of applications, including image and speech recognition, natural language processing, autonomous vehicles, and robotics. It is also used in industries such as healthcare, finance, and marketing for tasks such as data analysis, predictive modeling, and fraud detection.
Deep learning has several advantages over traditional machine learning algorithms, including the ability to handle large and complex datasets, automatic feature extraction, and the ability to learn and improve with more data. It also has the potential to outperform traditional algorithms in terms of accuracy and efficiency.
One limitation of deep learning is its reliance on large amounts of labeled data for training, which can be time-consuming and costly to obtain. It also requires significant computing power and resources to train and run the models. Additionally, deep learning models can be difficult to interpret and explain, making it challenging to understand how they make decisions.