Chapter 7 provides an overview of advanced techniques for performing image classification. It covers the
normalization technique that’s used to preprocess the data. It covers the
label smoothing techniques that are used to train the model. It covers the
test time augmentation technique that’s used to perform inference with the model. It also briefly covers overfitting, overconfidence, and prototyping.
ImageNet: A dataset that contains more than
14 million images that are hand-annotated to indicate what objects are in the images. It contains
27 categories that are organized into over
21 a thousand subcategories. It uses WordNet…
MaskCycleGAN-VC: An extension of CycleGAN-VC2 that uses non-parallel voice conversion to train voice converters without data of speakers uttering the same sentences. It uses a novel auxiliary task called filling-in-frames that applies a temporal mask to the input mel-spectrogram and encourages the converter to fill in the missing frames based on the surrounding frames.
Chapter 6 provides an overview of
multi-label classification. It introduces the mechanics of using the
DataBlock class for
multi-label classification. It covers how preprocessing, loss functions, and metrics are different for
multi-classification tasks. It also covers one-hot encoding, dataframes,
lambda functions, and debunks a myth about tuning the hyperparameters.
Multi-Label Classification: An
image classification task where each image has zero or more instances of two or more labels. It uses the
sigmoid function in the
final layer of the deep learning model to convert the
predicted values in the
input tensor into
probability values that range between the
The 5th chapter from the Fastai textbook provides an overview of the fine-tuning process. It introduces the mechanics of working with the dataset, performing data augmentation, and choosing the right loss function. It also covers the process of finding and implementing the optimal learning rate.
The images are resized from the largest size, the original size, to a large size on the CPU to produce higher quality images for training the model to create a more accurate model.
Chapter 4 provides an overview of the training process. It covers loading datasets, making predictions, measuring loss, calculating gradients, and updating weights and biases. It also covers some of the tensor operations, activation functions, loss functions, optimizer functions, and learning rate.
Grayscale Image: An image with one channel that’s represented as a 2-dimensional matrix. It contains pixel values that represent the intensity of light for each pixel in the image where zero is a black pixel, 255 is a white pixel, and all the values in between are the different shades of gray pixels.
Color Image: An image with three…
Chapter 3 provides an overview of the ethical issues that exist in the artificial intelligence field. It covers some cautionary tales, unintended consequences, and ethical considerations. It also covers the biases that contribute to the ethical issues and the tools that help mitigate them.
Ethics: A branch of philosophy that involves systematizing, defending, and recommending concepts of right and wrong behavior. It seeks to resolve questions of human morality by defining concepts such as good and evil, right and wrong, virtue and vice, and justice and crime. …
Chapter 2 provides an overview of building the model and preprocessing the data. It covers some of the capabilities, limitations, challenges, and considerations that are related to building the model. It also covers some of the challenges and considerations that are related to deploying the model.
Text models currently struggle to produce factually correct responses when asked questions about factual information. They can generate responses that appear compelling to laymen but are entirely incorrect. This problem is attributed to the current challenges in natural language processing which include contextual words, homonyms, synonyms, sarcasm, and ambiguity.
Chapter 1 provides a broad overview of artificial intelligence. It covers some history, prerequisites, theories, applications, milestones, terminology, and mechanics of the subject. It also demonstrates some code that’s used to load the dataset, train the model, and make predictions using different models.
N2N-Watermark Remove is a repository that removes watermarks from watermarked images. It can train the model to identify and remove watermarks that are fixed in size and located in different positions in an image. It also showcases advancements in Deep Learning technology which can produce near-perfect results without damaging the details in the image.
This guide walks through how to remove a complex watermark using the N2N-Watermark-Remove repository. It covers installing all the necessary prerequisites, installing all the required dependencies, and removing a watermark using the pre-trained model. …
The 4th chapter of the textbook provides an overview of the training process. It provides a detailed introduction to measuring the loss, calculating the gradient, and updating the weights. It also covers some of the mechanics of the training process which includes tensor operations, activation functions, loss functions, optimizer functions, and learning rate.
We’ve spent many weeks writing the questionnaires. And the reason for that, is because we tried to think about what we wanted you to take away from each chapter. …