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From: Non-Technical | To: Technical | Writes: To Make It Easier For Everyone | Topics: #Setup #Basics #AI #MachineLearning #DeepLearning #Repositories

Deep Learning

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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 progressive resizing, mixup, and 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.

01. What is the difference between ImageNet and Imagenette? When is it better to experiment on one versus the other?

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…


DEEP LEARNING

The nice and easy tutorial with step-by-step instructions

Summary:

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.

  • It was inspired by methods in other fields such as image inpainting in computer vision and text infilling in natural language processing.
  • It scored higher in naturalness and speaker similarity than CycleGAN-VC2 and CycleGAN-VC3 which were accepted benchmarks at the time.


Deep Learning

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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.

01. How could multi-label classification improve the usability of the bear classifier?

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 0.0 and…


Deep Learning

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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.

01. Why do we first resize to a large size on the CPU, and then to a smaller size on the GPU?

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.

  1. It resizes the images to a large size to provide extra space to reduce the empty space and lost detail that occurs…

Deep Learning

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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.

01. How is a grayscale image represented on a computer? How about a color image?

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…


Deep Learning

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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.

01. Does ethics provide a list of “right answers”?

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. …


Deep Learning

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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.

01. Where do text models currently have a major deficiency?

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.

  • Contextual words: refers to words…

DEEP LEARNING

The answer key for the questionnaire at the end of the chapter

Chapter Summary:

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.

01. Do you need these for deep learning

  1. Lots of Math: The mathematics are usually handled in the background by most deep learning libraries. It only becomes necessary to fine tune the model and exceed state-of-the-art performance which does require linear algebra, multivariable calculus, and probability and statistics.
  2. Lots of Data: Small datasets can achieve similar results as large datasets when the data is high-quality…


Series: Repositories

The condensed guide with instructions and screenshots

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. …


An answer key for the questionnaire at the end of the chapter

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. …

David Littlefield

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