Deep Learning Concepts Recap

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Questions and Answers

Which framework has the highest ease of use?

All frameworks have the same ease of use

TensorFlow

PyTorch

Keras (correct)

What aspect differentiates deep learning from traditional machine learning in terms of feature engineering?

Deep learning uses automatic feature engineering. (correct)

Deep learning requires manual feature engineering.

Both methods require extensive feature engineering.

Traditional ML uses automatic feature engineering.

What is the primary programming language used in TensorFlow?

Java

JavaScript

Python (correct)

C++

Which deep learning framework is developed by Google Brain?

TensorFlow Signup and view all the answers

Which of the following statements regarding the data requirements of deep learning is true?

Deep learning requires large datasets. Signup and view all the answers

What type of graphs does PyTorch utilize?

Dynamic graphs Signup and view all the answers

Which of the following statements is true about the computational needs of deep learning?

Deep learning requires higher computational power. Signup and view all the answers

In terms of model complexity, how does deep learning compare to traditional machine learning?

Deep learning models are more complex than traditional ML models. Signup and view all the answers

Which feature does Keras primarily depend on?

Backbone frameworks Signup and view all the answers

Which method is noted for having excellent handling of unstructured data?

Deep learning. Signup and view all the answers

Which framework provides TensorBoard for visualization?

Both TensorFlow and PyTorch Signup and view all the answers

What kind of community support does TensorFlow enjoy?

Very Large Signup and view all the answers

How does the interpretability of models differ between traditional machine learning and deep learning?

Traditional ML models are generally more interpretable. Signup and view all the answers

What is a key concept associated with TensorFlow?

Static and Dynamic execution Signup and view all the answers

What is one of the primary advantages of deep learning in terms of scalability?

Deep learning is highly scalable. Signup and view all the answers

Which of the following historical milestones is associated with the concept of backpropagation?

1986: Hinton, Rumelhart, and Williams publish a paper. Signup and view all the answers

What is a primary cause of overfitting in a deep learning model?

Too many model parameters relative to training data Signup and view all the answers

Which technique is used to promote sparsity in a model during training?

L1 Regularization (Lasso) Signup and view all the answers

What visual method can help detect whether a model is overfitting or underfitting?

Learning curves Signup and view all the answers

How does underfitting manifest in a model's performance?

Low accuracy on both training and validation data Signup and view all the answers

Which factor is essential for balancing model complexity with generalization?

Adequate feature representation Signup and view all the answers

What does L1 Regularization add to the loss function to control model complexity?

The absolute value of weights Signup and view all the answers

Which issue is likely responsible for a model displaying high accuracy on training data but low accuracy on validation/test data?

Model overfitting Signup and view all the answers

What could be a primary consequence of using a model that is adjusted for resource-constrained devices in IoT?

Reduced model size and energy consumption Signup and view all the answers

What is a primary limitation of gradient descent?

It is prone to overfitting, especially with small datasets. Signup and view all the answers

Which operation in CNNs helps to reduce the size of feature maps while enhancing computational efficiency?

Pooling operation Signup and view all the answers

What is the main purpose of the convolution operation in CNNs?

To apply filters that learn spatial hierarchies of features. Signup and view all the answers

Which of the following is true about the architecture of LeNet?

It consists of two convolutional layers and two fully connected layers. Signup and view all the answers

What key advantage do convolutional neural networks offer over traditional neural networks?

They can adaptively learn spatial hierarchies of features from input images. Signup and view all the answers

What does the term 'stride' refer to in the context of CNNs?

The movement of the filter during the convolution operation. Signup and view all the answers

Which CNN architecture is known for utilizing small $3x3$ convolution filters and great depth?

VGGNet Signup and view all the answers

In which of the following applications are CNNs particularly effective?

Image classification Signup and view all the answers

What is a significant advantage of edge devices in terms of data processing?

They provide real-time, low-latency processing. Signup and view all the answers

Which characteristic is true regarding the storage capabilities of edge and cloud systems?

Edge systems typically have limited storage capacity. Signup and view all the answers

How does Federated Learning primarily differ from traditional machine learning?

It trains models across decentralized devices. Signup and view all the answers

What is a primary consideration when comparing edge and cloud in terms of cost structure?

Cloud has higher ongoing costs due to potential data transfer. Signup and view all the answers

Which of the following frameworks is specifically intended for edge AI?

TensorFlow Lite Signup and view all the answers

What is a common deployment challenge associated with edge devices?

They often require complex deployment processes. Signup and view all the answers

Which hardware accelerator is NOT specifically mentioned for edge devices?

AMD Ryzen Signup and view all the answers

Which of the following statements about energy efficiency is true regarding edge and cloud computing?

Edge can be more energy-efficient for local processing. Signup and view all the answers

What is a key aspect of fine-tuning the ResNet50 model?

Custom layers are added on top for the specific classification task. Signup and view all the answers

Which step is NOT necessary when deploying a Flask API for the model?

Create a GUI for user interaction. Signup and view all the answers

What must be considered for cloud deployment of the trained model?

Follow the specific instructions for the chosen cloud platform. Signup and view all the answers

Which of the following is a resource constraint in IoT environments?

Limited memory for device operations. Signup and view all the answers

What is the purpose of model pruning in deep learning?

To remove unnecessary weights and neurons to reduce model size. Signup and view all the answers

Which of the following is a challenge when applying deep learning to IoT?

Bandwidth limitations and intermittent connectivity. Signup and view all the answers

What is an essential consideration during the deployment of a machine learning model for production use?

Implementing proper error handling and logging. Signup and view all the answers

In the context of deep learning for IoT, what does environmental factor refer to?

It encompasses the conditions sensors operate in. Signup and view all the answers

Study Notes

Recap - Deep Learning Study Notes

This document is a recap of deep learning concepts, presented on 16/09/2024, by Chourouk Guettas.
It covers various aspects of deep learning, from fundamental concepts to advanced techniques and frameworks.
The document is organized into sections, each focusing on a specific topic within the broader field of deep learning.
The topics covered include: Deep Learning: The hype and why?, Neural Network Fundamentals, Deep Neural Network Architectures, Training Deep Neural Networks, Advanced Deep Learning Concepts, Deep Learning Frameworks and Tools, and Deep Learning for IoT.
The table of contents lists the specific subsections and page numbers for each topic, allowing easy navigation.
The document details deep learning principles, including its definition, historical context, comparisons with traditional machine learning, and various architectures like MLPs, CNNs, RNNs, and Autoencoders.
It also discusses training procedures, regularization techniques, hyperparameter adjustments, and advanced concepts like GANs and attention mechanisms.
Furthermore, it covers deep learning frameworks (e.g., TensorFlow, PyTorch, Keras) and deployment methodologies, including GPU acceleration and distributed training.
Finally, the document explores the application of deep learning in IoT, addressing challenges and optimization techniques relevant to this specialized domain.

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Description

This quiz summarizes key concepts in deep learning, including fundamental ideas, architectures, and training methods. It provides an organized overview of topics such as neural networks, advanced techniques, and frameworks. Ideal for both beginners and advanced learners aiming to test their knowledge in this rapidly evolving field.