VII: Deep Learning Flashcards
What is deep learning?
Deep learning is a subfield of machine learning that focuses on training artificial neural networks with multiple layers to automatically learn and extract hierarchical representations of data. By using deep neural networks, which are composed of interconnected layers of neurons, deep learning models can effectively capture complex patterns and relationships in large datasets. Deep learning has revolutionized various fields, including computer vision, natural language processing, and speech recognition, by achieving state-of-the-art performance on tasks such as image classification, object detection, language translation, and more. It leverages the power of neural networks and extensive computational resources to tackle challenging problems and achieve remarkable accuracy.
What are deep neural networks?
Deep neural networks, also known as deep learning models, are artificial neural networks with multiple hidden layers between the input and output layers. These networks are designed to learn hierarchical representations of data, enabling them to capture complex patterns and relationships. Each layer consists of interconnected neurons that transform input signals using weighted connections and activation functions. Deep neural networks leverage the power of these multiple layers to learn increasingly abstract and sophisticated features from the data. This allows them to tackle complex tasks such as image recognition, natural language processing, and speech synthesis with superior performance compared to traditional machine learning models.
What are convolutional neural networks?
Convolutional neural networks (CNNs) are deep learning models specifically designed for analyzing visual data. They consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers. CNNs utilize convolutional operations to extract local features from input images, capturing spatial relationships. Pooling layers downsample the feature maps, reducing computational complexity. Finally, fully connected layers combine the extracted features and make predictions. CNNs are effective in tasks like image classification, object detection, and image segmentation, as they exploit the shared weights and hierarchical structure to learn intricate visual patterns and achieve state-of-the-art performance in computer vision tasks.
How does image recognition using deep learning work?
Image recognition using deep learning involves training deep neural networks on large labeled image datasets. Deep learning models, such as convolutional neural networks (CNNs), are capable of automatically learning relevant features and patterns from raw pixel data. During training, the network adjusts its internal parameters through backpropagation, optimizing them to minimize the error between predicted and actual labels. Once trained, the model can classify and recognize objects in new images by feeding them through the network and obtaining class probabilities or labels. Deep learning has revolutionized image recognition, enabling highly accurate and scalable solutions for various applications like object detection, image segmentation, and facial recognition.
What is max pooling?
Max pooling is a pooling operation commonly used in convolutional neural networks (CNNs) for downsampling feature maps. It partitions the input into non-overlapping regions and replaces each region with the maximum value. By retaining the maximum activation within each region, max pooling helps extract the most prominent features while reducing the dimensionality of the feature maps. This improves computational efficiency, reduces overfitting, and provides a form of translation invariance, enabling the network to capture important spatial information at different scales.
When is a pipeline applied?
A pipeline is applied to describe the handling of the stack of multiple networks. A pipeline feeds an output of one network into the next networks: hence, an output of one layer is used directly by another layer.
How is a deep learning network taught?
Training a deep learning network involves several steps, including defining the architecture, initializing the weights, and optimizing them using backpropagation. Activation functions play a crucial role in introducing non-linearity to the network, allowing it to learn complex patterns.
- Rectified Linear Unit (ReLU) is a popular activation function used in deep learning. It outputs the input if it is positive and zero otherwise. ReLU helps the network overcome the vanishing gradient problem and accelerates convergence by introducing non-linearity.
- Softmax function is commonly used for multi-class classification tasks. It converts a vector of real numbers into a probability distribution by exponentiating each element and normalizing them. The output represents the probability of each class, allowing us to interpret the network’s prediction as the most likely class.
During training, the network uses labeled data to compute the loss, which quantifies the difference between predicted and true labels. The backpropagation algorithm then calculates the gradient of the loss with respect to the network’s weights. These gradients are used to update the weights using optimization algorithms like gradient descent, which iteratively adjusts the weights to minimize the loss. By combining activation functions like ReLU to introduce non-linearity, softmax for multi-class classification, and training with backpropagation, deep learning networks can effectively learn complex representations and make accurate predictions across various tasks.
What does reuse mean?
It means that a convolutional network can be reused. Given the cost of training, reusing is a great aspect of deep learning systems.
What are Recurrent neural networks?
Recurrent neural networks are considered feedforward networks with feed backward connections representing time sequences between passes through the network. RNN is useful for time series, sound processing and written text. RNN uses hidden state to remember information on the sequence. RNN have units where connections between these units form a directed cycle. For each unit, the directed cycle is a recurrent visit of the same unit.
What are some challenges for deep learning?
Challenges in deep learning include the need for massive labeled data, potential overfitting, high computational requirements, interpretability of complex models, vulnerability to adversarial attacks, lack of generalization in some cases, and difficulty in fine-tuning hyperparameters for optimal performance.
