Deep Learning Concepts

Neural Networks

• A neural network consists of neurons, which are linear functions with optional non-linear activations.
• A neuron's output is calculated as yi = Σ xj*wij + bi, where xi is the input, wij is the weight, and bi is the bias.

Deep Neural Networks

• A deep neural network is a neural network with multiple layers.
• Deeper neural networks have more layers, allowing for more complex representations of data.

Activations

• Activations are used for intermediate neurons to introduce non-linearity.
• Examples of activation functions include sigmoid, tanh, and ReLU.

Classifiers

• Classifiers are machine learning models that predict labels from features.
• Examples of classifiers include Support Vector Machines (SVM), Naïve Bayes, Logistic Regression, and Neural Networks.
• Deep Learning is a type of Neural Network.

Learning a Classifier

• The goal of learning a classifier is to predict labels from features.
• This can be visualized as finding a decision boundary in feature space.
• Examples of classifier learning algorithms include SVM (max margin hyperplane) and KNN (look at K neighbors and use their class).### Artificial Intelligence and Computer Vision Application Domains
• Automotive: Self-driving cars, Driver Assistance, Advanced Driver Assistance Systems (ADAS), Autonomous Driving
• Manufacturing: Automated visual inspection, Automated manufacturing, Autonomous vision guided vehicles
• Security and Defense: Surveillance systems, Traffic monitoring, Access control, Crowd monitoring, Violence Detection, Facial Recognition, Blacklist, VIP list, stranger detection, Airport security, office buildings, hotels, Automatic Speech Recognition, Speaker Identification, Terrorism Prevention, Radicalization Prediction w/ Social Network Analysis, Patrol Robots (UGVs)
• Agriculture: Disease detection, Precision agriculture, Automated harvesting, Drones/ satellite images
• Retail: People Counting, Theft detection, Queue Detection, Customer tracking, favorite areas, Automated Checkout
• Healthcare: Computer Aided Diagnosis, Precision Medicine, Computational biochemistry/Drug discovery
• Entertainment: Cinema Visual Effects, Digital Game Interaction, Digital Content Generation

Artificial Intelligence and Computer Vision Tasks

• Image Classification
• Object Detection
• Semantic Segmentation
• Instance Segmentation
• Tracking
• Many other CV tasks

Machine Learning and Deep Learning

• Traditional Approach: Hand-crafted features, Classifiers, Computer Vision tasks
• Deep Learning Approach: Convolutional Neural Networks (CNNs), Features learned from data

Neural Networks

• Neural Networks for Classification in Computer Vision
• Evaluation and Metrics
• Training Neural Networks
• Implementation challenges
• Neural Networks for other Computer Vision tasks
• More Neural Networks

Machine Learning

• Supervised Learning: Training, Training Labels, Training Images, Learned model, Prediction, Testing
• Features and Classifiers
• Evaluation and Metrics Overview

Test your knowledge of advanced neural network architectures, including RNN, LSTM, GRU, GAN, CNN, and Transformers. This quiz covers various deep learning concepts and models used in AI and machine learning.