3. Introduction to Key Concepts: AI Algorithms, Hardware Acceleration, and Neural Network Architectures
AI algorithms, hardware acceleration, and neural network architectures are essential components of modern AI systems, enabling them to function efficiently and at scale. Different types of algorithms cater to various learning paradigms, while hardware accelerators like GPUs and TPUs significantly enhance computational capabilities. Additionally, neural network architectures play a crucial role in achieving complex task execution, as seen in applications ranging from image recognition to natural language processing.
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What we have learnt
- AI algorithms define how machines learn from data and make decisions.
- Hardware acceleration is essential for speeding up AI computations.
- Neural network architectures influence the performance and capability of AI models.
Key Concepts
- -- AI Algorithms
- The methods through which machines learn from data and make decisions; categorized into supervised, unsupervised, and reinforcement learning.
- -- Hardware Acceleration
- The use of specialized computing hardware like GPUs, TPUs, and FPGAs to enhance the speed and efficiency of AI computations.
- -- Neural Network Architectures
- The structural design of neural networks, which determines their ability to learn and generalize from data, including types such as FNNs, CNNs, RNNs, and transformers.
- -- Deep Learning
- A subset of machine learning using multi-layered neural networks capable of learning complex patterns in large datasets.
- -- Generative Adversarial Networks (GANs)
- A form of neural network architecture that comprises a generator and a discriminator to create new, synthetic instances of data.
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