Before we start learning AI , we should understand what the existing systems can do , so that we can appreciate what AI brings
So here you will learn about the key differences between AI and Non-AI Systems
Introductory topics
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AI in many industries
In this section i will explain how AI is being used or will be used in many industries. Note that AI is tech which will help other industries just like information technology.
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What is Generative AI and AI terminologies
Generative AI refers to a subset of artificial intelligence techniques where algorithms are designed not just to recognize patterns or classify data but to generate new content, whether it's images, text, music, or even entire sequences of events. This field encompasses a range of technologies and methodologies, including but not limited to: Generative Adversarial Networks (GANs): A type of neural network architecture where two networks (generator and discriminator) compete against each other to generate increasingly realistic data. GANs are widely used in generating images, videos, and other forms of media. Recurrent Neural Networks (RNNs): A class of neural networks designed to handle sequential data, such as text or speech. They are often used in natural language processing (NLP) tasks like language generation and text prediction. Transformer Networks: A type of neural network architecture based on self-attention mechanisms, originally designed for NLP tasks like translation and language modeling. Transformers have been highly successful in generating coherent and contextually relevant text. Variational Autoencoders (VAEs): A type of generative model that learns a low-dimensional representation of data (latent space) and can generate new data points that resemble the training data. Autoregressive Models: Models that generate output one element at a time, conditioning each prediction on previous elements. Examples include autoregressive language models like GPT (Generative Pretrained Transformer) series. Natural Language Generation (NLG): A subfield of AI focused on generating natural language output from structured data or other inputs. NLG is essential for applications like chatbots, summarization, and storytelling. AI Terminologies: Machine Learning: A branch of AI where algorithms learn patterns and make predictions from data without explicit programming. Deep Learning: A subset of machine learning using deep neural networks with many layers, capable of learning from large amounts of data. Supervised Learning: Learning where the model is trained on labeled data, i.e., data with input-output pairs provided. Unsupervised Learning: Learning where the model is trained on unlabeled data and must find patterns or groupings within the data. Reinforcement Learning: Learning where an agent learns to make decisions by interacting with an environment and receiving feedback in terms of rewards or penalties. Neural Networks: Computing systems inspired by the biological neural networks that constitute animal brains. Bias-Variance Tradeoff: The balance between model complexity and generalization performance. Overfitting and Underfitting: Problems where a model learns too much from noise or too little from data, respectively. Transfer Learning: Technique where a model trained on one task is reused as a starting point for a model on a different task. Data Augmentation: Techniques to increase the diversity of training data without collecting more data, often used in image and text processing. These terms and concepts are fundamental in understanding the capabilities and applications of AI and its various subfields like generative AI.
Practicals
In this section there will be demos or practical implementations where you can follow and do the same and learn some implementations or development of AI
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