AI, Neural Networks, Large Language Models, and Humanoids

About this course

Artificial intelligence (AI) has recently emerged to be a technology revolution that is able to provide benefits beyond traditional rules-based approaches. AI and neural networks are able to overcome the complexities and optimize the performance of communications networks, computer graphics, multimedia and language processing systems, data science, navigation and voice assistants, and numerous applications. Using 228 informative slides, two interesting projects, and Python code, this course will equip participants with the foundational knowledge on the key building blocks of naturally intelligent learning systems and generative AI, including large-scale language models used in the global phenomenon ChatGPT. It also contains a short quiz.

Learning Outcomes

Overview of AI and machine learning, and their capabilities

Study the design of ChatGPT, Llama, Claude, Gemini, Grok, and Deep SeekNeural network architecture and implementation, including perceptron and adaline training, backpropagation and attractor networks with memory, recurrent networks, biological and competitive learning, self-organizing map, and interpretable modeling using the Kolmogorov-Arnold network

Generative AI using supervised, reinforcement, unsupervised training

Speech, image, and video AI enhancements using recurrent neural network, convolutional neural network (AlexNet, Unet), Markov and diffusion models, and Adam optimizer

ChatGPT components, including tokenization, embedding, encoding, decoding, language and post processing

About the Instructor

The instructor has worked at the Georgia Institute of Technology for many years and has been an active speaker for the IEEE and industry. He has published over 70 scientific papers and has trained hundreds of engineers from various companies around the globe. More recently, six of his books were adopted by AI companies such as Anthropic as training

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