Neural Network Essentials: Dense Network to LLMs скачать в хорошем качестве

Neural Network Essentials: Dense Network to LLMs

The video provides an introduction to artificial intelligence, specifically focusing on neural networks and their role in chatbots like ChatGPT. It begins with an analogy, comparing early web browsers and the evolution of web applications to the way neural networks function and evolve. The discussion then delves into the fundamental structure of neural networks, explaining how neurons (perceptrons) process inputs, apply weights, and pass data through activation functions. It covers key concepts such as forward propagation, backpropagation, dropout layers, and the importance of nonlinearity in neural networks. The video transitions to large language models (LLMs) like GPT, describing their ability to predict the next word in a sequence. It defines terms like Generative AI, Transformers, and Pre-training, illustrating how modern chatbots are trained with billions of parameters to improve accuracy. A comparison between small neural networks and models like GPT-3 highlights the vast complexity of AI systems. Finally, the video emphasizes AI’s limitations, including the risk of hallucinations (false or misleading outputs), reinforcing the need for critical thinking and verification when using AI-generated content. It concludes by encouraging engagement and continued learning in AI and data science. The thumbnail image was generated using Google Gemini (Imagen) ---------- 00:00 Introduction 00:20 Example of simple knowledge and big impact 00:46 Example of URL 01:07 Stateless protocol 01:28 Problems with stateless 02:30 Neuron, input, output 02:52 Weights and activation functions 03:18 Neural network terminology 03:45 Dropout layer 04:10 Back propagation 04:37 The world is extremely nonlinear 04:50 Example of activation functions 05:13 nonlinear definition 05:37 Dropout layers add randomness 05:56 Different names for chatbots 07:40 GPT weights (parameters) and layers 08:05 GPT training data and tokens 08:30 Key things to remember 09:10 Other phases of training 09:30 Conclusion ----------