zero_to_gpt
Z
Zero To Gpt
Overview :
zero_to_gpt is a tutorial aimed at helping users learn deep learning from the ground up, ultimately enabling them to train their own GPT models. As AI technologies emerge from labs and find wide applications across various industries, the demand for professionals who can understand and apply AI is increasing. This tutorial integrates theory and practice by addressing real-world problems (such as weather prediction and language translation) to explore the theoretical foundations of deep learning, including gradient descent and backpropagation. The course content starts with basic neural network architectures and training methods, gradually advancing to complex topics such as transformers, GPU programming, and distributed training.
Target Users :
This tutorial is designed for beginners interested in deep learning, particularly those looking to master AI technologies for practical problem-solving. Whether you are a student, researcher, or industry practitioner, this course offers a systematic approach to deep learning concepts, laying a solid foundation for future career development.
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Use Cases
Students learned and understood the basic principles of deep learning through this tutorial and successfully implemented a simple neural network model.
Researchers accelerated the training process of large deep learning models using the distributed training techniques covered in the tutorial.
Industry practitioners enhanced their expertise in natural language processing by following this tutorial, leading to the development of efficient language translation services for their companies.
Features
Provide foundational theory of deep learning
Cover core algorithms such as gradient descent and backpropagation
Teach users how to build deep learning models using the PyTorch framework
Guide text data processing for training language models like GPT
Introduce transformer models to address vanishing or exploding gradients in RNNs
Explore distributed training technologies to enhance the efficiency of large model training
How to Use
Visit the tutorial page and read the course introduction
Choose whether to take the foundational courses in math and NumPy based on your background
Progressively learn theoretical concepts such as gradient descent and neural networks in the order provided
Implement the code examples from the tutorial through hands-on practice
Learn to build and train models using the PyTorch framework
Explore advanced applications of text data processing and transformer models
Upon completing the course, attempt to independently train a GPT model
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