Introduction to Neural Networks and PyTorch

О курсе

Get ready to build the foundational PyTorch skills you need to launch your career as an AI Engineer – the fastest growing job title in the United States. Starting with tensors, this course takes you right through to fully trained classification models. You will master tensor operations, build custom datasets, and implement linear regression models using PyTorch's nn.Module and autograd system. Then, you will progress through gradient descent, stochastic and mini-batch training, loss functions, and training/validation workflows. Further, you will build logistic regression classifiers, apply cross-entropy loss, and implement advanced optimization and regularization techniques. Through interactive labs, instructional videos, and an AI-assisted dialogue, you will practice building, training, and evaluating models using real PyTorch code patterns. By the end, you will create a portfolio-worthy project that demonstrates your ability to perform PyTorch classification and gradient-based optimization tasks. Enroll now to enhance your resume and complete a project that showcases your hands-on skills in the AI-driven job market.

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Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Skills Network Team

Преподаватель курса