Deep Learning with PyTorch

О курсе

Get hands-on experience in building and deploying intelligent systems using PyTorch by using one of the most widely used deep learning frameworks in AI development. In this practical course, you’ll gain job-ready skills in deep learning, machine learning, and neural networks, boosting your resume for roles like AI Engineer, Machine Learning Engineer, and Data Scientist. During the course, you’ll implement logistic regression and softmax regression, train deep neural networks, and build convolutional neural networks (CNNs) for real-world image classification tasks. You’ll master core techniques such as gradient descent, backpropagation, and cross entropy loss, while improving performance with weight initialization, dropout regularization, and batch normalization. Additionally, you’ll leverage GPU acceleration, perform hyperparameter tuning, and apply transfer learning using pretrained models such as ResNet18. Finally, you’ll complete a project, where you’ll design, train, and evaluate models using modern model optimization and data preprocessing workflows. Great to talk about in interviews! Enroll today to accelerate your career in deep learning, AI, and machine learning.

Навыки, которые вы освоите

Учитесь у экспертов

Harish Pant

Instructor

Joseph Santarcangelo

Ph.D., Data Scientist at IBM