Applied Deep Learning with PyTorch

Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The course begins with the basics of deep learning and PyTorch, guiding learners through building single-layer neural networks and progressing to more complex architectures like convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for natural language processing. Learners will also explore style transfer techniques and sequence analysis, gaining hands-on experience with real-world data.

This course is ideal for data scientists, analysts, and developers who want to apply deep learning techniques using PyTorch. With a focus on practical implementation, it helps participants build confidence in solving advanced data problems. Prior knowledge of Python and machine learning fundamentals is required, while familiarity with libraries like NumPy and Pandas is helpful but not essential.

Contents:

• Introducing Deep Learning and PyTorch to understand the fundamentals of neural networks and how PyTorch facilitates model development.
• Building Blocks of Neural Networks to construct and train single-layer and multi-layer networks for various learning tasks.
• Solving Classification Problems Using DNN to apply deep neural networks for categorizing data based on learned patterns.
• Building Convolutional Neural Networks to perform image classification and feature extraction using spatial hierarchies.
• Applying Style Transfer Techniques to creatively transform images by blending content and artistic styles using deep learning.
• Analyzing Sequences with RNNs to process and predict sequential data such as text or time series using recurrent neural networks.

This course is intended for data scientists, analysts, and developers who want to apply deep learning techniques using PyTorch to solve advanced data problems. It is suitable for individuals with working knowledge of Python and a solid understanding of machine learning fundamentals. Learners will benefit from practical experience in building neural networks, applying convolutional and recurrent architectures, and exploring techniques like style transfer and sequence analysis. The course is ideal for those looking to deepen their expertise in AI through hands-on implementation and real-world applications.

Certificate of Attendance, issued by Semos Education upon successful completion of the course.