Course Description

Dive deep into the world of advanced deep learning with this comprehensive course on PyTorch! As the second part of a two-course series, "Deep Learning with Python and PyTorch" takes you beyond the basics and into the realm of building and training sophisticated deep neural networks. This intermediate-level course is designed to equip you with the skills and knowledge needed to construct, train, and optimize complex deep learning models using PyTorch, one of the most popular and powerful deep learning frameworks available today.

What You'll Learn

• Apply advanced knowledge of Deep Neural Networks and related machine learning methods
• Build and train complex Deep Neural Networks using PyTorch
• Construct efficient Deep Learning pipelines
• Implement multiclass classification and feed-forward neural networks
• Master state-of-the-art training methods and hyperparameter tuning
• Develop Convolutional Neural Networks for computer vision tasks
• Utilize GPUs for accelerated model training
• Implement transfer learning with pre-trained models
• Explore dimensionality reduction techniques and autoencoders
• Gain hands-on experience through a final project

Prerequisites

• Familiarity with PyTorch basics and practical knowledge of its application to Machine Learning
• Proficiency in Python programming and Jupyter notebooks
• Understanding of fundamental Machine Learning concepts
• Knowledge of basic Deep Learning principles
• Completion of the "PyTorch Basics for Machine Learning" course is highly recommended

Course Outline

• Multiclass classification and Softmax Regression in PyTorch
• Feed-forward neural networks: construction, training, and optimization
• Advanced deep neural network techniques: dropout, initialization, batch normalization
• Convolutional Neural Networks (CNNs) for computer vision tasks
• GPU-accelerated model training
• Transfer Learning with pre-trained models
• Dimensionality reduction techniques: PCA, data whitening
• Autoencoders: shallow, deep, and transfer learning applications
• Independent final project for practical application of learned skills

Who This Course Is For

This course is ideal for intermediate-level data scientists, machine learning engineers, and AI enthusiasts who want to take their deep learning skills to the next level. It's perfect for those who have a basic understanding of PyTorch and machine learning concepts but want to dive deeper into advanced techniques and real-world applications of deep learning.

Real-World Applications

• Developing advanced image recognition systems for autonomous vehicles or medical imaging
• Creating sophisticated natural language processing models for chatbots or sentiment analysis
• Building recommendation systems for e-commerce or content platforms
• Implementing fraud detection algorithms in the finance sector
• Designing predictive maintenance systems for industrial applications
• Advancing research in fields such as drug discovery, climate modeling, or astrophysics

Syllabus

Each module covers specific topics and techniques, progressively building upon the knowledge gained in previous sections. The course culminates in an independent project, allowing students to apply their newly acquired skills to a real-world problem.

Conclusion

By completing this course, you'll not only earn a valuable skill badge from IBM but also gain the expertise needed to tackle complex deep learning challenges in your professional career or research endeavors. Don't miss this opportunity to become a PyTorch expert and join the forefront of the AI revolution!