Explore Deep Learning with PyTorch: 100 Projects in 100 Days

Dive deep into the world of artificial intelligence with “Mastering PyTorch – 100 Days: 100 Projects Bootcamp Training.” This comprehensive course is meticulously designed for both beginners and experienced professionals eager to excel in deep learning and AI. Starting from the ground up, it introduces learners to the fundamental aspects of PyTorch, where you’ll embark on a journey covering tensor operations, automatic differentiation, and the creation of neural networks from scratch. Understanding PyTorch’s dynamic computation graph is pivotal, and this course empowers you with that knowledge, making model creation intuitive and troubleshooting effective.

As the course unfolds, students will venture into advanced deep learning topics such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and the latest Transformer models. You will engage with practical applications, utilizing transfer learning techniques, developing custom layers, and employing model optimization strategies. Real-world projects filter through the curriculum, ensuring that you not only learn but apply your skills through hands-on exercises – building sentiment analyzers, image classifiers, and generative adversarial networks (GANs). With an emphasis on practical implementation, the course also delves into modern methodologies like distributed training and integrating with cloud services, setting you up for success in a sophisticated AI landscape.

By the conclusion of this course, you’ll not only command a thorough understanding of designing and deploying deep learning models but also be positioned to contribute to open-source projects, making a significant impact in fields like data science and machine learning. This course equips you with the skills needed to launch or elevate your career as an AI engineer or ML researcher in the dynamic, fast-paced world of artificial intelligence.

What you will learn:

• Understand PyTorch fundamentals, including tensors and computation graphs
• Build and train neural networks using PyTorch’s nn_Module
• Preprocess and load datasets with DataLoaders and custom datasets
• Implement advanced architectures like CNNs, RNNs, and Transformers
• Perform transfer learning and fine-tune pre-trained models
• Optimize models using hyperparameter tuning and regularization
• Deploy trained models using TorchScript and cloud services
• Debug and troubleshoot deep learning models effectively
• Develop custom layers, loss functions, and models
• Collaborate with the PyTorch community and contribute to open-source projects

Course Content:

• Sections: 6
• Lectures: 118
• Duration: 4h 18m

Requirements:

• Basic Computer Skills: Familiarity with using a computer and installing software
• Python Programming: Basic knowledge of Python (variables, functions, loops)
• Mathematics: Understanding of basic algebra, linear algebra, and calculus concepts (vectors, matrices, derivatives)
• Machine Learning Basics (optional): Awareness of ML concepts like models, training, and evaluation is helpful but not mandatory
• Enthusiasm to Learn: A willingness to learn through hands-on projects and experiments.

Who is it for?

• Beginners in AI/ML: Those with no prior deep learning experience but eager to learn PyTorch from scratch
• Data Science Enthusiasts: Aspiring data scientists looking to add PyTorch to their ML toolkit
• Developers and Engineers: Software developers transitioning into AI and deep learning roles
• Researchers and Academics: Those exploring cutting-edge ML research using PyTorch
• Career Switchers: Professionals transitioning to AI-related careers.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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