Published 12/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.35 GB | Duration: 2h 43m
From Basics to Advanced Deep Learning Training
What you’ll 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
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
Description
The “Mastering PyTorch: From Basics to Advanced Deep Learning Training” course is a complete learning journey designed for beginners and professionals aiming to excel in artificial intelligence and deep learning. This course begins with the fundamentals of PyTorch, covering essential topics such as tensor operations, automatic differentiation, and building neural networks from scratch. Learners will gain a deep understanding of how PyTorch’s dynamic computation graph works, enabling flexible model creation and troubleshooting.As the course progresses, students will explore advanced topics, including complex neural network architectures such as CNNs, RNNs, and Transformers. It also dives into transfer learning, custom layers, loss functions, and model optimization techniques. Learners will practice building real-world projects, such as image classifiers, NLP-based sentiment analyzers, and GAN-powered applications.The course places a strong emphasis on hands-on implementation, offering step-by-step exercises, coding challenges, and projects that reinforce key concepts. Additionally, learners will explore cutting-edge techniques like distributed training, cloud deployment, and integration with popular libraries.By the end of the course, learners will be proficient in designing, building, and deploying AI models using PyTorch. They will also be equipped to contribute to open-source projects and pursue careers as AI engineers, data scientists, or ML researchers in the growing field of deep learning.
Overview
Section 1: Introduction and Foundations
Lecture 1 Introduction to Learning PyTorch from Basics to Advanced Complete Training
Lecture 2 Introduction to PyTorch
Lecture 3 Getting Started with PyTorch
Section 2: Core Concepts and Model Building
Lecture 4 Working with Tensors
Lecture 5 Autograd and Dynamic Computation Graphs
Lecture 6 Building Simple Neural Networks
Section 3: Data Handling and Model Training
Lecture 7 Loading and Preprocessing Data
Lecture 8 Model Evaluation and Validation
Lecture 9 Advanced Neural Network Architectures
Lecture 10 Transfer Learning and Fine-Tuning
Section 4: Advanced Techniques and Deployment
Lecture 11 Handling Complex Data
Lecture 12 Model Deployment and Production
Lecture 13 Debugging and Troubleshooting
Lecture 14 Distributed Training and Performance Optimization
Section 5: Research, Customization, and Community
Lecture 15 Custom Layers and Loss Functions
Lecture 16 Research-oriented Techniques
Lecture 17 Integration with Other Libraries
Lecture 18 Contributing to PyTorch and Community Engagement
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
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