What will you in Practical Deep Learning with PyTorch Course
Understand deep learning fundamentals and how to implement them using PyTorch.
Build and train neural networks from scratch.
Master convolutional neural networks (CNNs) for image processing tasks.
Learn to manage overfitting, loss functions, and optimization techniques.
Gain hands-on experience with real-world datasets and model evaluation.
Program Overview
Module 1: Introduction to Deep Learning & PyTorch
⏳ 30 minutes
Core principles of deep learning and how PyTorch fits in.
Setting up the development environment and working with tensors.
Module 2: Building Neural Networks
⏳ 60 minutes
Structure of a neural network: layers, activation, loss, and optimizers.
Creating and training your first model using PyTorch.
Module 3: Training & Evaluation Techniques
⏳ 45 minutes
Data preprocessing, batching, and training loops.
Model evaluation metrics like accuracy and loss tracking.
Module 4: Convolutional Neural Networks (CNNs)
⏳ 60 minutes
Understanding CNN architecture and use cases.
Implementing a CNN for image classification.
Module 5: Avoiding Overfitting & Model Optimization
⏳ 45 minutes
Techniques like dropout, regularization, and data augmentation.
Hyperparameter tuning and model checkpointing.
Module 6: Real-World Projects with PyTorch
⏳ 90 minutes
Applying deep learning to real datasets (e.g., MNIST, CIFAR-10).
Building an end-to-end classification project.
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Job Outlook
High Demand: Deep learning engineers are in demand across industries such as healthcare, finance, and tech.
Career Advancement: Skills gained lead to roles like AI Engineer, ML Researcher, or Computer Vision Specialist.
Salary Potential: Deep learning professionals earn between $100K–$160K per year.
Freelance Opportunities: High-paying project work in AI-powered applications and model development.
Specification: Practical Deep Learning with PyTorch
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