Course curriculum
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1
Introduction to Deep Learning
- Exploring Deep Learning
- Deep Learning Epochs
- Neuron Know-How
- Lets get Networking
- Following the “Framework”
- Scalars and Vectors
- Building a neuron with PyTorch
- Quiz
- Course Handouts
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2
Understanding the working of Neural Networks
- How are Neural Networks trained - Forward Propagation
- Understanding Loss Functions + Hands on
- Reading: Creating a Custom Loss Function (Optional)
- Optimization Techniques - Gradient Descent
- What is Back Propagation?
- Types of Gradient Descent
- Common Optimization Techniques - Part 1
- Common Optimization Techniques - Part 2
- Building a Deep Neural Network (Hands-on Regression Model)
- Building a Deep Neural Network (Hands-on Classification Model)
- Quiz
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3
Improving Deep Neural Networks
- Problems in Deep Neural Networks
- The Solution to Vanishing and Exploding Gradients - Part 1
- Reading: Softmax Activation Function
- The Solution to Vanishing and Exploding Gradients - Part 2
- Hands-On: Vanishing and Exploding Gradients
- How to fix an Overfitting Model
- Solving Overfitting for Zeta Analytics
- Hands-On: Fine-Tuning Model Hyperparameters
- Hands-On: Model Saving using PyTorch
- Quiz
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4
Guided Project
- Project: Multi-classification Problem for HealthHabit
Introduction to Deep learning using PyTorch
This course will help you build a deep understanding of what Deep Learning is. After this course, you will be able to build advanced Deep Learning Models using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your all-in-one guide to becoming a deep learning expert. It is highly recommended that before taking up this course, you complete the advanced Machine Learning course.
Who Should Enroll:
- Professionals: Individuals looking to expand their skill set and apply deep learning across different industries.
- Aspiring Students: For those setting out on their journey to mastering deep learning and making a mark in the tech world.
Key Takeaways from the course
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Learn Pytorch, and Deep Learning techniques and build real-world Deep Learning Models.
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Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.