Applied Deep Learning with PyTorch

What you'll be able to do

• Basics of Pytorch.
• How to build helper functions for regression & classification?
• Build 1-layer logistic regression models in Pytorch.
• Build multi layer models in Pytorch.
• What is regularisation in deep learning?
• Various optimization algorithms in deep learning.
• How to build end to end DL solutions?

1. 01Introduction to PyTorch7 lessons · 18 min

   1. TensorsPreview2:53
   2. Download Resources1:00
   3. Dataset Class and DataloaderPreview4:06
   4. AutogradPreview2:29
   5. OptimizerPreview1:41
   6. Model ClassPreview4:03
   7. Saving ModelsPreview2:13
2. 02Setup and Helper Functions for PyTorch (Code Demo)5 lessons · 4 min

   1. Setup Environment0:39
   2. Data for Clasification and Regression0:45
   3. Helper Class for DataLoader1:14
   4. Helper Functions for Classification and Regression0:15
   5. Course Review1:00
3. 03Logistic Regression in PyTorch (Code Demo)6 lessons · 10 min

   1. Initialize Dataloader1:32
   2. Define Model architecture and Initialize1:47
   3. Loss and Optimizer0:41
   4. Model Training5:09
   5. Model Evaluation0:22
   6. Save Model0:37
4. 042-Layer Neural Network for Classification in PyTorch (Code Demo)3 lessons · 4 min

   1. Initialize Dataloader0:19
   2. Define Model architecture and initialize2:40
   3. Loss and Optimizer and model training1:12
5. 055-Layer Neural Network for Regression (Week 1 Assignment) in PyTorch (Code Demo)5 lessons · 7 min

   1. Initialize Dataloader1:09
   2. Define Model architecture and initialize2:38
   3. Loss and Optimizer0:11
   4. Model Training2:02
   5. Model Evaluation and save the model0:42
6. 06Regularization6 lessons · 20 min

   1. Introduction to Regularization2:47
   2. L1 and L2 Regularization5:37
   3. Why Regularization Works1:21
   4. Dropout Regularization4:10
   5. Inverted Dropout2:46
   6. Early Stopping3:37
7. 07Regularization in PyTorch (Code Demo)5 lessons · 20 min

   1. Regularization - Dropout2:54
   2. L1 Regularization2:35
   3. L2 Regularization1:13
   4. ElasticNet Regularization2:55
   5. Regularization - Early Stopping10:06
8. 08Optimization6 lessons · 8 min

   1. Stochastic Gradient Descent1:04
   2. Stochastic Gradient Descent With Momentum1:56
   3. Adaptive Gradient Algorithm (AdaGrad)2:12
   4. Root Mean Squared Propagation (RMSProp)0:55
   5. Adaptive Moment Estimation (Adam)0:42
   6. How to Choose - Checklist0:54
9. 09Optimization in PyTorch (Code Demo)1 lesson · 2 min

   1. Code Implemendation of Optimization1:41

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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