Foundation of Deep Learning - Part 2

What you'll be able to do

• How to Scale the Data?
• Define the loss function for regression/classification.
• How to setup environment in tensor flow?
• Build a 1-layer neural network for classification?
• How to perform forward backward propogation?
• Compute feature importance of variables in NN.
• How to build N-layer neural networks?

1. 01Preliminaries4 lessons · 12 min

   1. Scaling the DataPreview4:14
   2. Loss Functions of RegressionPreview1:24
   3. Loss Functions of ClassificationPreview3:33
   4. Overall Flow of Model TrainingPreview3:09
2. 02Setup Environment and Import Data (Code Demo)4 lessons · 6 min

   1. Import DataPreview1:44
   2. Conduct Train-Test SplitPreview0:51
   3. Reshape Data for Neural Network2:12
   4. Course Review1:00
3. 03Logistic Regression as a 1-Layer Neural Network (Code Demo)8 lessons · 20 min

   1. Introduction3:01
   2. Step 1 Initializing Parameters0:36
   3. Steps 2.1 & 2.2: Forward and Backward Propagation4:12
   4. Step 2.3 Update Parameters0:31
   5. Step 3: Combine into optimize() function3:52
   6. Step 4 Computing Predictions3:54
   7. Apply to Dataset1:50
   8. Analyze Effects of Learning Rates for a Neural Network2:16
4. 042-Layer Neural Network (Code Demo)9 lessons · 13 min

   1. Introduction3:29
   2. Step 1 Initializing Parameters0:57
   3. Step 2.1 Forward Propagation2:41
   4. Step 2.2 Backward Propagation2:29
   5. Step 2.3 Update Parameters0:07
   6. Step 3: Combine into optimize() function0:50
   7. Step 4 Computing Predictions0:47
   8. Apply to Dataset0:38
   9. Analyze Effects of Learning Rates for a Neural Network1:29
5. 05L-Layer Neural Network (Code Demo)6 lessons · 14 min

   1. Introduction1:49
   2. Step 1 Initializing Parameters2:47
   3. Step 2.1 Forward Propagation2:34
   4. Step 2.2: Backward Propagation and rest of the steps2:51
   5. Apply to Dataset0:51
   6. Analyze Effects of Learning Rates for a Neural Network2:44

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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