1 Lecture 1: Overview of Tensorflow
2 Lecture 2: Machine Learning Refresher
3 Lecture 3: Steps in Machine Learning Process
4 Lecture 4: Loss Functions in Machine Learning
5 Lecture 5: Gradient Descent
6 Lecture 6: Gradient Descent Variations
7 Lecture 7: Model Selection and Evaluation
8 Lecture 8: Machine Learning Visualization
9 Lecture 9: Deep Learning Refresher
10 Lecture 10: Introduction to Tensors
11 Lecture 11: Mathematical Foundations of Deep Learning - Contd.
12 Lecture 12A: Building Data Pipelines for Tensorflow - Part 1
13 Lecture 12B: Building Data Pipelines for Tensorflow - Part 2
14 Lecture 12C: Building Data Pipelines for Tensorflow - Part 3
15 Lecture 13: Text Processing with Tensorflow
16 Lecture 14: Classify Images
17 Lecture 15: Regression
18 Lecture 16: Classify Structured Data
19 Lecture 17: Text Classification
20 Lecture 18: Underfitting and Overfitting
21 Lecture 19: Save and Restore Models
22 Lecture 20: CNNs-Part 1
23 Lecture 21: CNNs-Part 2
24 Lecture 22: Transfer learning with pretrained CNNs
25 Lecture 23: Transfer learning with TF hub
26 Lecture 24: Image classification and Visualization
27 Lecture 25: Estimator API
28 Lecture 26: Logistic Regression
29 Lecture 27: Boosted Trees
30 Lecture 28: Introduction to word embeddings
31 Lecture 29: Recurrent Neural Networks Part 1
32 Lecture 30: Recurrent Neural Networks Part 2
33 Lecture 31: Time Series Forecasting with RNNs
34 Lecture 32: Text Generation with RNNs