Practical Machine Learning with Tensorflow

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