NOC: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 (Continued...)


Lecture 12 - Building Data Pipelines for Tensorflow - Part 1


Lecture 13 - Building Data Pipelines for Tensorflow - Part 2


Lecture 14 - Building Data Pipelines for Tensorflow - Part 3


Lecture 15 - Text Processing with Tensorflow


Lecture 16 - Classify Images


Lecture 17 - Regression


Lecture 18 - Classify Structured Data


Lecture 19 - Text Classification


Lecture 20 - Underfitting and Overfitting


Lecture 21 - Save and Restore Models


Lecture 22 - CNNs - Part 1


Lecture 23 - CNNs - Part 2


Lecture 24 - Transfer learning with pretrained CNNs


Lecture 25 - Transfer learning with TF hub


Lecture 26 - Image classification and visualization


Lecture 27 - Estimator API


Lecture 28 - Logistic Regression


Lecture 29 - Boosted Trees


Lecture 30 - Introduction to word embeddings


Lecture 31 - Recurrent Neural Networks - Part 1


Lecture 32 - Recurrent Neural Networks - Part 2


Lecture 33 - Time Series Forecasting with RNNs


Lecture 34 - Text Generation with RNNs


Lecture 35 - TensorFlow Customization


Lecture 36 - Customizing tf.keras - Part 1


Lecture 37 - Customizing tf.keras - Part 2


Lecture 38 - TensorFlow Distributed Training