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