Welcome to Practical Machine Learning with TensorFlow 2.0 MOOC. As the name suggests we will mainly focus on practical aspects of ML that involves writing code in Python with TensorFlow 2.0 API. In every session, we will review the concept from theory point of view and then jump straight into implementation. We will be using Google Colab as a platform for coding these models. We will mainly cover material from the following page: https://www.tensorflow.org/beta
I would strongly advise students to run the code and experience how the code works. Once you get the basic idea of the concept and its implementation, you can spend some time looking at the details of each function from TF RC 2.0 API.
We will learn how to use tf.Keras and tf.Estimator APIs for building models. We will also learn to use tf.Dataset API for building input pipelines for bringing data to ML models. Later in the course, we will learn how to build customized ML models and train them in distributed fashion.
Wish you a great journey of learning TensorFlow with us!
| Lecture | Title | Handout/Colabs |
|---|---|---|
| 1 | Introduction to TensorFlow | |
| Machine Learning - Overview [Handout] | ||
| 2 | Machine Learning Refresher | |
| 3 | Steps in Machine Learning Process | |
| 4 | Loss Functions in Machine Learning | |
| 5 | Gradient Descent | |
| 6 | Gradient Descent Variations | |
| 7 | Model Selection and Evaluation | |
| Neural Network Playground | ||
| 8 | Machine Learning Visualization | |
| Deep Learning Review | ||
| 9 | Deep Learning Refresher | |
| 10 | Introduction to Tensor | Colab |
| 11 | Mathematical Foundations of Deep Learning - Cntd | Colab |
| Data Handling in TensorFlow 2.0 | ||
| 12 (A/B/C) | Building Data Pipelines for Tensorflow | |
| 13 | Text Processing with Tensorflow | |
| Building basic models with TF | ||
| 14 | Classify Images | Colab |
| 15 | Regression | Colab |
| 16 | Classify Structured Data | Colab |
| 17 | Text Classification | Colab |
| 18 | Underfitting and Overfitting | Colab |
| 19 | Save and Restore Models | Colab |
| Image models with TF | ||
| 20 | CNNs-Part 1 | Colab |
| 21 | CNNs-Part 2 | Colab |
| 22 | Transfer learning with pretrained CNNs | Colab |
| 23 | Transfer learning with TF hub | Colab |
| 24 | Image classification and visualization | Colab |
| 24 | Image classification and visualization | Colab |
| TF Estimator APIs | ||
| 25 | Estimator API | Pre-made Estimator Colab |
| 26 | Logistic Regression | Logistic Regression Colab |
| 27 | Boosted Trees | Boosted Trees Colab |
| Sequence Models with TF | ||
| 28 | Introduction to Word Embedding | Word Embedding Colab |
| 29 | Recurrent Neural Networks (Part 1) | Colab |
| 30 | Recurrent Neural Networks (Part 2) | Text Classification Colab |
| 31 | Time Series Forecasting with RNNs | Colab |
| 32 | Text Generation with RNNs | Colab |