| 1. | |
Introduction to github, Markdown, Jupyter notebook | Introduction to colab and github |  |
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Introduction to github, Markdown, Jupyter notebook | Intro to Markdown | |
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Ch1 What is deep learning | Key factors behind deep learning’s rising popularity and future potential | |
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| 2. | |
Ch2 The mathematical building blocks of neural networks (1) | A first example of a neural network | |
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Ch2 The mathematical building blocks of neural networks (2) | Tensors and tensor operations | |
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| 3. | |
Ch2 The mathematical building blocks of neural networks (3) | How neural networks learn via backpropagation and gradient descent | |
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Ch3 Introduction to Keras and TensorFlow | A closer look at TensorFlow, Keras, and their relationship | |
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| 4. | |
Ch4 Getting started with neural networks: Classification and regression | first examples of real-world machine learning workflows - classification and regression examples | |
| 5. | |
Ch5 Fundamentals of machine learning | Understanding the tension between generalization and optimization, the fundamental issue in machine learning | |
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Ch6 The universal workflowof machine learning | Steps fframing a machine learning problem, Steps fdeveloping a working model | |
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| 6. | |
Ch7 Working with Keras: A deep dive | Creating Keras models, Using built-in Keras training and evaluation loops, Using Keras callbacks | |
| 7. | |
Ch8 Introduction to deep learning for computer vision | Understanding convolutional neural networks, Using data augmentation, Using a pretrained convnet, Fine-tuning a pretrained convnet | |
| 8. | |
Ch9 Advanced deep learning for computer vision (1) | The different branches of computer vision, Modern convnet architecture patterns | |
| 9. | |
Ch9 Advanced deep learning for computer vision (2) | Techniques for visualizing and interpreting what convnets learn | |
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Ch10 Deep learning for timeseries | Examples of machine learning tasks that involve timeseries data, Understanding recurrent neural networks, Advanced RNN | |
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| 10. | |
Ch11 Deep learning for text (1) | Preprocessing text data for machine learning applications, Bag-of-words approaches and sequence-modeling approaches for text processing | |
| 11. | |
Ch11 Deep learning for text (2) | The Transformer architecture, Sequence-to-sequence learning | |
| 12. | |
Ch12 Generative deep learning | text generation, deep dream | |
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