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## Course materials
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| Number | Notebook | Data/Model | Exercises & Extra-curriculum | Slides |
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| ----- | ----- | ----- | ----- | ----- |
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| 00 | [TensorFlow Fundamentals](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/00_tensorflow_fundamentals.ipynb) | | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-00-tensorflow-fundamentals-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/00_introduction_to_tensorflow_and_deep_learning.pdf) |
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| 01 | [TensorFlow Regression](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/01_neural_network_regression_in_tensorflow.ipynb) | | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-01-neural-network-regression-with-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/01_neural_network_regression_with_tensorflow.pdf) |
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| 02 | [TensorFlow Classification](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/02_neural_network_classification_in_tensorflow.ipynb) | | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-02-neural-network-classification-with-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/02_neural_network_classification_with_tensorflow.pdf) |
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| 03 | [TensorFlow Computer Vision](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/03_convolutional_neural_networks_in_tensorflow.ipynb) | [`pizza_steak`](https://storage.googleapis.com/ztm_tf_course/food_vision/pizza_steak.zip), [`10_food_classes_all_data`](https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-03-computer-vision--convolutional-neural-networks-in-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/03_convolution_neural_networks_and_computer_vision_with_tensorflow.pdf) |
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| 04 | [Transfer Learning Part 1: Feature extraction](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/04_transfer_learning_in_tensorflow_part_1_feature_extraction.ipynb) | [`10_food_classes_10_percent`](https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-04-transfer-learning-in-tensorflow-part-1-feature-extraction-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/04_transfer_learning_with_tensorflow_part_1_feature_extraction.pdf) |
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| 05 | [Transfer Learning Part 2: Fine-tuning](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/05_transfer_learning_in_tensorflow_part_2_fine_tuning.ipynb) | [`10_food_classes_10_percent`](https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip), [`10_food_classes_1_percent`](https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_1_percent.zip), [`10_food_classes_all_data`](https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-05-transfer-learning-in-tensorflow-part-2-fine-tuning-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/05_transfer_learning_with_tensorflow_part_2_fine_tuning.pdf) |
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| 06 | [Transfer Learning Part 3: Scaling up](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/06_transfer_learning_in_tensorflow_part_3_scaling_up.ipynb) | [`101_food_classes_10_percent`](https://storage.googleapis.com/ztm_tf_course/food_vision/101_food_classes_10_percent.zip), [`custom_food_images`](https://storage.googleapis.com/ztm_tf_course/food_vision/custom_food_images.zip), [`fine_tuned_efficientnet_model`](https://storage.googleapis.com/ztm_tf_course/food_vision/06_101_food_class_10_percent_saved_big_dog_model.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-06-transfer-learning-in-tensorflow-part-3-scaling-up-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/06_transfer_learning_with_tensorflow_part_3_scaling_up.pdf) |
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| 07 | [Milestone Project 1: Food Vision 🍔👁](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/07_food_vision_milestone_project_1.ipynb), [Template (your challenge)](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/TEMPLATE_07_food_vision_milestone_project_1.ipynb) | [`feature_extraction_mixed_precision_efficientnet_model`](https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip), [`fine_tuned_mixed_precision_efficientnet_model`](https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-07-milestone-project-1--food-vision-big-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/07_milestone_project_1_food_vision.pdf) |
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| 08 | [TensorFlow NLP Fundamentals](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/08_introduction_to_nlp_in_tensorflow.ipynb) | [`diaster_or_no_diaster_tweets`](https://storage.googleapis.com/ztm_tf_course/nlp_getting_started.zip), [`USE_feature_extractor_model`](https://storage.googleapis.com/ztm_tf_course/08_model_6_USE_feature_extractor.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-08-introduction-to-nlp-natural-language-processing-in-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/08_natural_language_processing_in_tensorflow.pdf) |
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| 09 | [Milestone Project 2: SkimLit 📄🔥](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/09_SkimLit_nlp_milestone_project_2.ipynb) | [`pubmed_RCT_200k_dataset`](https://github.com/Franck-Dernoncourt/pubmed-rct.git), [`skimlit_tribrid_model`](https://storage.googleapis.com/ztm_tf_course/skimlit/skimlit_tribrid_model.zip) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-09-milestone-project-2-skimlit--exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/09_milestone_project_2_skimlit.pdf) |
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| 10 | [TensorFlow Time Series Fundamentals & Milestone Project 3: BitPredict 💰📈 (videos coming soon)](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/10_time_series_forecasting_in_tensorflow.ipynb) | [`bitcoin_price_data_USD_2013-10-01_2021-05-18.csv`](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv) | | |
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The following table represents contents of the book (each notebook is a chapter) with extra links to slides, exercises and extra-curriculum.
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| Number | Notebook | Exercises & Extra-curriculum | Slides |
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| ----- | ----- | ----- | ----- |
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| 00 | [TensorFlow Fundamentals](https://dev.mrdbourke.com/tensorflow-deep-learning/00_tensorflow_fundamentals/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-00-tensorflow-fundamentals-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/00_introduction_to_tensorflow_and_deep_learning.pdf) |
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| 01 | [TensorFlow Regression](https://dev.mrdbourke.com/tensorflow-deep-learning/01_neural_network_regression_in_tensorflow/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-01-neural-network-regression-with-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/01_neural_network_regression_with_tensorflow.pdf) |
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| 02 | [TensorFlow Classification](https://dev.mrdbourke.com/tensorflow-deep-learning/02_neural_network_classification_in_tensorflow/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-02-neural-network-classification-with-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/02_neural_network_classification_with_tensorflow.pdf) |
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| 03 | [TensorFlow Computer Vision](https://dev.mrdbourke.com/tensorflow-deep-learning/03_convolutional_neural_networks_in_tensorflow/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-03-computer-vision--convolutional-neural-networks-in-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/03_convolution_neural_networks_and_computer_vision_with_tensorflow.pdf) |
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| 04 | [Transfer Learning Part 1: Feature extraction](https://dev.mrdbourke.com/tensorflow-deep-learning/04_transfer_learning_in_tensorflow_part_1_feature_extraction/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-04-transfer-learning-in-tensorflow-part-1-feature-extraction-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/04_transfer_learning_with_tensorflow_part_1_feature_extraction.pdf) |
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| 05 | [Transfer Learning Part 2: Fine-tuning](https://dev.mrdbourke.com/tensorflow-deep-learning/05_transfer_learning_in_tensorflow_part_2_fine_tuning/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-05-transfer-learning-in-tensorflow-part-2-fine-tuning-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/05_transfer_learning_with_tensorflow_part_2_fine_tuning.pdf) |
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| 06 | [Transfer Learning Part 3: Scaling up](https://dev.mrdbourke.com/tensorflow-deep-learning/06_transfer_learning_in_tensorflow_part_3_scaling_up/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-06-transfer-learning-in-tensorflow-part-3-scaling-up-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/06_transfer_learning_with_tensorflow_part_3_scaling_up.pdf) |
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| 07 | [Milestone Project 1: Food Vision 🍔👁](https://dev.mrdbourke.com/tensorflow-deep-learning/07_food_vision_milestone_project_1/), [Template (your challenge)](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/TEMPLATE_07_food_vision_milestone_project_1.ipynb) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-07-milestone-project-1--food-vision-big-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/07_milestone_project_1_food_vision.pdf) |
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| 08 | [TensorFlow NLP Fundamentals](https://dev.mrdbourke.com/tensorflow-deep-learning/08_introduction_to_nlp_in_tensorflow/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-08-introduction-to-nlp-natural-language-processing-in-tensorflow-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/08_natural_language_processing_in_tensorflow.pdf) |
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| 09 | [Milestone Project 2: SkimLit 📄🔥](https://dev.mrdbourke.com/tensorflow-deep-learning/09_SkimLit_nlp_milestone_project_2/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning#-09-milestone-project-2-skimlit--exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/09_milestone_project_2_skimlit.pdf) |
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| 10 | [TensorFlow Time Series Fundamentals & Milestone Project 3: BitPredict 💰📈](https://dev.mrdbourke.com/tensorflow-deep-learning/10_time_series_forecasting_in_tensorflow/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/README.md#-10-time-series-fundamentals-and-milestone-project-3-bitpredict--exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/10_time_series_fundamentals_and_milestone_project_3_bitpredict.pdf) |
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| 11 | [Preparing to Pass the TensorFlow Developer Certification Exam](https://dev.mrdbourke.com/tensorflow-deep-learning/11_passing_the_tensorflow_developer_certification_exam/) | [Go to exercises & extra-curriculum](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/README.md#-11-passing-the-tensorflow-developer-certification-exercises) | [Go to slides](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/slides/11_passing_the_tensorflow_developer_certification_exam.pdf) |
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## Course structure
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* **Comfortable using Google Colab/Jupyter Notebooks.** This course uses Google Colab throughout. If you have never used Google Colab before, it works very similar to Jupyter Notebooks with a few extra features. If you’re not familiar with Google Colab notebooks, I’d suggest going through the [Introduction to Google Colab notebook](https://colab.research.google.com/notebooks/intro.ipynb).
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* **Plug:** The [Zero to Mastery beginner-friendly machine learning course](https://dbourke.link/ZTMMLcourse) (I also teach this) teaches all of the above (and this course, the one you're reading about now, is designed as a follow on).
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## How to use this book
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TODO (things coming to this page):
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All of the materials are taught code-first. The chapters are Jupyter Notebooks (also Google Colab notebooks) which can be run interactively.
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- [ ] course outline (make this like the headings on the side bar & link to appropriate resources)
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- [x] what this course teaches you
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- [ ] short video on how to use this page (e.g. show the link to Colab & how it works)
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- [x] links to YouTube videos of course
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- [ ] sign up button
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- [ ] extensions
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- [ ] what this course is missing
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You can read all of the materials but they'll be best learned if you practice writing the code yourself.
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![running a notebook chapter in Google Colab](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/misc-run-notebook-in-google-colab.png)
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*To start running a notebook interactively, click the "Open in Colab" button at the top of each chapter.*
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## Who made this book?
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I did, ah, me, Daniel, [Daniel Bourke](https://www.mrdbourke.com). I'm a machine learning engineer who makes YouTube videos and writes stories, pop philosophy and machine learning coding tutorials (like the ones contained in this book).
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Sometimes documentaiton and other resources can be a bit hard to read for certain things. So I've done my best to make this a book (and a video course, I mean, that's where this book came from) I'd like to have read when I was getting into the exciting world of deep learning.
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## Extensions
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Enjoyed this book/course?
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I'd also recommend the following:
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* [Neural Networks and Deep Learning Book](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen - If the Zero to Mastery TensorFlow for Deep Learning book is top down, this book is bottom up. A fantastic resource to sandwich your knowledge.
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* [Deeplearning.AI specializations](https://www.deeplearning.ai) - This course focuses on code-first, the deeplearning.ai specializations will teach you what's going on behind the code.
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* [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow Book](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) (especially the 2nd half) - Many of the materials in this course were inspired by and guided by the pages of this beautiful text book.
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* [Full Stack Deep Learning](https://fullstackdeeplearning.com) - Learn how to turn your models into machine learning-powered applications.
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* [Made with ML MLOps materials](https://madewithml.com/#mlops) - Similar to Full Stack Deep Learning but comprised into many small lessons around all the pieces of the puzzle (data collection, labelling, deployment and more) required to build a full-stack machine learning-powered application.
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* [fast.ai Curriculum](https://www.fast.ai) - One of the best (and free) AI/deep learning courses online. Enough said.
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* ["How does a beginner data scientist like me gain experience?"](https://www.mrdbourke.com/how-can-a-beginner-data-scientist-like-me-gain-experience/) by Daniel Bourke - Read this on how to get experience for a job after studying online/at unveristy (start the job before you have it).
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Get ready to dream in tensors!
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Onward.

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