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# DeepLearning.AI TensorFlow Developer

Welcome to the public repo for this course.
Welcome to the public repository for the **DeepLearning.AI TensorFlow Developer** course. This repository contains all the assignments and ungraded labs, organized course-wise.

Below is the list of assignments and ungraded labs course-wise.
## 📬 Want to contribute?

We're not currently accepting Pull Requests, but if you have suggestions or spot any typos, feel free to raise an issue. If you encounter a blocking bug, consider joining our [community](https://community.deeplearning.ai/), where our mentors and team are ready to help. More information is available on our [Coursera](https://www.coursera.org/learn/introduction-tensorflow/ungradedLti/3L0GK/important-have-questions-issues-or-ideas-join-our-community) page.

### Want to contribute?

At the time we are not accepting Pull Requests but if you have any suggestion or spot any typo please raise an issue.

If you find a bug that is blocking in any way consider joining our [community](https://community.deeplearning.ai/) where our mentors and team will help you. You can also find more information on our community in this Reading Item within
[Coursera](https://www.coursera.org/learn/introduction-tensorflow/ungradedLti/3L0GK/important-have-questions-issues-or-ideas-join-our-community).

-----
---

## C1 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

## Week 1

### Assignment

- Housing Prices _(C1W1_Assignment.ipynb)_

### Ungraded Labs

1. Hello World Neural Network _(C1_W1_Lab_1_hello_world_nn.ipynb)_

## Week 2

### Assignment

- Handwriting Recognition _(C1W2_Assignment.ipynb)_

### Ungraded Labs

1. Beyond Hello World, A Computer Vision Example _(C1_W2_Lab_1_beyond_hello_world.ipynb)_
2. Callbacks _(C1_W2_Lab_2_callbacks.ipynb)_

## Week 3

### Assignment

- Improve MNIST with Convolutions _(C1W3_Assignment.ipynb)_

### Ungraded Labs

1. Improving Accuracy with Convolutions _(C1_W3_Lab_1_improving_accuracy_using_convolutions.ipynb)_
2. Exploring Convolutions _(C1_W3_Lab_2_exploring_convolutions.ipynb)_

## Week 4

### Assignment

- Handling Complex Images _(C1W4_Assignment.ipynb)_

### Ungraded Labs

1. Preprocessing Images to Train a Neural Network _(C1_W4_Lab_1_image_data_preprocessing_no_validation.ipynb)_
2. Image Data Preprocessing with a Validation Set _(C1_W4_Lab_2_image_data_preprocessing_with_validation.ipynb)_
3. Compacted Images _(C1_W4_Lab_3_compacted_images.ipynb)_
### Week 1
- **Assignment**
- 📝 Housing Prices `C1W1_Assignment.ipynb`
- **Ungraded Labs**
1. Hello World Neural Network `C1_W1_Lab_1_hello_world_nn.ipynb`

### Week 2
- **Assignment**
- 📝 Handwriting Recognition `C1W2_Assignment.ipynb`
- **Ungraded Labs**
1. Beyond Hello World, A Computer Vision Example `C1_W2_Lab_1_beyond_hello_world.ipynb`
2. Callbacks `C1_W2_Lab_2_callbacks.ipynb`

### Week 3
- **Assignment**
- 📝 Improve MNIST with Convolutions `C1W3_Assignment.ipynb`
- **Ungraded Labs**
1. Improving Accuracy with Convolutions `C1_W3_Lab_1_improving_accuracy_using_convolutions.ipynb`
2. Exploring Convolutions `C1_W3_Lab_2_exploring_convolutions.ipynb`

### Week 4
- **Assignment**
- 📝 Handling Complex Images `C1W4_Assignment.ipynb`
- **Ungraded Labs**
1. Preprocessing Images to Train a Neural Network `C1_W4_Lab_1_image_data_preprocessing_no_validation.ipynb`
2. Image Data Preprocessing with a Validation Set `C1_W4_Lab_2_image_data_preprocessing_with_validation.ipynb`
3. Compacted Images `C1_W4_Lab_3_compacted_images.ipynb`

---

## C2 - Convolutional Neural Networks in TensorFlow

## Week 1

### Assignment

- Cats vs. Dogs _(C2W1_Assignment.ipynb)_

### Ungraded Labs

1. Using more sophisticated images with Convolutional Neural Networks _(C2_W1_Lab_1_cats_vs_dogs.ipynb)_

## Week 2

### Assignment

- Cats vs. Dogs using Augmentation _(C2W2_Assignment.ipynb)_

### Ungraded Labs

1. Cats vs. Dogs with Augmentation _(C2_W2_Lab_1_cats_v_dogs_augmentation.ipynb)_
2. Horses vs. Humans with Augmentation _(C2_W2_Lab_2_horses_v_humans_augmentation.ipynb)_

## Week 3

### Assignment

- Horses vs. Humans using Transfer Learning _(C2W3_Assignment.ipynb)_

### Ungraded Labs

1. Exploring Transfer Learning _(C2_W3_Lab_1_transfer_learning.ipynb)_

## Week 4

### Assignment

- Multi-class Classifier _(C2W4_Assignment.ipynb)_

### Ungraded Labs

1. Classifying Rock, Paper, and Scissors _(C2_W4_Lab_1_multi_class_classifier.ipynb)_
### Week 1
- **Assignment**
- 📝 Cats vs. Dogs `C2W1_Assignment.ipynb`
- **Ungraded Labs**
1. Using more sophisticated images with Convolutional Neural Networks `C2_W1_Lab_1_cats_vs_dogs.ipynb`

### Week 2
- **Assignment**
- 📝 Cats vs. Dogs using Augmentation `C2W2_Assignment.ipynb`
- **Ungraded Labs**
1. Cats vs. Dogs with Augmentation `C2_W2_Lab_1_cats_v_dogs_augmentation.ipynb`
2. Horses vs. Humans with Augmentation `C2_W2_Lab_2_horses_v_humans_augmentation.ipynb`

### Week 3
- **Assignment**
- 📝 Horses vs. Humans using Transfer Learning `C2W3_Assignment.ipynb`
- **Ungraded Labs**
1. Exploring Transfer Learning `C2_W3_Lab_1_transfer_learning.ipynb`

### Week 4
- **Assignment**
- 📝 Multi-class Classifier `C2W4_Assignment.ipynb`
- **Ungraded Labs**
1. Classifying Rock, Paper, and Scissors `C2_W4_Lab_1_multi_class_classifier.ipynb`

---

## C3 - Natural Language Processing in TensorFlow

## Week 1

### Assignment

- Explore the BBC News Archive _(C3W1_Assignment.ipynb)_

### Ungraded Labs

1. Building a Vocabulary _(C3_W1_Lab_1_building_a_vocabulary.ipynb)_
2. Simple Sequences _(C3_W1_Lab_2_sequences_basic.ipynb)_
3. Sarcasm _(C3_W1_Lab_3_sarcasm.ipynb)_

## Week 2

### Assignment

- Categorizing the BBC News Archive _(C3W2_Assignment.ipynb)_

### Ungraded Labs

1. Positive or Negative IMDB Reviews _(C3_W2_Lab_1_imdb.ipynb)_
2. Sarcasm Classifier _(C3_W2_Lab_2_sarcasm_classifier.ipynb)_
3. IMDB Review Subwords _(C3_W2_Lab_3_imdb_subwords.ipynb)_

## Week 3

### Assignment

- Exploring Overfitting in NLP _(C3W3_Assignment.ipynb)_

### Ungraded Labs

1. IMDB Subwords with Single Layer LSTM _(C3_W3_Lab_1_single_layer_LSTM.ipynb)_
2. IMDB Subwords with Multi Layer LSTM _(C3_W3_Lab_2_multiple_layer_LSTM.ipynb)_
3. IMDB Subwords with 1D Convolutional Layer _(C3_W3_Lab_3_Conv1D.ipynb)_
4. IMDB Reviews with GRU (and optional LSTM and Conv1D) _(C3_W3_Lab_4_imdb_reviews_with_GRU_LSTM_Conv1D.ipynb)_
5. Sarcasm with Bidirectional LSTM _(C3_W3_Lab_5_sarcasm_with_bi_LSTM.ipynb)_
6. Sarcasm with 1D Convolutional Layer _(C3_W3_Lab_6_sarcasm_with_1D_convolutional.ipynb)_

## Week 4

### Assignment

- Writing Shakespeare with LSTMs _(C3W4_Assignment.ipynb)_

### Ungraded Labs

1. NLP with Irish Music _(C3_W4_Lab_1.ipynb)_
2. Generating Poetry from Irish Lyrics _(C3_W4_Lab_2_irish_lyrics.ipynb)_
### Week 1
- **Assignment**
- 📝 Explore the BBC News Archive `C3W1_Assignment.ipynb`
- **Ungraded Labs**
1. Building a Vocabulary `C3_W1_Lab_1_building_a_vocabulary.ipynb`
2. Simple Sequences `C3_W1_Lab_2_sequences_basic.ipynb`
3. Sarcasm `C3_W1_Lab_3_sarcasm.ipynb`

### Week 2
- **Assignment**
- 📝 Categorizing the BBC News Archive `C3W2_Assignment.ipynb`
- **Ungraded Labs**
1. Positive or Negative IMDB Reviews `C3_W2_Lab_1_imdb.ipynb`
2. Sarcasm Classifier `C3_W2_Lab_2_sarcasm_classifier.ipynb`
3. IMDB Review Subwords `C3_W2_Lab_3_imdb_subwords.ipynb`

### Week 3
- **Assignment**
- 📝 Exploring Overfitting in NLP `C3W3_Assignment.ipynb`
- **Ungraded Labs**
1. IMDB Subwords with Single Layer LSTM `C3_W3_Lab_1_single_layer_LSTM.ipynb`
2. IMDB Subwords with Multi Layer LSTM `C3_W3_Lab_2_multiple_layer_LSTM.ipynb`
3. IMDB Subwords with 1D Convolutional Layer `C3_W3_Lab_3_Conv1D.ipynb`
4. IMDB Reviews with GRU (and optional LSTM and Conv1D) `C3_W3_Lab_4_imdb_reviews_with_GRU_LSTM_Conv1D.ipynb`
5. Sarcasm with Bidirectional LSTM `C3_W3_Lab_5_sarcasm_with_bi_LSTM.ipynb`
6. Sarcasm with 1D Convolutional Layer `C3_W3_Lab_6_sarcasm_with_1D_convolutional.ipynb`

### Week 4
- **Assignment**
- 📝 Writing Shakespeare with LSTMs `C3W4_Assignment.ipynb`
- **Ungraded Labs**
1. NLP with Irish Music `C3_W4_Lab_1.ipynb`
2. Generating Poetry from Irish Lyrics `C3_W4_Lab_2_irish_lyrics.ipynb`

---

## C4 - Sequences, Time Series and Prediction

## Week 1

### Assignment

- Create and Predict Synthetic Data _(C4W1_Assignment.ipynb)_

### Ungraded Labs

1. Time Series _(C4_W1_Lab_1_time_series.ipynb)_
2. Forecasting _(C4_W1_Lab_2_forecasting.ipynb)_

## Week 2

### Assignment

- Predict with a DNN _(C4W2_Assignment.ipynb)_

### Ungraded Labs

1. Preparing Features and Labels _(C4_W2_Lab_1_features_and_labels.ipynb)_
2. Single Layer Neural Network _(C4_W2_Lab_2_single_layer_NN.ipynb)_
3. Deep Neural Network _(C4_W2_Lab_3_deep_NN.ipynb)_

## Week 3

### Assignment

- Using RNN's and LSTM's for time series _(C4W3_Assignment.ipynb)_

### Ungraded Labs

1. Recurrent Neural Network (RNN) _(C4_W3_Lab_1_RNN.ipynb)_
2. Long Short-Term Memory (LSTM) _(C4_W3_Lab_2_LSTM.ipynb)_

## Week 4

### Assignment

- Daily Minimum Temperatures in Melbourne - Real Life Data _(C4W4_Assignment.ipynb)_

### Ungraded Labs

1. Long Short-Term Memory (LSTM) _(C4_W4_Lab_1_LSTM.ipynb)_
2. Sunspots _(C4_W4_Lab_2_Sunspots.ipynb)_
3. Sunspots - DNN Only _(C4_W4_Lab_3_DNN_only.ipynb)_
### Week 1
- **Assignment**
- 📝 Create and Predict Synthetic Data `C4W1_Assignment.ipynb`
- **Ungraded Labs**
1. Time Series `C4_W1_Lab_1_time_series.ipynb`
2. Forecasting `C4_W1_Lab_2_forecasting.ipynb`

### Week 2
- **Assignment**
- 📝 Predict with a DNN `C4W2_Assignment.ipynb`
- **Ungraded Labs**
1. Preparing Features and Labels `C4_W2_Lab_1_features_and_labels.ipynb`
2. Single Layer Neural Network `C4_W2_Lab_2_single_layer_NN.ipynb`
3. Deep Neural Network `C4_W2_Lab_3_deep_NN.ipynb`

### Week 3
- **Assignment**
- 📝 Using RNN's and LSTM's for time series `C4W3_Assignment.ipynb`
- **Ungraded Labs**
1. Recurrent Neural Network (RNN) `C4_W3_Lab_1_RNN.ipynb`
2. Long Short-Term Memory (LSTM) `C4_W3_Lab_2_LSTM.ipynb`

### Week 4
- **Assignment**
- 📝 Daily Minimum Temperatures in Melbourne - Real Life Data `C4W4_Assignment.ipynb`
- **Ungraded Labs**
1. Long Short-Term Memory (LSTM) `C4_W4_Lab_1_LSTM.ipynb`
2. Sunspots `C4_W4_Lab_2_Sunspots.ipynb`
3. Sunspots - DNN Only `C4_W4_Lab_3_DNN_only.ipynb`

---

Happy coding! 🚀