DeepLearning.AI TensorFlow Developer Professional

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Course content

week1.A new programming paradigm
- 1.Introduction: A conversation with Andrew Ng.mp4
- 2.A primer in machine learning.mp4
- 3.The ‘Hello World’ of neural networks.mp4
- 4.Working through ‘Hello World’ in TensorFlow and Python.mp4
- Before you begin_ TensorFlow 2.0 and this course _ Coursera.pdf
- Exercise_1_House_Prices_Question.ipynb
- From rules to data _ Coursera.pdf
quiz
- Screenshot from 2021-08-20 17-59-24.png
- Screenshot from 2021-08-20 17-59-43.png
- Screenshot from 2021-08-20 17-59-51.png
- Try it for yourself _ Coursera.pdf
- Week 1 Resources _ Coursera.pdf
- week1.tar.gz
week2.Introduction to Computer Vision
- 1.A Conversation with Andrew Ng.mp4
- 2.An Introduction to computer vision.mp4
- 3.Writing code to load training data.mp4
- 4.Coding a Computer Vision Neural Network.mp4
- 5.Walk through a Notebook for computer vision.mp4
- 6.Using Callbacks to control training.mp4
- 7.Walk through a notebook with Callbacks.mp4
- Exercise2_Question.ipynb
- Exploring how to use data _ Coursera.pdf
- Get hands-on with computer vision _ Coursera.pdf
- notebook.tar.gz
quiz
- Screenshot from 2021-08-20 18-11-11.png
- Screenshot from 2021-08-20 18-11-20.png
- Screenshot from 2021-08-20 18-11-27.png
- See how it done _ Coursera.pdf
- See how to implement Callbacks _ Coursera.pdf
- The structure of Fashion MNIST data _ Coursera.pdf
- Week 2 Resources _ Coursera.pdf
week3.Enhancing Vision with Convolutional Neural Networks
- 1.A conversation with Andrew Ng.mp4
- 2.What are convolutions and pooling?.mp4
- 3.Implementing convolutional layers.mp4
- 4.Implementing pooling layers.mp4
- 5.Improving the Fashion classifier with convolutions.mp4
- 6.Walking through convolutions.mp4
- Coding convolutions and pooling layers _ Coursera.pdf
- Exercise_3_Question.ipynb
- Experiment with filters and pools _ Coursera.pdf
- Getting hands-on, your first ConvNet _ Coursera.pdf
- Learn more about convolutions _ Coursera.pdf
quiz
- Screenshot from 2021-08-20 18-20-58.png
- Screenshot from 2021-08-20 18-21-06.png
- Try it for yourself _ Coursera.pdf
- Week 3 Resources _ Coursera.pdf
- week3.tar.gz
week4.Using Real-world Images
- 1.A conversation with Andrew Ng.mp4
- 2.Understanding ImageGenerator.mp4
- 3.Defining a ConvNet to use complex images.mp4
- 4.Training the ConvNet with fit_generator.mp4
- 5.Walking through developing a ConvNet.mp4
- 6.Walking through training the ConvNet with fit_generator.mp4
- 7.Adding automatic validation to test accuracy.mp4
- 8.Exploring the impact of compressing images.mp4
- Designing the neural network _ Coursera.pdf
- Exercise_4_Question.ipynb
- Experiment with the horse or human classifier _ Coursera.pdf
- Explore an impactful, real-world solution _ Coursera.pdf
- Exploring the solution _ Coursera.pdf
- Get hands-on and use validation _ Coursera.pdf
- Get Hands-on with compacted images _ Coursera.pdf
quiz
- Screenshot from 2021-08-20 22-22-08.png
- Screenshot from 2021-08-20 22-22-16.png
- Screenshot from 2021-08-20 22-22-26.png
- Train the ConvNet with ImageGenerator _ Coursera.pdf
- Training the neural network _ Coursera.pdf
- Week 4 Resources _ Coursera.pdf
- week4.tar.gz
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Introduction, A conversation with Andrew Ng.mp4
week1.Exploring a Larger Dataset
- 1.A conversation with Andrew Ng.mp4
- 2.Training with the cats vs. dogs dataset.mp4
- 3.Working through the notebook.mp4
- 4.Fixing through cropping.mp4
- 5.Visualizing the effect of the convolutions.mp4
- 6.Looking at accuracy and loss.mp4
- 7.Week 1 Wrap up.mp4
- Exercise_5_Question.ipynb
- Looking at the notebook _ Coursera.pdf
quiz
- Screenshot from 2021-08-20 23-09-06.png
- Screenshot from 2021-08-20 23-09-13.png
- Screenshot from 2021-08-20 23-09-25.png
- The cats vs dogs dataset _ Coursera.pdf
- week1.tar.gz
- What have we seen so far_ _ Coursera.pdf
- What you'll see next _ Coursera.pdf
week2.Augmentation: A technique to avoid overfitting
- 1.A conversation with Andrew Ng.mp4
- 2.Introducing augmentation.mp4
- 3.Coding augmentation with ImageDataGenerator.mp4
- 4.Demonstrating overfitting in cats vs. dogs.mp4
- 5.Adding augmentation to cats vs. dogs.mp4
- 6.Exploring augmentation with horses vs. humans.mp4
- 7.Week 2 Wrap up.mp4
- Exercise_6_Question.ipynb
- Image Augmentation _ Coursera.pdf
- Looking at the notebook _ Coursera.pdf
quiz
- Screenshot from 2021-08-20 23-19-27.png
- Screenshot from 2021-08-20 23-19-33.png
- Screenshot from 2021-08-20 23-19-39.png
- Start Coding... _ Coursera.pdf
- The impact of augmentation on Cats vs. Dogs _ Coursera.pdf
- Try it for yourself! _ Coursera.pdf
- week2.tar.gz
- What have we seen so far_ _ Coursera.pdf
week3.Transfer Learning
- 1.A conversation with Andrew Ng.mp4
- 2.Understanding transfer learning: the concepts.mp4
- 3.Coding transfer learning from the inception mode.mp4
- 4.Coding your own model with transferred features.mp4
- 5.Exploring dropouts.mp4
- 6.Exploring Transfer Learning with Inception.mp4
- 7.Week 3 Wrap up.mp4
- Adding your DNN _ Coursera.pdf
- Applying Transfer Learning to Cats v Dogs _ Coursera.pdf
- Exercise_7_Question.ipynb
quiz
- Screenshot from 2021-08-20 23-34-13.png
- Screenshot from 2021-08-20 23-34-32.png
- Screenshot from 2021-08-20 23-34-39.png
- Start coding! _ Coursera.pdf
- Using dropouts! _ Coursera.pdf
- week3.tar.gz
- What have we seen so far_ _ Coursera.pdf
week4.Multiclass Classifications
- 1.A conversation with Andrew Ng.mp4
- 2.Moving from binary to multi-class classification.mp4
- 3.Explore multi-class with Rock Paper Scissors dataset.mp4
- 4.Train a classifier with Rock Paper Scissors.mp4
- 5.Test the Rock Paper Scissors classifier.mp4
- 6.A conversation with Andrew Ng.mp4
- Check out the code! _ Coursera.pdf
- Exercise_8_Question.ipynb
- Introducing the Rock-Paper-Scissors dataset _ Coursera.pdf
quiz
- Screenshot from 2021-08-20 23-41-05.png
- Screenshot from 2021-08-20 23-41-12.png
- Screenshot from 2021-08-20 23-41-17.png
- Try testing the classifier _ Coursera.pdf
- week4.tar.gz
- What have we seen so far_ _ Coursera.pdf
- Wrap up _ Coursera.pdf
week1.Sentiment in text
- 1.Introduction.mp4
- 2.Word based encodings.mp4
- 3.Using APIs.mp4
- 4.Notebook for lesson 1.mp4
- 5.Text to sequence.mp4
- 6.Looking more at the Tokenizer.mp4
- 7.Padding.mp4
- 8.Notebook for lesson 2.mp4
- 9.Sarcasm, really?.mp4
- 10.Working with the Tokenizer.mp4
- 11.Notebook for lesson 3.mp4
- 12.Week 1 Wrap up.mp4
- Check out the code! _ Coursera.pdf
- Check out the code! _ Coursera1.pdf
- Check out the code! _ Coursera2.pdf
- Course_3_Week_1_Exercise_answer.ipynb
- Course_3_Week_1_Exercise_question.ipynb
- News headlines dataset for sarcasm detection _ Coursera.pdf
quiz
- Screenshot from 2021-08-21 09-31-15.png
- Screenshot from 2021-08-21 09-31-21.png
- Screenshot from 2021-08-21 09-31-27.png
week2.Word Embeddings
- 1.A conversation with Andrew Ng.mp4
- 2.Introduction.mp4
- 3.The IMBD dataset.mp4
- 4.Looking into the details.mp4
- 5.How can we use vectors?.mp4
- 6.More into the details.mp4
- 7.Notebook for lesson 1.mp4
- 8.Remember the sarcasm dataset?.mp4
- 9.Building a classifier for the sarcasm dataset.mp4
- 10.Let’s talk about the loss function.mp4
- 11.Pre-tokenized datasets.mp4
- 12.Diving into the code (part 1).mp4
- 13.Diving into the code (part 2).mp4
- 14.Notebook for lesson 3.mp4
- Check out the code! _ Coursera.pdf
- Check out the code! _ Coursera1.pdf
- Check out the code! _ Coursera2.pdf
- Course_3_Week_2_Exercise_Answer.ipynb
- Course_3_Week_2_Exercise_Question.ipynb
- IMDB reviews dataset _ Coursera.pdf
quiz
- Screenshot from 2021-08-21 09-40-30.png
- Screenshot from 2021-08-21 09-40-37.png
- Screenshot from 2021-08-21 09-40-42.png
- Subwords text encoder _ Coursera.pdf
- TensorFlow datasets _ Coursera.pdf
- Week 2 Wrap up _ Coursera.pdf
week3.Sequence models
- 1.A conversation with Andrew Ng.mp4
- 2.Introduction.mp4
- 3.LSTMs.mp4
- 4.Implementing LSTMs in code.mp4
- 5.Accuracy and loss.mp4
- 6.A word from Laurence.mp4
- 7.Looking into the code.mp4
- 8.Using a convolutional network.mp4
- 9.Going back to the IMDB dataset.mp4
- 10.Tips from Laurence.mp4
- Check out the code! _ Coursera.pdf
- Check out the code! _ Coursera1.pdf
- Check out the code! _ Coursera2.pdf
- Exploring different sequence models _ Coursera.pdf
- Link to Andrew's sequence modeling course _ Coursera.pdf
- More info on LSTMs _ Coursera.pdf
- NLP_Course_Week_3_Exercise_Answer.ipynb
- NLP_Course_Week_3_Exercise_Question.ipynb
quiz
- Screenshot from 2021-08-21 09-46-54.png
- Screenshot from 2021-08-21 09-47-02.png
- Screenshot from 2021-08-21 09-47-08.png
- Week 3 Wrap up _ Coursera.pdf
week4.Sequence models and literature
- 1.A conversation with Andrew Ng.mp4
- 2.Introduction.mp4
- 3.Looking into the code.mp4
- 4.Training the data.mp4
- 5.More on training the data.mp4
- 6.Notebook for lesson 1.mp4
- 7.Finding what the next word should be.mp4
- 8.Example.mp4
- 9.Predicting a word.mp4
- 10.Poetry!.mp4
- 11.Looking into the code.mp4
- 12.Laurence the poet!.mp4
- 13.Your next task.mp4
- 14.A conversation with Andrew Ng.mp4
- Check out the code! _ Coursera.pdf
- Check out the code! _ Coursera1.pdf
- Link to generating text using a character-based RNN _ Coursera.pdf
- link to Laurence's poetry _ Coursera.pdf
- NLP_Week4_Exercise_Shakespeare_Answer.ipynb
- NLP_Week4_Exercise_Shakespeare_Question.ipynb
quiz
- Screenshot from 2021-08-21 10-03-14.png
- Screenshot from 2021-08-21 10-03-19.png
- Screenshot from 2021-08-21 10-03-24.png
- Wrap up _ Coursera.pdf
week1.Sequences and Prediction
- 1.Time series examples.mp4
- 2.Machine learning applied to time series.mp4
- 3.Common patterns in time series.mp4
- 4.Introduction to time series.mp4
- 5.Train, validation and test sets.mp4
- 6.Metrics for evaluating performance.mp4
- 7.Moving average and differencing.mp4
- 8.Trailing versus centered windows.mp4
- 9.Forecasting.mp4
- Forecasting notebook _ Coursera.pdf
- Introduction to time series notebook _ Coursera.pdf
quiz
- Screenshot from 2021-08-21 10-09-57.png
- Screenshot from 2021-08-21 10-10-06.png
- Screenshot from 2021-08-21 10-10-17.png
- Screenshot from 2021-08-21 10-10-24.png
- Week 1 Wrap up _ Coursera.pdf
- Week_1_Exercise_Answer.ipynb
- Week_1_Exercise_Question.ipynb
week2.Deep Neural Networks for Time Series
- 1.A conversation with Andrew Ng.mp4
- 2.Preparing features and labels.mp4
- 3.Preparing features and labels.mp4
- 4.Feeding windowed dataset into neural network.mp4
- 5.Single layer neural network.mp4
- 6.Machine learning on time windows.mp4
- 7.Prediction.mp4
- 8.More on single layer neural network.mp4
- 9.Deep neural network training, tuning and prediction.mp4
- 10.Deep neural network.mp4
- Deep neural network notebook _ Coursera.pdf
- Preparing features and labels notebook _ Coursera.pdf
quiz
- Screenshot from 2021-08-21 10-18-11.png
- Screenshot from 2021-08-21 10-18-34.png
- Screenshot from 2021-08-21 10-18-49.png
- Screenshot from 2021-08-21 10-18-55.png
- Screenshot from 2021-08-21 10-19-00.png
- Screenshot from 2021-08-21 10-19-05.png
- S+P_Week_2_Exercise_Answer.ipynb
- S+P_Week_2_Exercise_Question.ipynb
- Sequence bias _ Coursera.pdf
- Single layer neural network notebook _ Coursera.pdf
- Week 2 Wrap up _ Coursera.pdf
week3.Recurrent Neural Networks for Time Series
- 1.Week 3 - A conversation with Andrew Ng.mp4
- 2.Conceptual overview.mp4
- 3.Shape of the inputs to the RNN.mp4
- 4.Outputting a sequence.mp4
- 5.Lambda layers.mp4
- 6.Adjusting the learning rate dynamically.mp4
- 7.RNN.mp4
- 8.LSTM.mp4
- 9.Coding LSTMs.mp4
- 10.More on LSTM.mp4
- Link to the LSTM lesson _ Coursera.pdf
- LSTM notebook _ Coursera.pdf
- More info on Huber loss _ Coursera.pdf
quiz
- Screenshot from 2021-08-21 10-24-54.png
- Screenshot from 2021-08-21 10-25-02.png
- Screenshot from 2021-08-21 10-25-08.png
- RNN notebook _ Coursera.pdf
- S+P_Week_3_Exercise_Answer.ipynb
- S+P_Week_3_Exercise_Question.ipynb
- Week 3 Wrap up _ Coursera.pdf
week4.Real-world time series data
- (Optional) Opportunity to Mentor Other Learners _ Coursera.pdf
- 1.Week 4 - A conversation with Andrew Ng.mp4
- 2.Convolutions.mp4
- 3.Bi-directional LSTMs.mp4
- 4.LSTM.mp4
- 5.Real data - sunspots.mp4
- 6.Train and tune the model.mp4
- 7.Prediction.mp4
- 8.Sunspots.mp4
- 9.Combining our tools for analysis.mp4
- 10.Congratulations.mp4
- 11.Specialization wrap up - A conversation with Andrew Ng.mp4
- Convolutional neural networks course _ Coursera.pdf
- LSTM notebook _ Coursera.pdf
- More on batch sizing _ Coursera.pdf
quiz
- Screenshot from 2021-08-21 10-31-41.png
- Screenshot from 2021-08-21 10-32-01.png
- Screenshot from 2021-08-21 10-32-06.png
- Screenshot from 2021-08-21 10-32-12.png
- S+P_Week_4_Exercise_Answer.ipynb
- S+P_Week_4_Exercise_Question.ipynb
- Sunspots notebook _ Coursera.pdf
- What next_ _ Coursera.pdf
- Wrap up _ Coursera.pdf

Experiment with filters and pools _ Coursera.pdf

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DeepLearning.AI TensorFlow Developer Professional

Experiment with filters and pools _ Coursera.pdf

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