You're done!

1. You're done!

If you've finished the whole course you've probably put a lot of energy and work into understanding all of these new concepts, so

2. Congratulations!

congratulations! I hope you had fun! Now you have a better understanding of many key points when it comes to building a variety of neural networks and what you can use them for!

3. You've learned a lot

You've learned a lot! You completed 44 exercises and 14 lessons, you should be proud! You have a lot of power in your hands.

4. What you've learned

You've learned the basics of neural networks. Built sequential models and learned to solve regression, binary classification, multi-class and multi-label problems with neural networks. You've explored activation functions and carried out hyperparameter tuning, turning your keras models into sklearn estimators. You've used autoencoders and de-noised images with them. You've learned key concepts about CNN networks and used the pre-trained resnet50 model to classify images. You visualized convolutions for the MNIST dataset. You learned about LSTMs concepts and worked with text and embedding layers. You've used a lot of different datasets along the way also learning a lot of keras utility functions.

5. What could you learn next?

To keep on improving your understanding and learning on neural networks you should go deeper into CNNs, there's an specific course here on Datacamp. You can also go deeper into LSTMs, there's a lot more to learn. You could also check how to work with the Keras functional API which allows you to build more powerful models with shared layers and several branches. And finally, consider getting creative with Generative Adversarial Networks and taking on some Deeplearning projects of your own.

6. Goodbye!

If you liked this course consider spreading the word so other people can also experience your journey! And feel free to give me a follow on Twitter or LinkedIn if you'd like. And remember, your adventures with neural networks just begun, have a good one!

7. Have a good one!

¡Hasta la vista!

This exercise is part of the course

Introduction to Deep Learning with Keras

IntermediateSkill Level

4.8+

Start Course for Free

In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

Exercise 1: What is Keras?Exercise 2: Describing Keras Exercise 3: Would you use deep learning?Exercise 4: Your first neural network Exercise 5: Hello nets!Exercise 6: Counting parameters Exercise 7: Build as shown!Exercise 8: Surviving a meteor strike Exercise 9: Specifying a model Exercise 10: Training Exercise 11: Predicting the orbit!

By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

Exercise 1: Binary classification Exercise 2: Exploring dollar bills Exercise 3: A binary classification model Exercise 4: Is this dollar bill fake ?Exercise 5: Multi-class classification Exercise 6: A multi-class model Exercise 7: Prepare your dataset Exercise 8: Training on dart throwers Exercise 9: Softmax predictions Exercise 10: Multi-label classification Exercise 11: An irrigation machine Exercise 12: Training with multiple labels Exercise 13: Keras callbacks Exercise 14: The history callback Exercise 15: Early stopping your model Exercise 16: A combination of callbacks

In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

Exercise 1: Learning curves Exercise 2: Learning the digits Exercise 3: Is the model overfitting?Exercise 4: Do we need more data?Exercise 5: Activation functions Exercise 6: Different activation functions Exercise 7: Comparing activation functions Exercise 8: Comparing activation functions II Exercise 9: Batch size and batch normalization Exercise 10: Changing batch sizes Exercise 11: Batch normalizing a familiar model Exercise 12: Batch normalization effects Exercise 13: Hyperparameter tuning Exercise 14: Preparing a model for tuning Exercise 15: Tuning the model parameters Exercise 16: Training with cross-validation

It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

Exercise 1: Tensors, layers, and autoencoders Exercise 2: It's a flow of tensors Exercise 3: Neural separation Exercise 4: Building an autoencoder Exercise 5: De-noising like an autoencoder Exercise 6: Intro to CNNs Exercise 7: Building a CNN model Exercise 8: Looking at convolutions Exercise 9: Preparing your input image Exercise 10: Using a real world model Exercise 11: Intro to LSTMs Exercise 12: Text prediction with LSTMs Exercise 13: Build your LSTM model Exercise 14: Decode your predictions Exercise 15: Test your model!Exercise 16: You're done!

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