Beginning Deep Learning with Tensorflow - Long, Liangqu

Yo?ll start with an introduction to AI, where yo?ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, yo?ll jump into simple classification programs for hand-writing analysis. Once yo?ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, yo?ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs.

Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer!

Biographie:

Chapter 1: Introduction to Artificial Intelligence.- Chapter 2. Regression.- Chapter 3. Classification.- Chapter 4. Basic Tensorflow.- Chapter 5. Advanced Tensorflow.- Chapter 6. Neural Network.- Chapter 7. Backward Propagation Algorithm.- Chapter 8. Keras Advanced API.- Chapter 9. Overfitting.- Chapter 10. Convolutional Neural Networks.- Chapter 11. Recurrent Neural Network.- Chapter 12. Autoencoder.- Chapter 13. Generative Adversarial Network (GAN).- Chapter 14. Reinforcement Learning.- Chapter 15. Custom Dataset.

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Sommaire:

Chapter 1: Introduction to Artificial Intelligence.- Chapter 2. Regression.- Chapter 3. Classification.- Chapter 4. Basic Tensorflow.- Chapter 5. Advanced Tensorflow.- Chapter 6. Neural Network.- Chapter 7. Backward Propagation Algorithm.- Chapter 8. Keras Advanced API.- Chapter 9. Overfitting.- Chapter 10. Convolutional Neural Networks.- Chapter 11. Recurrent Neural Network.- Chapter 12. Autoencoder.- Chapter 13. Generative Adversarial Network (GAN).- Chapter 14. Reinforcement Learning.- Chapter 15. Custom Dataset.