Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...

After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on...

The author integrates introductory statistics for engineers and  introductory...

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from...

• Train your own models for effective prediction, using high-level Keras API
• Perform supervised and unsupervised machine learning and learn...