- Suppose you construct a DNN and after training it, it yields only garbage. After inspection, you find that all of the weights and biases are either huge numbers or NaNs. What might the problem be?
- Name one possible problem with a small training_rate value.
- Name one possible problem with a large training_rate value.
- Suppose we want to train a DNN that will assign multiple labels to an image of an animal ("slimey", "furry", "red", "brown", and so on). Should we use a sigmoid or softmax layer at the end of the DNN?
- Suppose we want to classify an image of a single animal as either a cat or dog. Do we use sigmoid or softmax?
- If we decrease the batch size, will there be more or less updates to the weights and biases during gradient descent training?
Hands-On GPU Programming with Python and CUDA
By :
Hands-On GPU Programming with Python and CUDA
By:
Overview of this book
Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.
As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.
With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.
By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.
Related Content you might be interested in
Current Title:
Hands-On GPU Programming with Python and CUDA
Table of Contents (15 chapters)
Preface
Free Chapter
Why GPU Programming?
Setting Up Your GPU Programming Environment
Getting Started with PyCUDA
Kernels, Threads, Blocks, and Grids
Streams, Events, Contexts, and Concurrency
Debugging and Profiling Your CUDA Code
Using the CUDA Libraries with Scikit-CUDA
The CUDA Device Function Libraries and Thrust
Implementation of a Deep Neural Network
Working with Compiled GPU Code
Performance Optimization in CUDA
Where to Go from Here
Assessment
Other Books You May Enjoy
Customer Reviews