This is the code repository for Introduction to Deep Learning with Caffe2 [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Deep learning is one of the most highly sought-after skills in the technology sector. If you want to take a crack at AI, then this course will help you do so. One of the many reasons for choosing Caffe2 for this course is its processing speed as compared to other platforms. Since the basis of the architecture in Caffe2 is CUDA, it provides flexibility in optimizing the code as per the hardware being used.
You’ll learn the foundations of Deep Learning, understand how to build neural networks and develop an understanding of convolutional networks, RNNs, Adam, Dropout, BatchNorm and more. You’ll be working on various projects throughout this MOOC with a focus on how to train and manipulate a deep neural network effectively. You’ll practice all these ideas in Caffe2 using Python programming languages.
By the end of the course, you’ll gain an understanding of every element of Caffe2 and be able to use the library in the most efficient way.
- Caffe2 architecture and how to use the platform efficiently
- Setting up Caffe2 on your system
- Working with a Simple Neural Network application
- Implementing Back-Propagation and Gradient Descent
- Exploring different layers of CNN and the problem of Image Classification
- Using RNN for text prediction and implement Autoencoders
- Diving into the different layers of Caffe2
- Exploring and Applying CUDA programming to Deep Learning
To fully benefit from the coverage included in this course, you will need:
If you’re a data scientist who wants to learn how to effectively apply deep learning using Caffe2 to build real-world applications, this is the course you need. This course is perfect for industry and technology experts, who have to develop production-grade services and modules and bring automation to real-world scenarios. Familiarity with the Caffe2 library is not required.