[Packtpub] Introduction to Deep Learning with Caffe2


[Packtpub] Introduction to Deep Learning with Caffe2

Create powerful applications for the real world using Caffe2

Video Description

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.

Style and Approach

An exhaustive course packed with step-by-step instructions, working examples, and actionable advice on understanding Caffe2 to build deep learning applications. This course is properly segmented so that you can learn at your own pace and focus on your area of interest.

What You Will Learn

  • 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
  • Understanding RNNs and LSTMs
  • Diving into the different layers of Caffe2
  • Experimenting with activation functions using caffe2
  • Understanding the importance of weight initialization and optimization in deep learning

Created By Abhishek Kumar Annamraju, Akash Deep Singh
Released Date August 31/2018
Duration 1h 56m

Size: 988.38 MB

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