English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 38m | 332 MB
Build, train, and deploy your models using the speed and efficiency of Caffe and get future-ready in the world of Deep Learning
Caffe is a popular Deep Learning library implemented in C++ and renowned for its speed and efficiency. This video course is for you if you are familiar with C++ and want to get started with Deep Learning using Caffe to train real-world models.
This course will teach you how Deep Learning functions and how the Caffe framework enhances the speed and performance of your model to make it smarter for real-world uses. You will learn practical skills about creating layers, configuring networks, training and deploying using Caffe (written in C++). You will also learn about some of the internals of Caffe. Throughout the course, you will work with practical examples to get a good training in Deep Learning.
By the end of this course, you will have the skills to build and train your own real-world Deep Learning models using Caffe.
The course has been organized into layers. We start at the surface and keep digging deeper as we proceed. We adopt a practical approach, whereby you learn a concept and then put it into practice like “Peek under the Hood” for programmers who are familiar with C++. We also help you avoid common pitfalls most people encounter in Caffe
What You Will Learn
- Important Deep Learning concepts to get started with
- Introduction to Caffe and its functionalities
- Relating Caffe to Deep Learning concepts
- Elements such as blob, network for model representation in Caffe
- Training neural network with Caffe’s C++ API
- Training, tweaking, and deploying Caffe as per your requirements
- Avoiding common practical mistakes and pitfalls
Table of Contents
01 The Course Overview
02 Quick Introduction to Deep Learning
03 Caffe as a Deep Learning Framework
04 Installing Caffe and Avoiding Common Mistakes
05 How Caffe Is Organized
06 Compiling the Program
07 Running the Program and Inspecting the Results
08 Peek under the Hood
09 Layers and Dimensions
10 Deeper Look at Deep Learning
11 Deeper Look at Caffe
12 Protobuf, Networks, and Blobs in Caffe
13 What Does a Network Look Like
14 What Does a Model Look Like
15 Parameters
16 Application Code Walkthrough
17 Overview of the API for C++ Caffe
18 Editing Application Code, Build, and Run
19 Avoiding Common Mistakes
20 Preparing Data and Network
21 Training and Deploying Your Model to Caffe
22 Fine-Tuning Caffe for Your Model’s Performance
23 Miscellaneous Topics
24 Summing Up
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