English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 59m | 415 MB
Practical use cases will teach you to code once and run your Deep Learning models anywhere
Caffe2, open-sourced by Facebook, is a simple, flexible framework for efficient deep learning. This course will teach you about Caffe2 and show you how to train your deep learning models.
The course starts off with the basics of Caffe2 such as blobs, workspaces, operators, and nets; moving on, you will learn how to build a model using Caffe2’s new API brew. You will also learn how to create Convolutional Neural Networks (CNNs) that can identify not only handwriting but also fashion items from an image. You will work on transferring learning to allow you to work with CNN’s for image recognition by fine-tuning models that are already pre-trained on a large-scale dataset. We cover common models such as ResNet-50. Finally, the course will show you how to deploy your models on any platform.
By the end of this course, you will be able to effectively train Deep Learning models with Caffe2, providing you with high-performance and first-class support for large-scale distributed training, mobile deployment, new hardware support, and flexibility.
All classes in this course are hands-on; you will get sufficient background about each class’s content, and you will then go through critical examples that you need to know. At the end of each to, you will also be presented with quizzes to help you master Caffe2.
What You Will Learn
- Install Caffe2 and prepare your developing environment.
- The basic elements of Caffe2—such as blobs, workspaces, and tensors—and how to use them to build a computational graph.
- Foundational knowledge about training models using Caffe2.
- The brew, an API for creating models in Caffe2.
- Address the supervised learning problem of image classification using Caffe2.
- How to use RNNs in Caffe2 to learn to write poems like Shakespeare.
- Deep Q Network, and how to use it in Caffe2.
- Running models on devices with Caffe2.
Table of Contents
Getting Started with Caffe2
1 The Course Overview
2 Why Deep Learning
3 Machine Learning Categories
4 Why Caffe2
5 Install and Set Up Caffe2
6 Build a Caffe2 Docker
Basic Elements
7 Definition of a Computational Graph Through Examples
8 Introduce Workspace, Operators, and Nets
9 Working with Computational Graphs
Building Blocks of a Training Model
10 Housing Price Prediction
11 Representing a Linear Regression Model in a Computational Graph
12 Training Procedure
13 Training a Linear Regression Model
Supervised Learning and Transfer Learning
14 Fashion Product Recognition Problem
15 What Is Supervised Learning
16 What Is Transfer Learning
17 Model Zoo in Caffe2
18 Fine-Tune a Model for Recognizing Fashion Products
Sequence-to-Sequence Learning
19 Chatbot Customer Service
20 What Is Sequence-to-Sequence Learning
21 What Are RNNs and LSTMs
22 Training an RNN-Based Model to Write like Shakespeare
Reinforcement Learning
23 Why Deep Reinforcement Learning
24 What Is Deep Reinforcement Learning
25 What Is Deep Q-Network
26 Training a Deep Q- Network for Solving the Cart-Pole Problem
Running AI in Your Hands
27 AI on Mobile Devices Using Face ID
28 Challenges in Running AI Models on Mobile Devices
29 SequeezeNet
30 Deploy SequeezeNet on a Mobile Device
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