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Introduction to Deep Learning with Caffe2

Video Description

Create powerful applications for the real world using Caffe2

About This Video

  • Gain an in-depth understanding of deep learning and the components of Caffe2
  • Create deep learning applications using Caffe2 for various real-world use cases
  • Build a strong foundation of deep learning using Caffe2 with a comprehensive tutorial and a brief introduction to CUDA programming

In Detail

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.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Introduction-to-Deep-Learning-with-Caffe2

Table of Contents

  1. Chapter 1 : Setting Up Caffe2
    1. The Course Overview 00:01:32
    2. Set Up Caffe2 on Linux 00:06:21
    3. Understanding the Caffe2 Architecture 00:05:18
    4. Transitioning from Machine Learning to Deep Learning 00:02:21
    5. Running an Image Classifier Using Caffe2 00:03:23
  2. Chapter 2 : Implementing Neural Networks and Deep Learning
    1. Learn about Matrices Using Python – NumPy 00:03:49
    2. Understanding and Implementing Logistic Regression and Neural Networks 00:01:53
    3. Understanding and Implementing Deep Neural Networks 00:04:13
  3. Chapter 3 : Understanding Caffe2
    1. Caffe2 Introduction 00:05:27
    2. Caffe2 Python Wrapper 00:08:19
    3. Mathematical Operators in Caffe2 00:05:17
    4. Network Creators and Assisters in Caffe2 – Part 1 00:06:19
    5. Network Creators and Assisters in Caffe2 – Part 2 00:06:43
    6. Network Creators and Assisters in Caffe2 – Part 3 00:03:16
  4. Chapter 4 : Understanding a Convolutional Neural Network
    1. How Machines Learn to See! 00:02:02
    2. Introduction to Convolutional Neural Networks 00:03:35
    3. Implement a Convolution Layer Using Caffe2 00:01:06
    4. Pooling Layer and Dropout in Caffe2 00:03:22
    5. Role of Activation Functions in Solving Non-Linear Optimization 00:06:34
  5. Chapter 5 : Implementing Weight Initialization, Optimization, and Regularization
    1. Machine Learning Strategy 00:01:37
    2. How to Perform Data Selection, Preparation, and Processing 00:05:01
    3. Regularization of Neural Networks 00:03:02
    4. Optimizing Neural Networks 00:02:45
    5. Optimization Algorithms 00:04:19
  6. Chapter 6 : Introduction to Recurrent Neural Network
    1. Sequence Learning 00:01:56
    2. Introduction to Recurrent Neural Networks 00:02:29
    3. LSTMs – A Special Case of RNNs 00:05:02
    4. Learning about Word Embeddings 00:04:05
    5. Introduction to Augmented Recurrent Neural Networks 00:04:57