English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 56m | 0.97 GB
Create powerful applications for the real world using Caffe2
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.
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
Table of Contents
Setting Up Caffe2
1 The Course Overview
2 Set Up Caffe2 on Linux
3 Understanding the Caffe2 Architecture
4 Transitioning from Machine Learning to Deep Learning
5 Running an Image Classifier Using Caffe2
Implementing Neural Networks and Deep Learning
6 Learn about Matrices Using Python – NumPy
7 Understanding and Implementing Logistic Regression and Neural Networks
8 Understanding and Implementing Deep Neural Networks
Understanding Caffe2
9 Caffe2 Introduction
10 Caffe2 Python Wrapper
11 Mathematical Operators in Caffe2
12 Network Creators and Assisters in Caffe2 – Part 1
13 Network Creators and Assisters in Caffe2 – Part 2
14 Network Creators and Assisters in Caffe2 – Part 3
Understanding a Convolutional Neural Network
15 How Machines Learn to See!
16 Introduction to Convolutional Neural Networks
17 Implement a Convolution Layer Using Caffe2
18 Pooling Layer and Dropout in Caffe2
19 Role of Activation Functions in Solving Non-Linear Optimization
Implementing Weight Initialization, Optimization, and Regularization
20 Machine Learning Strategy
21 How to Perform Data Selection, Preparation, and Processing
22 Regularization of Neural Networks
23 Optimizing Neural Networks
24 Optimization Algorithms
Introduction to Recurrent Neural Network
25 Sequence Learning
26 Introduction to Recurrent Neural Networks
27 LSTMs – A Special Case of RNNs
28 Learning about Word Embeddings
29 Introduction to Augmented Recurrent Neural Networks
Resolve the captcha to access the links!