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.
* Hard copy material is not applicable for this course.
Why should one take this certification?
In this online course, you will 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 online course with a focus on how to train and manipulate a deep neural network effectively. All these will be practiced in Caffe2 using Python programming languages.
Who will benefit from taking this certification?
Job seekers looking for employment in various IT companies, PSUs or MNCs. Certification in Deep learning with Caffe framework benefits Data Science professionals, students and professionals across various Industries.
Companies that hire Vskills Deep Learning with Caffe2 Professionals
IT companies, MNCs, Consultancies hire Pytorch professionals for Data Science related opportunities. Companies employing Data Science include Capgemini, JP Morgan Chase, TCS, Wipro, Zensar, Accenture, Infor etc.
Deep Learning with Caffe2 Table of Contents
https://www.vskills.in/certification/deep-learning-with-caffe2-table-of-content
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TABLE OF CONTENTS
Module 1
SETTING UP CAFFE2
- The Course Overview
- Set Up Caffe2 on Linux
- Understanding the Caffe2 Architecture
- Transitioning from Machine Learning to Deep Learning
- Running an Image Classifier Using Caffe2
IMPLEMENTING NEURAL NETWORKS AND DEEP LEARNING
- Learn about Matrices Using Python – NumPy
- Understanding and Implementing Logistic Regression and Neural Networks
- Understanding and Implementing Deep Neural Networks
UNDERSTANDING CAFFE2
- Caffe2 Introduction
- Caffe2 Python Wrapper
- Mathematical Operators in Caffe2
- Network Creators and Assisters in Caffe2 – Part 1
- Network Creators and Assisters in Caffe2 – Part 2
- Network Creators and Assisters in Caffe2 – Part 3
UNDERSTANDING A CONVOLUTIONAL NEURAL NETWORK
- How Machines Learn to See!
- Introduction to Convolutional Neural Networks
- Implement a Convolution Layer Using Caffe2
- Pooling Layer and Dropout in Caffe2
- Role of Activation Functions in Solving Non-Linear Optimization
IMPLEMENTING WEIGHT INITIALIZATION, OPTIMIZATION, AND REGULARIZATION
- Machine Learning Strategy
- How to Perform Data Selection, Preparation, and Processing
- Regularization of Neural Networks
- Optimizing Neural Networks
- Optimization Algorithms
INTRODUCTION TO RECURRENT NEURAL NETWORK
- Sequence Learning
- Introduction to Recurrent Neural Networks
- LSTMs – A Special Case of RNNs
- Learning about Word Embeddings
- Introduction to Augmented Recurrent Neural Networks
Module 2
GETTING STARTED WITH CAFFE2
- The Course Overview
- Why Deep Learning?
- Machine Learning Categories
- Why Caffe2?
- Install and Set Up Caffe2
- Build a Caffe2 Docker
BASIC ELEMENTS
- Definition of a Computational Graph Through Examples
- Introduce Workspace, Operators, and Nets
- Working with Computational Graphs
BUILDING BLOCKS OF A TRAINING MODEL
- Housing Price Prediction
- Representing a Linear Regression Model in a Computational Graph
- Training Procedure
- Training a Linear Regression Model
SUPERVISED LEARNING AND TRANSFER LEARNING
- Fashion Product Recognition Problem
- What Is Supervised Learning?
- What Is Transfer Learning?
- Model Zoo in Caffe2
- Fine-Tune a Model for Recognizing Fashion Products
SEQUENCE-TO-SEQUENCE LEARNING
- Chatbot Customer Service
- What Is Sequence-to-Sequence Learning?
- What Are RNNs and LSTMs?
- Training an RNN-Based Model to Write like Shakespeare
REINFORCEMENT LEARNING
- Why Deep Reinforcement Learning?
- What Is Deep Reinforcement Learning?
- What Is Deep Q-Network?
- Training a Deep Q- Network for Solving the Cart-Pole Problem
RUNNING AI IN YOUR HANDS
- AI on Mobile Devices Using Face ID
- Challenges in Running AI Models on Mobile Devices
- SequeezeNet
- Deploy SequeezeNet on a Mobile Device
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