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Build, train & deploy models using the speed & efficiency of Caffe 2 & get future-ready in the world of deep learning

What you'll learn

Learn the Caffe 2 architecture and how to use the platform efficiently

Work with 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 write poems like Shakespeare

Understand the Deep Q Network and how to use it in Caffe2

Implement Back-Propagation and Gradient Descent

Explore different layers of CNN and the problem of Image Classification

Understand the importance of weight initialization and optimization in deep learning

Run your models on mobile devices

Requirements

No prior knowledge of Caffe 2 is required however some knowledge of linear algebra and machine learning will be beneficial.

Description

Caffe 2 is an open-sourced Deep Learning framework, refactored to provide further flexibility in computation. It is a light-weighted and modular framework, and is being optimized for cloud and mobile applications. It boosts Deep Learning on mobile and low-power devices by building, training, and evaluating the models and enables programming for Android and iOS devices, and Raspberry Pi boards.If you want to develop your own customised neural networks and deep learning models which can also be deployed efficiently, then take up this course.This course teaches you to create, train, and deploy your neural networks and deep learning models using Caffe 2. You will b with an introduction to Caffe 2 and learn the basic concepts of Caffe 2 such as blobs, workspaces, operators, and nets. You will then build neural networks and develop an understanding of convolutional neural networks, RNNs, Adam, Dropout, BatchNorm, and more. You will also learn how train and manipulate deep neural networks effectively. Finally, you will learn how to deploy your models on mobile devices.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-On Deep Learning with Caffe2, starts off with the basics of Caffe2 such as blobs, workspaces, operators, and nets. You will then learn how to build a model using Caffe 2'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. Next, 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. Finally, you will learn how to deploy your models on any platform.In the second course, Introduction to Deep Learning with Caffe2, 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 will work on various projects throughout this MOOC with a focus on how to train and manipulate a deep neural network effectively.By the end of this course, you will be able to effectively create and 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.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Shuai Zheng, also known as Kyle, did his Ph.D. degree in Machine Learning and Computer Vision at the University of Oxford. He has published in top-tier machine learning and computer vision conferences such as CVPR, ECCV, and ICCV. His research interests are in deep learning and its applications in computer vision such as semantic sntation. He is currently a research scientist at eBay Inc, where he works on both fundamental and practical problems in Augmented Reality, Computer Vision, and Deep Learning.Abhishek Kumar Annamraju, is the CTO and co-founder at Tessellate Imaging. His research areas include computer vision, machine learning, NLP and photogrammetry. As a part of his undergraduate thesis and then continued employment at Tata Elxsi, India, he built and later lead the machine learning and sensor analytics team. He has research papers on cascade classifiers and shape based object analysis, and a research on traffic sign classifier with accuracies reaching upto 99% as per GTSRB stats is one of the state of art solutions available. He participated in the Google Summer of Code (GSoC), 2016, program, working with Open-Detection, to develop a deep learning oriented vision based classifier and an end-to-end GUI based classifier training module. His past projects include image based monitoring solution to curb illegal sand mining, on-road real- vehicle detection, 3D facial model generation and classification, deep learning based face recognition, and camera auto-calibration for fisheye images (Tesseract Imaging, India). He was also a part of Mahindra rise challenge, 2014, to develop real- stationary-cam object detection modules. His research work includes projects involving forensic sketch to image matching and biomedical image processing.Akash Deep Singh, is the COO and co-founder at Tessellate Imaging and is passionate about combining Artificial Intelligence and Machine Vision. Prior to Tessellate Imaging, he worked on building solutions rag from novel systems to detect and classify glioma cancer to a real- stat generation camera solution for basketball players. He was also part of the team which built India’s first panoramic camera where he acted as the Machine Learning lead. He has a vast experience in building real- object detection and tracking systems. His past projects include autopilot firmware for Search and Rescue drones, building Disguised and Imposter face recognition software, an all-terrain navigation vehicle and sketch to face image matching for forensics. A national cyber olympiad gold medalist, he loves reading books.

Overview

Section 1: Hands-On Deep Learning with Caffe2

Lecture 1 The Course Overview

Lecture 2 Why Deep Learning?

Lecture 3 Machine Learning Categories

Lecture 4 Why Caffe2?

Lecture 5 Install and Set Up Caffe2

Lecture 6 Build a Caffe2 Docker

Lecture 7 Definition of a Computational Graph Through Examples

Lecture 8 Introduce Workspace, Operators, and Nets

Lecture 9 Working with Computational Graphs

Lecture 10 Housing Price Prediction

Lecture 11 Representing a Linear Regression Model in a Computational Graph

Lecture 12 Training Procedure

Lecture 13 Training a Linear Regression Model

Lecture 14 Fashion Product Recognition Problem

Lecture 15 What Is Supervised Learning?

Lecture 16 What Is Transfer Learning?

Lecture 17 Model Zoo in Caffe2

Lecture 18 Fine-Tune a Model for Recognizing Fashion Products

Lecture 19 Chatbot Customer Service

Lecture 20 What Is Sequence-to-Sequence Learning?

Lecture 21 What Are RNNs and LSTMs?

Lecture 22 Training an RNN-Based Model to Write like Shakespeare

Lecture 23 Why Deep Reinforcement Learning?

Lecture 24 What Is Deep Reinforcement Learning?

Lecture 25 What Is Deep Q-Network?

Lecture 26 Training a Deep Q- Network for Solving the Cart-Pole Problem

Lecture 27 AI on Mobile Devices Using Face ID

Lecture 28 Challenges in Running AI Models on Mobile Devices

Lecture 29 SequeezeNet

Lecture 30 Deploy SequeezeNet on a Mobile Device

Section 2: Introduction to Deep Learning with Caffe2

Lecture 31 The Course Overview

Lecture 32 Set Up Caffe2 on Linux

Lecture 33 Understanding the Caffe2 Architecture

Lecture 34 Transitioning from Machine Learning to Deep Learning

Lecture 35 Running an Image Classifier Using Caffe2

Lecture 36 Learn about Matrices Using Python – NumPy

Lecture 37 Understanding and Implementing Logistic Regression and Neural Networks

Lecture 38 Understanding and Implementing Deep Neural Networks

Lecture 39 Caffe2 Introduction

Lecture 40 Caffe2 Python Wrapper

Lecture 41 Mathematical Operators in Caffe2

Lecture 42 Network Creators and Assisters in Caffe2 – Part 1

Lecture 43 Network Creators and Assisters in Caffe2 – Part 2

Lecture 44 Network Creators and Assisters in Caffe2 – Part 3

Lecture 45 How Machines Learn to See!

Lecture 46 Introduction to Convolutional Neural Networks

Lecture 47 Implement a Convolution Layer Using Caffe2

Lecture 48 Pooling Layer and Dropout in Caffe2

Lecture 49 Role of Activation Functions in Solving Non-Linear Optimization

Lecture 50 Machine Learning Strategy

Lecture 51 How to Perform Data Selection, Preparation, and Processing

Lecture 52 Regularization of Neural Networks

Lecture 53 Optimizing Neural Networks

Lecture 54 Optimization Algorithms

Lecture 55 Sequence Learning

Lecture 56 Introduction to Recurrent Neural Networks

Lecture 57 LSTMs – A Special Case of RNNs

Lecture 58 Learning about Word Embeddings

Lecture 59 Introduction to Augmented Recurrent Neural Networks

This course is for data scientists and machine learning enthusiasts who are keen to learn Caffe 2 framework for training deep learning models, building real-world applications, and developing production-grade services and modules to bring automation to real-world scenarios.

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