Learn Machine Learning

Learn how to build and deploy deep learning models

What you'll learn

Build your own Neural Network from Scratch with R!

Use MXNet for Image Classification with Convolutional Neural Networks with R!

Learn how to achieve world-class accuracy in the prediction of Breast Cancer by applying Neural Nets with R!

Learn how to use IBM's Deep learning framework

Understand the mathematics behind deep learning

Requirements

No programming experience needed, you'll learn everything you need to know in this course

Basic understanding of computer science

Description

Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognise complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervisedDeep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performanceIn simple terms, Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.From another angle to view deep learning, deep learning refers to ‘computer-simulate’ or ‘automate’ human learning processes from a source (e.g., an image of dogs) to a learned object (dogs). Therefore, a notion coined as “deeper” learning or “deepest” learning [9] makes sense. The deepest learning refers to the fully automatic learning from a source to a final learned object. A deeper learning thus refers to a mixed learning process: a human learning process from a source to a learned semi-object, followed by a computer learning process from the human learned semi-object to a final learned object.In this course, you will learn how to build and deploy your own deep learning models using Rstudio

Overview

Section 1: Introduction

Lecture 1 Introduction: Deep Learning – Definition

Lecture 2 Deep Learning – Why it matters

Lecture 3 Examples of deep learning

Lecture 4 Deep learning - How it works

Lecture 5 Deep learning vs Machine learning

Section 2: Deep learning - Overview

Lecture 6 Overview of deep learning

Lecture 7 Deep learning - Neural Networks

Lecture 8 Why Deep learning works

Section 3: History of Deep learning

Lecture 9 A brief history of deep learning

Lecture 10 Expected growth in the Artificial Intelligence (AI) industry

Lecture 11 Timeline of Deep learning

Lecture 12 Why Warren McCulloch should be known as the grandfather of AI

Lecture 13 Who created the first machine learning program

Lecture 14 Creation of perceptron

Lecture 15 Invention of the first working deep learning models

Lecture 16 Kunihiko Fukusima proposes the neoconitron

Lecture 17 Computer learns to pronounce english words

Lecture 18 LSTM was proposed

Lecture 19 Launch of imagenet

Lecture 20 Artificial pattern-recognition algorithms achieve human-level performance

Lecture 21 Google acquires deep mind for 400 million pounds

Lecture 22 Google's Deepmind beats professional Go player

Section 4: Deep Learning - Deep Learning Models

Lecture 23 Classical deep learning models

Lecture 24 Convolutional Neural Networks

Lecture 25 Recurrent Neural Network

Lecture 26 The power of recurrent neural network

Lecture 27 Sequence to Sequence models

Lecture 28 Reinforcement learning

Lecture 29 Generative Adversarial Networks

Lecture 30 Overview of Generative Adversarial networks

Lecture 31 Deep learning - Challenges

Lecture 32 challenges of deep learning

Lecture 33 Deep learning needs enough data to operate

Lecture 34 Artificial Intelligence and the hype

Lecture 35 Becoming production ready

Lecture 36 Deep learning does not understand context very much

Lecture 37 Cybersecurity challenges

Lecture 38 It is difficult to know how deep learning arrives at its insight

Lecture 39 AI developers should know the limits of deep learning

Section 5: Deep learning App – Speech recognition

Lecture 40 Speech recognition

Lecture 41 Steps needed for a deep learning project

Lecture 42 Problem formulation

Lecture 43 Case study

Lecture 44 Data preparation

Lecture 45 Data partitioning

Lecture 46 Data size

Lecture 47 Data pre-processing

Lecture 48 Case study

Lecture 49 Metric definition

Lecture 50 Case study

Lecture 51 Model development

Lecture 52 Case study

Lecture 53 Analysing audio file using IBM

Lecture 54 Cleaning up the dataset

Lecture 55 Putting an audio file in the right working directory

Lecture 56 Reading the result

Lecture 57 Reading the result

Lecture 58 Deployment

Lecture 59 File uniform resource location

Developers curious about deep learning and how it works,Beginner R developers interested in data science