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Instructor
Dr Anne Hsu, Instructor - Introduction to Deep Learning

Dr Anne Hsu

Instructor

Associate Professor at Queen Mary, University of London. 20+ years experience as a researcher in Machine Learning ( Gatsby Computational Neuroscience Unit, University College London, UC Berkeley) Published over 30 international peer-reviewed research articles.

Instructor: Dr Anne Hsu

Make sense of the models behind the AI revolution.

Understand the deep learning/neural network models and how they fit within AI and Machine Learning landscape.

Instructor has 20+ years of experience as a researcher in Machine Learning ( Gatsby Computational Neuroscience Unit, University College London, UC Berkeley)

Published over 30 international peer-reviewed research articles.

Course Description

Deep learning models are transforming society. Everyone can understand how these models work. This course walks through all the important concepts relating to deep learning models and how they give rise to the recent results in AI. We use guided examples and discuss a variety of practical applications, all accompanied by animations and visualizations. We also cover recent breakthroughs in deep learning research. This course will demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning.

What am I going to get from this course?



 After taking this course students will be able to understand:
  • What deep learning is and how it  differs from other types of machine learning and artificial intelligence
     
  • How deep learning models use neural networks to make computations
     
  • What types of problems deep  learning models can be used to solve 
     
  • The types of data needed to train deep learning models
     
  • The variety of inputs deep  learning models receive and solutions they produce
     
  • The advantages that deep learning can offer over traditional machine learning
     
  • Why multi-neuron networks are able to solve complex problems
     
  • How neural networks use gradient descent and back-propagation to learn to make predictions
     
  • All material will be accompanied with ample visualisations and animations to provide intuitive understanding
     

Prerequisites and Target Audience

What will students need to know or do before starting this course?

No prerequisites required.

Who should take this course? Who should not?

Who should take Introduction to Deep Learning:
  • Working Professionals who work around AI and technology and would like to have a clearer understanding of how deep learning works
     
  • Managers and Executives from any field who would like to have a firmer grasp of the foundations of cutting-edge Artificial Intelligence 
     
  • Data scientists and machine learning practitioners who would like to expand   their knowledge to deep learning
     
  • Aspiring deep learning practitioners who want to an introduction that provides friendly examples and intuition while still covering the background needed to enables further learning for serious deep learning work.
     
  • Students and fresh graduates who are interested in a career working around or in Data Science Machine Learning and Artificial Intelligence 
     
  • Everyone who would like to understand the fundamental principles that underlie cutting-edge Artificial Intelligence
     
Who should not take Introduction to Deep Learning:      

You want software code-based examples of implementing deep learning models

Curriculum

Module 1: Fundamentals

Lecture 1 Intelligence and Information Processing

In this lecture you will learn what information processing is and how intelligence relates to information processing. You will learn how artificial intelligence is achieved through models that solve information processing problems. (Transcript attached)

Lecture 2 Artificial Intelligence, Machine Learning and Deep Learning part 1

In this lecture go through examples of how AI solves complex information processing problems. You will learn how deep learning sits within the larger context of artificial intelligence and traditional machine learning. (Transcript attached)

Lecture 3 Artificial Intelligence, Machine Learning and Deep Learning part 2

In this lecture we go over an example of how deep learning differs from traditional AI and traditional machine learning. You will also learn about advantages offered by deep learning over traditional machine learning and what factors contributed to the deep learning revolution. (Transcript attached)

Lecture 4 Inputs, Outputs, & Features

In this lecture we go through example inputs deep learning models might receive and example output they might produce. (Transcript attached)

Lecture 5 Importance of Feature Representations

In this lecture we go over why it is important that deep learning models can find efficient feature representations is important and how this relates to the function of our brains. (Transcript attached)

Lecture 6 Efficient Feature Representation Example

Here we walk through an example of efficient feature representation. (Transcript attached)

Lecture 7 Supervised Learning

Overview of supervised learning models (Transcript attached)

Lecture 8 Unsupervised Learning

Overview of unsupervised learning models (Transcript attached)

Lecture 9 Reinforcement Learning

Overview of supervised learning models (Transcript attached)

Module 2: Perceptron: Weights, Biases, Activation Functions

Lecture 10 Introduction to Single Neuron Model

Introduction to single neuron models, which is the basic computing component of deep learning models. (Transcript attached)

Lecture 11 Single Neuron Example: 1D Example of Bluffing at Poker

Walk through of an example of a simple neuron model solving the problem of predicting whether your friend is bluffing at poker. This will provide the groundwork for understanding how neural network models make predictions from data. (Transcript attached)

Lecture 12 Single Neuron Example: 2D Example of Bluffing at Poker

A further example of a single neuron model making predictions, applied to two dimensional data. (Transcript attached)

Lecture 13 Single Neuron Model Limitations

Discussion of the limitations of a single neuron model and the inability of single neurons to solve nonlinearly separable problems. (Transcript attached)

Module 3: Multi-neuron Networks : XOR and nonlinearity

Lecture 14 Binary Logical Operators and XOR

Explanation of XOR, which is an example of a nonlinearly separable problem that a single neuron will not be able to solve. (Transcript attached)

Lecture 15 Multi-neuron solution to XOR

Walk-through of how a multi-neuron network can solve the XOR problem. (Transcript attached)

Lecture 16 Universal Approximation Theorem

A visual sketch of the proof of the universal approximation theorem, which shows how a multi-neuron network can represent any nonlinear function. (Transcript attached)

Lecture 17 Neural Network Walk-through

We step through how a fully connected feed forward neural network takes in input and produces an output. (Transcript attached)

Module 4: Learning: Gradient Descent

Lecture 18 Cost function

In this lecture you will learn about the cost function and how the reduction of the cost function is the goal of neural network learning. (Transcript attached)

Lecture 19 Derivative and Gradient Part 1

In this lecture you will learn about derivatives and gradients, which tell us how to change parameter values in order to minimise the cost function. You will also learn how the gradient is used to update neural network parameters reduces the cost function as the neural network learns. (Transcript attached)

Lecture 20 Derivative and Gradient Part 2

More about derivatives and gradients (Transcript attached)

Lecture 21 Neural Network Parameters and Matrix Notation

In this lecture we will walk through the computations of a fully connected feedforward neural network. We will count the parameters that need to be learned. You will also learn linear algebra notation. (Transcript attached)

Lecture 22 Chain Rule

In this lecture we cover the chain rule and how neural networks use the chain rule to calculate the gradient for learning. (Transcript attached)

Lecture 23 Back propagation and Overfitting

In this lecture we cover back propagation, the algorithm neural networks use to learn. You will also learn about the problem of overfitting. (Transcript attached)

Lecture 24 Back Propagation for Handwritten Digit Recognition

Example of how back propagation is used to train a neural network to recognise handwritten digits. (Transcript attached)

Lecture 25 Variations on Learning

In this lecture we cover variations on back propagation and discuss additional considerations when training neural networks. (Transcript attached)

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