(3 hours of instruction!)
Speaker: CL Kim
Overview: From the book introduction: “Neural networks and deep learning currently provides the best solutions to many problems in image recognition, speech recognition, and natural language processing.”
This Part 1 and the planned Part 2 (late spring/early summer 2021, to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning.
Reference book: “Neural Networks and Deep Learning” by Michael Nielsen, http://
More from the book introduction: “We’ll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits. …it can be solved pretty well using a simple neural network, with just a few tens of lines of code, and no special libraries.”
“But you don’t need to be a professional programmer.”
The code provided is in Python, which even if you don’t program in Python, should be easy to understand with just a little effort.
Benefits of attending the series:
* Learn the core principles behind neural networks and deep learning.
* See a simple python program that solves a concrete problem: teaching a computer to recognize a handwritten digit.
* Improve the result through incorporating more and more of core ideas about neural networks and deep learning.
* Principle-oriented, with worked-out proofs of fundamental equations of backpropagation for those interested.
* Yet hands-on practical, with simple code examples.
Course Background and Content: This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself. It will be a pre-requisite for the planned second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book, and (in planned Part 2) touching on more recent neural network types such as ResNet.
Introduction to Practical Neural Networks and Deep Learning (Part 1)
Feedforward Neural Networks.
* Simple (Python) Network to classify a handwritten digit
* Learning with Gradient Descent
* How the backpropagation algorithm works
* Improving the way neural networks learn:
** Cross-entropy cost function
** Softmax activation function and log-likelihood cost function
** Rectified Linear Unit
** Overfitting and Regularization:
*** L2 regularization
*** Artificially expanding data set
Introduction to Practical Neural Networks and Deep Learning (planned Part 2, to be confirmed)
Convolutional Neural Networks.
* Local receptive field, Feature map. * Pooling layer. * Simple (Python) Convolutional Neural Network to classify a handwritten digit. * Improving the network, Regularization. * Touch on more recent progress in image recognition, such as Residual Network (ResNet).
Pre-requisites: There is some heavier mathematics in proving the four fundamental equations behind backprogation, so a basic familiarity with multivariable calculus and linear algebra is expected, but nothing advanced is required. (The backpropagation equations can be also just accepted without bothering with the proofs since the provided python code for the simple network just makes use of the equations.)
Speaker Background: CL Kim works in Software Engineering at CarGurus, Inc. He has graduate degrees in Business Administration and in Computer and Information Science from the University of Pennsylvania. He has previously taught for a few years the well-rated IEEE Boston Section class on introduction to the Android platform and API.