What Is Deep Learning?

Deep Learning vs Machine Learning

Working of Neural Networks

How does Deep Learning work?

Neurons

Applications of Deep Learning

Limitations of Deep Learning

Quiz

Deep Learning Frameworks

Conclusion

What is Machine Learning?

What is Deep Learning?

Feedforward vs Backpropagation

Wrap up

Introduction

What you don’t need to do deep learning

What is the point of learning deep learning

Neural Nets: a brief history

Top to bottom learning approach

The software stack

Git Repositories

First practical exercise in Jupyter Notebook

Interpretation and explanation of the exercise

Stochastic Gradient Descent (SGD)

Consider how a model interacts with its environment

"doc" function and fastai framework documentation

Image Segmentation

Classifying a review's sentiment based on IMDB text reviews

Predicting salary based on tabular data from CSV

Lesson Summary

Introduction

What has changed since 2015

Images are made of numbers

Downloading images

Training the model and making a prediction

What can deep learning do now

How the course will be taught. Top down learning

Jeremy Howard’s qualifications

Visualizing layers of a trained neural network

Image classification applied to audio

Pytorch vs Tensorflow

Example of how Fastai builds off Pytorch (AdamW optimizer)

Bird or not bird? & explaining some Kaggle features

Best practice - viewing your data between steps

Datablocks API overarching explanation

Where to find fastai documentation

Fastai’s learner (combines model & data)

What’s a pretrained model?

Testing your model with predict method

Segmentation code explanation

Tabular analysis with fastai

Collaborative filtering (recommendation system) example

How to turn your notebooks into a presentation tool (RISE)

What can deep learning do presently?

Homework

Deep Learning Specialization

Course Overview

DeepLearning.ai

Course Instructors

Enrollment Requirements

Course Structure

Pricing and Certification

Career Opportunities

Student Reviews

Is Deep Learning Specialization Worth It?

Introduction

Course information

Why deep learning?

The perceptron

Perceptron example

From perceptrons to neural networks

Applying neural networks

Loss functions

Training and gradient descent

Backpropagation

Setting the learning rate

Batched gradient descent

Regularization: dropout and early stopping

Summary

Introduction

Deep learning in one slide

History of ideas and tools

Simple example in TensorFlow

TensorFlow in one slide

Deep learning is representation learning

Why deep learning (and why not)

Challenges for supervised learning

Key low-level concepts

Higher-level methods

Toward artificial general intelligence

Deep learning example

End-to-end learning

Deep learning in robotics

History of Deep Learning

Why is Deep Learning so popular?

Deep neural networks

Examples

Detecting or locating objects of interest in an image

History of Geometry

What is Machine Learning?

Scale-separation

Graph isomorphism test

GNN special cases

Manifold learning

The Convolution Theorem in Signal Processing

Gauge Transformations

Drug design

Conclusion

How is the course organized?

Course Structure

Machine Learning

Course overview

Course Outline

Introduction

What is machine learning?

What is deep learning?

Applications of deep learning

The main differences between machine learning and deep learning