Deep learning is an artificial intelligence technique that imitates the human brain in processing data to recognize patterns and make decisions. It has transformed industries like web search and advertising and is enabling new products and businesses. Deep learning uses neural networks and applications like medicine, self-driving cars, agriculture, and more. It is considered a highly sought after skill and part of the rapid rise of artificial intelligence. This document provides an overview of deep learning and what topics will be covered, including neural networks, deep neural networks, improving models, structuring machine learning projects, convolutional neural networks, and natural language processing.

1. Neural Network and
Deep Learning
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001 – What is Deep Learning?

2.  Deep Learning is an artificial intelligence function that imitates the
workings of the human brain in processing data and creating patterns for
use in decision making.
 Deep Learning transformed internet businesses like web search and
advertising.
 Deep Learning is enabling brand new products and businesses.
001 – What is Deep Learning?

3. 001 – What is Deep Learning?
 Deep Learning uses:
 Medicine (reading x-ray images)
 Self driving car
 Agriculture (precision agriculture)
 And many more...

4.  Opportunity to create an amazing world powered by AI.
 AI is the new electricity
 Electricity transformed every major industry, from manufacturing to
healthcare and many more.
 AI is on path to bring about an equally big transformation.
 Big part of rapid rise of AI is driven by developments in deep
learning.
 One of the highly sought out skills.
001 – What is Deep Learning?

5.  What will we learn?
 Neural Networks and Deep Learning
 You will learn how to build a neural network including a deep neural network.
 How to train it on data.
 In the end, you will be able to build a deep neural network to recognize cats.
 Improving Deep Neural Networks
 Hyperparameter tuning, Regularization, Optimization, how to diagnose bias and variance.
 Structuring your Machine Learning project
 The strategy has changed in the era of deep learning.
 A split of train and test
 End-to-end deep learning
 Convolutional Neural Networks
 Often applied to images
 Natural Language Processing
 Building sequence models
 Applying them to sequence data
 Natural language is just a sequence of words
001 – What is Deep Learning?