# Deep Learning Course by Udacity

**#18**in category Data Science

Learning Experience | 9.4 |
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In the Deep Learning Course, you will master fundamentals of AI that will enable you to go further in the field towards advanced career path.

## About this course

The Deep Learning Course from Udacity is a Nano-degree program that offers you a solid introduction to the world of artificial intelligence. In this program, you will master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of deep learning talent that will help define a beneficial, new, AI-powered future for our world.

Before proceeding to the deep learning course details, let us first understand what is deep learning and why is it popular these days?

## What is Deep Learning?

Deep Learning is a subset of Machine Learning, which on the other hand, is a subset of Artificial Intelligence. Artificial Intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine Learning represents a set of algorithms trained on data that make all of this possible.

Deep Learning is just a type of Machine Learning inspired by the structure of the human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.

The human brain works similarly. Whenever we receive new information, the brain tries to compare it with known objects. Deep neural networks also use the same concept.

## Why is Deep Learning is Popular these Days?

Long before deep learning was used, traditional machine learning methods were mainly used. Such as Decision Trees, SVM, Naïve Bayes Classifier, and Logistic Regression.

These algorithms are also called flat algorithms. Flat here means that these algorithms were not normally be applied directly to the raw data (such as .csv, images, text, etc.). We need a preprocessing step called Feature Extraction. Feature Extraction is a representation of the given raw data that these classic machine learning algorithms can now use to perform a task. For example, the classification of the data into several categories or classes. Feature Extraction is usually quite complex and requires detailed knowledge of the problem domain. This preprocessing layer must be adapted, tested, and refined over several iterations for optimal results.

## What you will learn from this Deep Learning course?

You will study cutting-edge topics such as Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Network Deployment, and build projects PyTorch and NumPy. You will learn from authorities such as Ian Good fellow and Jun-Yan Zhu, inventors of types of generative adversarial networks, as well as AI experts Sebastian Thrun and Andrew Trask.

For anyone interested in this transformational technology, this program is an ideal point-of-entry. The program is comprised of 5 courses and 5 projects. Each project you build will be an opportunity to prove your skills and demonstrate what you’ve learned in your lessons.

## Why should you enroll in this Course?

In this program, you will master deep learning fundamentals that will prepare you to launch or advance a career and additionally pursue further advanced studies in the field of AI. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment. Also, you will be able to build projects in Keras and NumPy, in addition to TensorFlow PyTorch. Moreover, you can learn from experts in the field and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nano-degree program is an ideal point-of-entry.

## Syllabus

### Course 1: Deep Learning course: Neural Networks

##### LESSON ONE: Introduction to Neural Networks

- In this lesson, you will learn solid foundations on deep learning and neural networks. You’ll also implement gradient descent and backpropagation in Python.

##### LESSON TWO: Implementing Gradient Descent

- Mat and Luis will introduce you to a different error function and guide you through implementing gradient descent using NumPy matrix multiplication.

##### LESSON THREE: Training Neural Networks

- Now that you know what neural networks are, you will learn several techniques to improve their training in this lesson. Learn how to prevent overfitting of training data and best practices for minimizing the error of a network.

##### LESSON FOUR: Sentiment Analysis

- In this lesson, Andrew Trask, the author of Grokking Deep Learning, will show you how to define and train neural networks for sentiment analysis (identifying and categorizing opinions expressed in the text).

##### LESSON FIVE: Deep Learning WIth Pytorch

- Learn how to use PyTorch for building and testing deep learning models.

### Course 2: Deep Learning course: Convolutional Neural Networks

##### LESSON ONE: Cloud Computing

- Take advantage of Amazon’s GPUs to train your neural network faster. In this lesson, you’ll set up an instance on AWS and train a neural network on a GPU.

##### LESSON TWO: Convolutional Neural Network

- Alexis and Cezanne explain how Convolutional Neural Networks can identify patterns in images and how they help us dramatically improve performance in image classification tasks.

##### LESSON THREE: CNNs In PyTorch

- In this lesson, you’ll walk through an example Convolutional Neural Network (CNN) in PyTorch. You’ll study the line-by-line breakdown of the code and can download the code and run it yourself.

##### LESSON FOUR: Weight Initialization

- In this lesson, you’ll learn how to find good initial weights for a neural network. Having good initial weights often allows a neural network to arrive at an optimal solution faster than without initialization.

##### LESSON FIVE: Autoencoders

- Autoencoders are neural networks used for data compression, image denoising, and dimensionality reduction. Here, you’ll build autoencoders using PyTorch.

##### LESSON SIX: Transfer Learning in PyTorch

- Most people don’t train their own networks on massive datasets. In this lesson, you’ll learn how to finetune and use a pre-trained network and apply it to a new task using transfer learning.

##### LESSON SEVEN: Deep Learning for Cancer Detection

- Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with Convolutional Neural Networks in this lesson.

### Course 3: Deep Learning course: Recurrent Neural Networks

##### LESSON ONE: Recurrent Neural Networks

- Oral will introduce Recurrent Neural Networks (RNNs), which are machine learning models that can recognize and act on sequences of inputs.

##### LESSON TWO: Long Short-Term Memory Network

- Luis explains Long Short-Term Memory Networks (LSTM) and similar architectures that form a memory about a sequence of inputs over time.

##### LESSON THREE: Implementation of RNN & LSTM

- Train recurrent neural networks to generate new characters, words, and bodies of text.

##### LESSON FOUR: Hyperparameters

- In this lesson, we’ll look at several different hyperparameters that are important for our deep learning work, such as learning rates. We’ll discuss starting values and intuitions for tuning each hyperparameter.

##### LESSON FIVE: Embeddings & Word2vec

- In this lesson, you’ll learn about embeddings in neural networks by implementing a word2vec model that converts words into a representative vector of numerical values.

##### LESSON SIX Sentiment Prediction RNN

- In this lesson, you’ll learn to implement a recurrent neural network for predicting sentiment. This is intended to give you more experience building RNNs.

### Course 4: Deep Learning course: Generative Adversarial Networks

##### LESSON ONE: Generative Adversarial Network

- Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You’ll also implement your own GAN on a simple dataset.

##### LESSON TWO: Deep Convolutional GANs

- Implement a Deep Convolutional GAN to generate complex, color images of house numbers.

##### LESSON THREE: PIX2PIX & Cyclegan

- Jun-Yan Zhu and Cezanne lead you through a CycleGAN formulation that you can learn from unlabeled sets of images.

## This Nano-degree Program Includes:

- Experienced Project reviews.
- Technical mentor support.
- Personal career services.

## How is the Nano-degree program structured?

The Deep Learning Nano-degree program is comprised of content and curriculum to support five (5) projects. Also, they estimated that students could complete the program in four (4) months working 10 hours per week. The Udacity reviewer network will review each project. Feedback will be provided, and if you do not pass the project, you will be asked to resubmit the project until it passes.

## About Project description

Throughout this Nano-degree program, you will have the opportunity to prove your skills by building the projects.

## Prerequisites:

Students interested in enrolling must have intermediate-level Python programming knowledge and experience with NumPy and pandas. You will need to be able to communicate fluently and professionally in written and spoken English. Additionally, students must have the necessary math knowledge, including algebra and some calculus—specifically partial derivatives and matrix multiplication (linear algebra).

## If you do not meet the requirements to enroll, What should you do?

Udacity has several Nano-degree programs and free courses that can help you prepare, which includes:

- Introduct to Data Analysis
- Introduction to Computer Science
- Introduction to Python
- Linear Algebra Refresher
- Introduction to Programming Nanodegree program with Data Analysis specialization

*Note: Your review matters** *

*If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

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## Description

## About this course

The Deep Learning Course from Udacity is a Nano-degree program that offers you a solid introduction to the world of artificial intelligence. In this program, you will master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of deep learning talent that will help define a beneficial, new, AI-powered future for our world.

Before proceeding to the deep learning course details, let us first understand what is deep learning and why is it popular these days?

## What is Deep Learning?

Deep Learning is a subset of Machine Learning, which on the other hand, is a subset of Artificial Intelligence. Artificial Intelligence is a general term that refers to techniques that enable computers to mimic human behavior. Machine Learning represents a set of algorithms trained on data that make all of this possible.

Deep Learning is just a type of Machine Learning inspired by the structure of the human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.

The human brain works similarly. Whenever we receive new information, the brain tries to compare it with known objects. Deep neural networks also use the same concept.

## Why is Deep Learning is Popular these Days?

Long before deep learning was used, traditional machine learning methods were mainly used. Such as Decision Trees, SVM, Naïve Bayes Classifier, and Logistic Regression.

These algorithms are also called flat algorithms. Flat here means that these algorithms were not normally be applied directly to the raw data (such as .csv, images, text, etc.). We need a preprocessing step called Feature Extraction. Feature Extraction is a representation of the given raw data that these classic machine learning algorithms can now use to perform a task. For example, the classification of the data into several categories or classes. Feature Extraction is usually quite complex and requires detailed knowledge of the problem domain. This preprocessing layer must be adapted, tested, and refined over several iterations for optimal results.

## What you will learn from this Deep Learning course?

You will study cutting-edge topics such as Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Network Deployment, and build projects PyTorch and NumPy. You will learn from authorities such as Ian Good fellow and Jun-Yan Zhu, inventors of types of generative adversarial networks, as well as AI experts Sebastian Thrun and Andrew Trask.

For anyone interested in this transformational technology, this program is an ideal point-of-entry. The program is comprised of 5 courses and 5 projects. Each project you build will be an opportunity to prove your skills and demonstrate what you’ve learned in your lessons.

## Why should you enroll in this Course?

In this program, you will master deep learning fundamentals that will prepare you to launch or advance a career and additionally pursue further advanced studies in the field of AI. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment. Also, you will be able to build projects in Keras and NumPy, in addition to TensorFlow PyTorch. Moreover, you can learn from experts in the field and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nano-degree program is an ideal point-of-entry.

## Syllabus

### Course 1: Deep Learning course: Neural Networks

##### LESSON ONE: Introduction to Neural Networks

- In this lesson, you will learn solid foundations on deep learning and neural networks. You’ll also implement gradient descent and backpropagation in Python.

##### LESSON TWO: Implementing Gradient Descent

- Mat and Luis will introduce you to a different error function and guide you through implementing gradient descent using NumPy matrix multiplication.

##### LESSON THREE: Training Neural Networks

- Now that you know what neural networks are, you will learn several techniques to improve their training in this lesson. Learn how to prevent overfitting of training data and best practices for minimizing the error of a network.

##### LESSON FOUR: Sentiment Analysis

- In this lesson, Andrew Trask, the author of Grokking Deep Learning, will show you how to define and train neural networks for sentiment analysis (identifying and categorizing opinions expressed in the text).

##### LESSON FIVE: Deep Learning WIth Pytorch

- Learn how to use PyTorch for building and testing deep learning models.

### Course 2: Deep Learning course: Convolutional Neural Networks

##### LESSON ONE: Cloud Computing

- Take advantage of Amazon’s GPUs to train your neural network faster. In this lesson, you’ll set up an instance on AWS and train a neural network on a GPU.

##### LESSON TWO: Convolutional Neural Network

- Alexis and Cezanne explain how Convolutional Neural Networks can identify patterns in images and how they help us dramatically improve performance in image classification tasks.

##### LESSON THREE: CNNs In PyTorch

- In this lesson, you’ll walk through an example Convolutional Neural Network (CNN) in PyTorch. You’ll study the line-by-line breakdown of the code and can download the code and run it yourself.

##### LESSON FOUR: Weight Initialization

- In this lesson, you’ll learn how to find good initial weights for a neural network. Having good initial weights often allows a neural network to arrive at an optimal solution faster than without initialization.

##### LESSON FIVE: Autoencoders

- Autoencoders are neural networks used for data compression, image denoising, and dimensionality reduction. Here, you’ll build autoencoders using PyTorch.

##### LESSON SIX: Transfer Learning in PyTorch

- Most people don’t train their own networks on massive datasets. In this lesson, you’ll learn how to finetune and use a pre-trained network and apply it to a new task using transfer learning.

##### LESSON SEVEN: Deep Learning for Cancer Detection

- Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with Convolutional Neural Networks in this lesson.

### Course 3: Deep Learning course: Recurrent Neural Networks

##### LESSON ONE: Recurrent Neural Networks

- Oral will introduce Recurrent Neural Networks (RNNs), which are machine learning models that can recognize and act on sequences of inputs.

##### LESSON TWO: Long Short-Term Memory Network

- Luis explains Long Short-Term Memory Networks (LSTM) and similar architectures that form a memory about a sequence of inputs over time.

##### LESSON THREE: Implementation of RNN & LSTM

- Train recurrent neural networks to generate new characters, words, and bodies of text.

##### LESSON FOUR: Hyperparameters

- In this lesson, we’ll look at several different hyperparameters that are important for our deep learning work, such as learning rates. We’ll discuss starting values and intuitions for tuning each hyperparameter.

##### LESSON FIVE: Embeddings & Word2vec

- In this lesson, you’ll learn about embeddings in neural networks by implementing a word2vec model that converts words into a representative vector of numerical values.

##### LESSON SIX Sentiment Prediction RNN

- In this lesson, you’ll learn to implement a recurrent neural network for predicting sentiment. This is intended to give you more experience building RNNs.

### Course 4: Deep Learning course: Generative Adversarial Networks

##### LESSON ONE: Generative Adversarial Network

- Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You’ll also implement your own GAN on a simple dataset.

##### LESSON TWO: Deep Convolutional GANs

- Implement a Deep Convolutional GAN to generate complex, color images of house numbers.

##### LESSON THREE: PIX2PIX & Cyclegan

- Jun-Yan Zhu and Cezanne lead you through a CycleGAN formulation that you can learn from unlabeled sets of images.

## This Nano-degree Program Includes:

- Experienced Project reviews.
- Technical mentor support.
- Personal career services.

## How is the Nano-degree program structured?

The Deep Learning Nano-degree program is comprised of content and curriculum to support five (5) projects. Also, they estimated that students could complete the program in four (4) months working 10 hours per week. The Udacity reviewer network will review each project. Feedback will be provided, and if you do not pass the project, you will be asked to resubmit the project until it passes.

## About Project description

Throughout this Nano-degree program, you will have the opportunity to prove your skills by building the projects.

## Prerequisites:

Students interested in enrolling must have intermediate-level Python programming knowledge and experience with NumPy and pandas. You will need to be able to communicate fluently and professionally in written and spoken English. Additionally, students must have the necessary math knowledge, including algebra and some calculus—specifically partial derivatives and matrix multiplication (linear algebra).

## If you do not meet the requirements to enroll, What should you do?

Udacity has several Nano-degree programs and free courses that can help you prepare, which includes:

- Introduct to Data Analysis
- Introduction to Computer Science
- Introduction to Python
- Linear Algebra Refresher
- Introduction to Programming Nanodegree program with Data Analysis specialization

*Note: Your review matters** *

*If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

FAQ

## Specification:

- Udacity
- Amazon Web Services Facebook Artificial Intelligence
- Microdegree
- Self-paced
- Intermediate
- 3+ Months
- Paid Course (Paid certificate)
- English
- Python
- Basic Scripting in Python
- Data Science with 'Python' Deep learning Natural language processing Neural Networks Pytorch

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