Deep Learning is one of the most sought-after AI abilities. A good Deep Learning online course will assist you in mastering Deep Learning. This is because it will give you an understanding of what it entails.

Deep learning refers to a class of **machine learning techniques **that employ numerous layers to extract higher-level features from raw data.

It can be supervised, semi-supervised, or unsupervised and is based on artificial neural networks and representation learning.

Convolutional neural networks are often used in deep learning models, although they can also include propositional formulae or latent variables grouped per layer.

With this in mind, we’ve put together a list of the top deep learning courses online that can improve your neural network and machine learning skills for business or play.

This isn’t a comprehensive list though, but it does include the top deep learning online courses from Udemy, a reputable online platform.

Aside from that, we’ve taken time to talk extensively about what Deep Learning is, how it works, and why you need it. The table of contents below will guide you!

## What is Deep Learning?

Deep Learning is a subset of **Artificial Intelligence,** a machine learning technique for teaching computers and devices how to think logically.

The phrase “deep learning” comes from the fact that it entails delving into numerous layers of a network, including a hidden layer. The further you delve into it, the more detailed information you’ll find.

To simulate human intellect, its approaches rely on various complicated systems. This strategy teaches machines to recognize themes to classify them into different categories.

Deep learning requires pattern recognition, and thanks to machine learning, computers no longer need to rely on complex programming.

Thanks to deep learning, machines can use photos, text, or audio information to identify and do any task in a human-like manner.

Deep learning is changing daily lives, as evidenced by self-driving cars, tailored suggestions, and voice assistants.

Deep learning is a branch of machine learning that focuses on iterative learning approaches that expose machines to large data sets. It aids computers in picking up identifying qualities and adapting to change by doing so.

Machines that are repeatedly exposed to data sets learn to recognize different logics and come to a reliable data conclusion.

Deep learning has improved in recent years, allowing it to perform increasingly complex operations with greater accuracy. It’s no surprise that this field is gaining interest and attracting young professionals.

**What is the significance of Deep Learning?**

Deep Learning plays a significant role in making our daily lives more accessible, and this trend is to continue.

Deep learning is powering a lot of automation in today’s society, whether automated parking or face recognition at the airport.

On the other hand, deep learning’s importance can deal with the fact that our world is currently producing exponential volumes of data, which require a large-scale structure.

Deep Learning has made excellent use of the expanding volume and data availability. All the knowledge gathered from these sources creates correct results through iterative learning models.

## How does it work?

Deep learning, at its heart, uses iterative approaches to educate robots to mimic human intellect. This iterative process uses an artificial neural network at various hierarchical levels.

The first levels assist the machines in learning basic information, and as the levels progress, the data continues to grow.

With each new group, devices gather more data and mix it with what they learned in the previous grade.

The system collects an ultimate piece of information, a compound input, at the end of the procedure. This data is organized into many layers and resembles advanced logical reasoning.

## What’s the difference between Machine Learning and Deep Learning?

Deep learning and machine learning are both parts of artificial intelligence but are not the same thing, despite being commonly used interchangeably.

Machine Learning is a more extensive term that refers to defining and creating learning models using data. Machine learning uses statistical models to comprehend the structure of data.

It begins with data mining, which involves manually extracting useful information from large data sets.

Then followed by applying algorithms to instruct computers to learn from the data and generate predictions. Machine learning has been around for a while and has progressed through time.

Deep Learning is a relatively new topic that focuses only on neural networking to learn and function.

As previously said, neural networking artificially duplicates human brain networks to screen and gain information from data.

Because deep learning is an end-to-end learning process in which raw data is supplied into the system, the more data it examines, the more precise and accurate the outputs will be.

This leads to the second distinction between deep learning and machine learning.

While the former can scale up with more extensive data, machine learning models are limited to shallow learning.

It reaches a plateau beyond a certain point, and any additional data adds no value. The critical distinctions between the two domains are:

**Data Set Size:**

Deep Learning does not work well with a smaller data set. Machine Learning algorithms, on the other hand, can process a smaller data set without sacrificing performance.

Although more data improves the model’s performance, a smaller data set may be the best choice for a specific function in classical machine learning.

**Featured Engineering: **

Featured engineering is an essential aspect of all machine learning algorithms, and its complexity distinguishes ML from DL.

In classical machine learning, an expert defines a model’s features and then hand-codes the data type and functions.

On the flip side, Deep Learning does feature engineering at sub-levels, separating low-level features from high-level characteristics to feed to neural networks.

**Technology Requirements:**

Sophisticated high-end hardware is necessary to handle the heavyweight of matrix multiplication operations and computations, the hallmark of deep learning.

On the flip side, machine learning algorithms run on even the most basic computers. Deep Learning algorithms require gPUs for complicated computations to be efficiently optimized.

**Time to Execution: **

Because a deep learning algorithm is more developed than a machine learning algorithm, it is easy to believe it will take less time to execute.

On the other hand, deep learning causes a more extended training period because of the massive data collection and the neural network’s intricacy.

## What is the best way to get started with Deep Learning?

Candidates must ensure adequate mathematical and computer language skills before working with Deep Learning.

Because Deep Learning is a subset of artificial intelligence, expertise with broader concepts is frequently required. Deep Learning’s fundamental abilities are:

**Maths: **

If the mere mention of the word “maths” makes you nervous, let me reassure you. Deep learning has simple mathematical prerequisites, similar to those taught at the undergraduate level.

Calculus, probability, and linear algebra are just a few of the concepts you’ll need to master. Many ebooks and math lessons are available online for professionals who want to gain deep learning skills but don’t have a math degree.

Knowledge of several programming languages is another need for understanding Deep Learning.

Because Python is a highly interactive, portable, dynamic, and **object-oriented programming language,** and deep learning book will disclose that there are several applications for Deep Learning in Python.

It features many support libraries that limit the amount of code that needs to be written for specific functionalities.

It integrates seamlessly **with C, C++**, or Java, and its control features, and great support for objects, modules, and other reusability techniques, make it the clear choice for deep learning projects.

**Cloud Computing:**

Because Cloud now hosts practically much computing, a basic understanding of Cloud is required to grasp Deep Learning.

Beginners should begin by learning how Cloud service providers operate. Examine topics like computing, databases, storage, and migration in depth.

Knowledge of major cloud service providers, such as AWS and Azure will also give you an advantage.

Cloud computing necessitates a basic understanding of networking, which is strongly related to Machine Learning.

These strategies are not mutually exclusive, and understanding these notions can help you master the skills more quickly.

Now that we’ve studied Deep Learning principles, it’s time to go deeper into the various applications of deep learning.

## Types of Deep Learning

Let’s take a look at the different types of Deep Learning:

### 1. **Deep Learning for Computer Vision:**

Computer vision is used in Deep Learning methods to train computers in image classification, object identification, and face recognition. Simply put, computer vision aims to mimic human perception and the functions it performs.

### 2. **Deep Learning for Text and Sequence: **

Deep learning is used in various text and audio classifications, including speech recognition, sentiment classification, machine translation, DNA sequence analysis, and video activity recognition, among others.

Sequence models are used to train computers to interpret, identify, and classify information in each of these scenarios.

Many-to-many, many-to-one, and one-to-many recurrent neural networks are used for sentiment classification, object identification, and other tasks.

### 3. **Deep Generative Learning: **

Generative models are employed in unsupervised learning for data distribution. The goal of the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) is to distribute data in the most efficient way possible.

This is so that computers can produce new data points from diverse variations. GAN seeks to balance Generator and Discriminator, whereas VAE maximizes the lower limit for data-log likelihood.

### What are the Top Deep Learning Online Courses?

### #1. Deep Learning A-Z: Hands-On Artificial Neural Networks

**Cost: $74.18**

**Instructor: Kirill Eremenko**

**Course Duration: 22h 37m **

**Level: Beginners**

Deep Learning A-Z is one of the best Deep Learning courses online explicitly designed for students interested in Deep Learning.

In this course, Eremenko and Hadelin will teach you how to apply Artificial Neural Networks in practice.

During this 22.5-hour on-demand video course, you will learn how to apply the intuition behind artificial, convolutional, and Recurrent Neural Networks.

All you need is to be at high school mathematics level and have **basic Python programming knowledge.** Note, at the end of this course; you’ll get a certificate of completion.

### #2. Machine Learning, Data Science and Deep Learning with Python

**Cost: $59.78**

**Instructor: Frank Kane**

**Course Duration: 15h 36m**

**Level: Intermediate**

With this Deep Learning online course, you will learn how to use Tensorflow and Keras to create artificial neural networks.

Also, you’ll learn how to classify images, data, and sentiments, use linear regression, polynomial regression, and multivariate regression to make predictions.

Moreso, you’ll learn how to build a Pac-Man bot and how to understand reinforcement learning.

The instructor, Frank Kane, will also teach you how to choose and optimize your models using train/test and K-Fold cross-validation during the course.

Talking about what you need for this 15.5-hour on-demand video Deep Learning course, you must have prior coding or scripting experience and at least high school-level math skills.

### #3. Deep Learning: GANs and Variational Autoencoders

**Cost: $15.61**

**Instructor: Lazy Programmer Team**

**Course Duration: 7h 43m**

**Level: Intermediate**

Do you** **want to improve your deep learning knowledge? This Deep Learning course is for you.!

In this top Deep Learning course, you’ll learn the basic principles of generative models. Also, you’ll learn how to build a GAN and a variational autoencoder in Theano and TensorFlow.

To enjoy this course, you must know how to build a neural network in Theano and Tensorflow, understand the concept of probability, multivariate Calculus, NumPy, etc.

### #4. A deep understanding of Deep Learning (with Python intro)

**Cost: $10.80**

**Instructor: Mike X CohenTeam**

**Course Duration: 57h 17m**

**Level: Intermediate**

This Deep Learning online course aims to provide you with a thorough understanding of deep learning.

You will get deep learning skills that are adaptable, foundational, and long-lasting.

Also, you will have a thorough understanding of deep learning’s fundamental concepts, allowing you to study new topics and trends as they develop in the future.

Keep in mind that this course is not for anyone looking for an overview of deep learning with a few solved examples.

It is for those who want to learn how Deep Learning works, when and how to choose meta parameters like optimizers, normalizations, and learning rates.

It will be best for you to learn how to evaluate the performance of deep neural network models and change and adapt existing models to solve new problems.

### #5. [2022] Machine Learning and Deep Learning Bootcamp in Python

**Cost: $52.58**

**Instructor: Holczer Balazs**

**Course Duration: ** **31h 6m**

**Level: Intermediate**

Do you want to learn more about machine learning, deep learning, and computer vision? Then this is the course for you!

The core ideas of machine learning, deep learning, reinforcement learning, and machine learning are covered in this course.

In this Deep Learning course, you’ll learn how to solve regression and classification issues and how to employ neural networks.

Deep neural networks (DNNs), convolutional neural networks (CNNS), and recurrent neural networks (RNNs) will also be covered (RNNs).

### #6. Deep Learning Prerequisites: Logistic Regression in Python

**Cost: $64.58**

**Created by: Lazy Programmer inc**

**Course Duration: 6h 16m**

**Level: Beginners**

The Deep Learning Prerequisite course is a prerequisite for deep learning and neural networks; it teaches logistic regression, a prominent and essential technique in machine learning, data science, and statistics.

This course covers the theory from the ground up, including deducing the answer and applying it to real-world problems.

You will learn how to write your logistic regression package in Python during this course.

You’ll also learn to use logistic regression for real-world business problems, such as forecasting user actions from e-commerce data and facial recognition.

In this 6.5-hour on-demand video, you’ll discover more about why regularization is employed in machine learning.

You must be familiar with the Numpy Stack and have some basic Python coding skills in the prerequisites.

### #7. Deep Learning: Convolutional Neural Networks in Python

**Cost: $59.78**

**Created by: Lazy Programmer inc**

**Course Duration: 12h 1m**

**Level: Beginners**

The Convolutional Neural Network (CNN) has been used to achieve innovative outcomes in computer vision applications like object identification, picture segmentation, and photo-realistic images of people and things that don’t exist in the real world!

This Deep Learning course will teach the basics of convolution and why it’s useful for deep learning and even natural language processing (natural language processing).

You’ll learn innovative approaches like data augmentation and batch normalization and how to construct modern architectures like VGG.

### #8. Recommender Systems and Deep Learning in Python

**Cost: $59.78**

**Created by: Lazy Programmer inc**

**Course Duration: 12h 6m**

**Level: Beginners**

If you’re a machine learning, deep learning, artificial intelligence, or data science student, this is one of the best Deep Learning courses to take in 2022.

Using innovative and straightforward algorithms, you’ll learn to analyze and implement suggestions for your users during this course.

You’ll also learn to use an AWS EC2 cluster to extensive data matrix factorization on Spark.

You’ll also learn how to use pure Numpy to comprehend the notion of matrix factorization / SVD.

### #9. Natural Language Processing with Deep Learning in Python

**Cost: $20.41**

**Created by: Lazy Programmer inc**

**Course Duration: 12h 0m**

**Level: Beginners**

The Lazy Programmer Team will walk you through the concept of word2vec in this Deep Learning online course.

The CBOW approach and the skip-gram method in word2vec will be covered in this course.

You’ll also learn how to use word2vec’s negative sampling optimization and how to use gradient descent and alternating least squares to construct GloVe.

In addition, you’ll learn how to employ recurrent neural networks for parts-of-speech tagging and named entity recognition in this 12-hour on-demand video course.

You’ll also learn to use Gensim to get pre-trained word vectors and computer analogies and similarities.

### #10. Complete Guide to TensorFlow for Deep Learning with Python

**Cost: $74.18**

**Created by: Jose Portilla**

**Course Duration: 14h 9m **

**Level: Beginners**

Do you have Python knowledge, and you’re eager to learn the latest Deep Learning Techniques with TensorFlow? This Deep Learning course is for you!

This course will show you how to design artificial neural networks for deep learning to use Google’s TensorFlow framework!

The course aims to provide you with an easy-to-understand introduction to the complexity of Google’s TensorFlow framework.

Moreso, the course is to blend theory and practical application with complete jupyter notebook code guides and easy-to-reference slides and notes. Along the way, we’ll have lots of exercises to put your new skills to the test!

## FAQs

## What is deep learning, and how does it work?

Deep Learning is a subtype of machine learning that processes data in the same way that the human brain does. Deep learning, often known as a deep neural network, is a type of decision-making algorithm that may develop patterns.

## Who is the inventor of deep learning?

Alexey Ivakhnenko, a Soviet mathematician, built tiny functional neural networks in the mid-1960s, which is regarded as the first important deep learning achievement.

## Where do I begin with deep learning?

Before diving into Deep Learning, it’s a good idea to brush up on your linear algebra, calculus, probability, and programming skills. If you want to work in this industry, it’s a good idea to start with a deep learning course first.

## What are some of the applications of deep learning?

Deep learning is a subset of machine learning that is used to process data, recognize speech, translate languages, and make decisions in the same way that the human brain does. Self-driving cars, news aggregation and fraud news detection, virtual assistants, entertainment, and healthcare all use deep learning.

## Where do I begin with deep learning?

Before diving into Deep Learning, it’s a good idea to brush up on your linear algebra, calculus, probability, and programming skills. If you want to work in this industry, it’s a good idea to start with a deep learning course first.

## What are the Benefits of Deep Learning?

Deep learning is useful for tackling complicated problems that need the discovery of hidden patterns in data, the ability to produce high-quality results, the elimination of data labeling, and the avoidance of excessive expenses.