The best Deep Learning courses online &  Tutorials to Learn Deep Learning courses for beginners to advanced level.

Deep learning courses 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 is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

Top Deep learning Courses, Tutorials, Certifications List

  1. Total TensorFlow: Deep Learning with Python
  2. Neural Networks and Deep Learning
  3. Deep Learning
  4. Deep Learning
  5. Deep Learning A-Z™: Hands-On Artificial Neural Networks
  6. Natural Language Processing with Deep Learning in Python
  7. Modern Deep Learning in Python
  8. Deep Learning Explained
  9. Data Science: Deep Learning in Python
  10. Deep Learning: Recurrent Neural Networks in Python

1.Total TensorFlow: Deep Learning with Python

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques.

⭐ : 4.5 (11,858 ratings)

With this deep learning courses, you will:

  • Understand how Neural Networks Work
  • Build your own Neural Network from Scratch with Python
  • Use TensorFlow for Classification and Regression Tasks
  • Use TensorFlow for Image Classification with Convolutional Neural Networks
  • Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
  • Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
  • Learn how to conduct Reinforcement Learning with OpenAI Gym
  • Create Generative Adversarial Networks with TensorFlow

This deep learning courses online aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks AutoEncoders Reinforcement Learning OpenAI Gym and much more.

You can take Total TensorFlow: Deep Learning with Python Certificate Course on Udemy.

2.Neural Networks and Deep Learning

Learn Neural Networks and Deep Learning from deeplearning.ai. If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new.

⭐ : 4.9 (60,425 ratings)

With this deep learning courses, you will:

  • Understand the major technology trends driving Deep Learning courses
  • Be able to build, train and apply fully connected deep neural networks
  • Know how to implement efficient (vectorized) neural networks
  • Understand the key parameters in a neural network's architecture

If you want to break into cutting-edge AI, this deep learning courses will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning.

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.

This is the first course of the Coursera Deep Learning Specialization Certificate. Be able to explain the major trends driving the rise of deep learning courses, and understand where and how it is applied today. Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

Learn to build a neural network with one hidden layer, using forward propagation and backpropagation. Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

You can take Neural Networks and Deep Learning Certificate Course on Coursera.

3.Deep Learning

AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware.

With this deep learning courses, you will:

  • Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
  • Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
  • Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
  • Application of Deep Learning Courses to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
  • Master Deep Learning Courses at scale with accelerated hardware and GPUs

The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers -- and the job salaries of Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.

Within this series of deep learning courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision.

Throughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers.

This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.

You can take Deep Learning Certificate Course on EDX.

4.Deep Learning

Learn Deep Learning from deeplearning ai. If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

With this deep learning courses, you will:

  • Understand the major technology trends driving Deep Learning Courses
  • Be able to build, train and apply fully connected deep neural networks
  • Know how to implement efficient (vectorized) neural networks
  • Understand the key parameters in a neural network architecture

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects.

You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI. If you want to break into cutting-edge AI, this course will help you do so.

Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.

This is the first course of the Deep Learning Specialization. This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results.

You can take Deep Learning Certificate Course on Coursera.

5.Deep Learning A-Z™: Hands-On Artificial Neural Networks

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

⭐ : 4.5 (23,379 ratings)

With this deep learning courses, you will:

  • Understand the intuition behind Artificial Neural Networks
  • Apply Artificial Neural Networks in practice
  • Understand the intuition behind Convolutional Neural Networks
  • Apply Convolutional Neural Networks in practice
  • Understand the intuition behind Recurrent Neural Networks
  • Apply Recurrent Neural Networks in practice
  • Understand the intuition behind Self-Organizing Maps
  • Apply Self-Organizing Maps in practice
  • Understand the intuition behind Boltzmann Machines
  • Apply Boltzmann Machines in practice
  • Understand the intuition behind AutoEncoders
  • Apply AutoEncoders in practice

Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.

And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there:

1. ROBUST STRUCTURE The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it. That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.

2. INTUITION TUTORIALS So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive feel for the concepts behind Deep Learning algorithms. With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.

3. EXCITING PROJECTS Are you tired of courses based on over-used, outdated data sets? Yes? Well then you're in for a treat. Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges: Artificial Neural Networks to solve a Customer Churn problem Convolutional Neural Networks for Image Recognition Recurrent Neural Networks to predict Stock Prices Self-Organizing Maps to investigate Fraud Boltzmann Machines to create a Recomender System Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize *Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth.

4. HANDS-ON CODING In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after. This is a course which naturally extends into your career.

5. IN-COURSE SUPPORT Have you ever taken a course or read a book where you have questions but cannot reach the author? Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help. In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out.

In this course you will learn both, TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.

PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook. So which is better and for what? Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances. The interesting thing is that both these libraries are barely over 1 year old.

You can take Deep Learning A-Z™: Hands-On Artificial Neural Networks Certificate Course on Udemy.

6.Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets.

⭐ : 4.6 (3,684 ratings)

With this deep learning courses, you will:

  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimization in word2vec
  • Understand and implement GloVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis

In this deep learning courses we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this deep learning course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course. First up is word2vec. In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like: king - man = queen - woman France - Paris = England - London December - Novemeber = July - June We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems. Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity. Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

You can take Natural Language Processing with Deep Learning in Python Certificate Course on Udemy.

7.Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

⭐ : 4.6 (1,799 ratings)

With this deep learning course, you will:

  • Apply momentum to backpropagation to train neural networks
  • Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
  • Understand the basic building blocks of Theano
  • Build a neural network in Theano
  • Understand the basic building blocks of TensorFlow
  • Build a neural network in TensorFlow
  • Build a neural network that performs well on the MNIST dataset
  • Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
  • Understand and implement dropout regularization in Theano and TensorFlow
  • Understand and implement batch normalization in Theano and Tensorflow
  • Write a neural network using Keras
  • Write a neural network using PyTorch
  • Write a neural network using CNTK
  • Write a neural network using MXNet

This deep learning courses continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.

You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time. You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano.

This deep learning course is constantly being updated and more advanced regularization techniques are coming in the near future. In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on - what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning - Theano.

With this library we will also examine the basic building blocks - variables, expressions, and functions - so that you can build neural networks in Theano with confidence. Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best. Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network. With all this extra speed, we are going to look at a real dataset - the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks.

This is THE dataset researchers look at first when they want to ask the question, "does this thing work?" These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

You can take Modern Deep Learning in Python Certificate Course on Udemy.

8.Deep Learning Explained

Learn an intuitive approach to building the complex models that help machines solve real-world problems with human-like intelligence.

With this deep learning course, you will:

  • Learn components of a deep neural network and how they work together
  • Learn the basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
  • Learn the working knowledge of vocabulary, concepts, and algorithms used in deep learning
  • Build end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron)
  • Build a CNN (Convolution Neural Network) model for improved digit recognition
  • Build an RNN (Recurrent Neural Network) model to forecast time-series data
  • Build an LSTM (Long Short Term Memory) model to process sequential text data

Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Deep learning is a sub-field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data.

Deep learning is a key enabler of AI powered technologies being developed across the globe. In this deep learning course, you will learn an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. The intuitive approaches will be translated into working code with practical problems and hands-on experience.

You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. Alternatively, you can leverage the Microsoft Azure Notebooks platform for free.

This deep learning courses6 provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology. At the same time, you will learn simple yet powerful “motifs” that can be used with lego-like flexibility to build an end-to-end deep learning model. You will learn how to use the Microsoft Cognitive Toolkit — previously known as CNTK — to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.

You can take Deep Learning Explained Certificate Course on EDX.

9.Data Science: Deep Learning in Python

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow.

⭐ : 4.6 (5,212 ratings)

With this deep learning courses, you will:

  • Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
  • Learn how a neural network is built from basic building blocks (the neuron)
  • Code a neural network from scratch in Python and numpy
  • Code a neural network using Google's TensorFlow
  • Describe different types of neural networks and the different types of problems they are used for
  • Derive the backpropagation rule from first principles
  • Create a neural network with an output that has K > 2 classes using softmax
  • Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
  • Install TensorFlow

This deep learning course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles.

I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general.

You can take Data Science: Deep Learning in Python Certificate Course on Udemy.

10.Deep Learning: Recurrent Neural Networks in Python

GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences.

⭐ : 4.6 (2,035 ratings)

With this deep learning course, you will:

  • Understand the simple recurrent unit (Elman unit)
  • Understand the GRU (gated recurrent unit)
  • Understand the LSTM (long short-term memory unit)
  • Write various recurrent networks in Theano
  • Understand backpropagation through time
  • Understand how to mitigate the vanishing gradient problem
  • Solve the XOR and parity problems using a recurrent neural network
  • Use recurrent neural networks for language modeling
  • Use RNNs for generating text, like poetry
  • Visualize word embeddings and look for patterns in word vector representations

Like this deep learning course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem - you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks - language modeling. You saw when we studied Markov Models that we could do things like generate poetry and it didn’t look too bad. We could even discriminate between 2 different poets just from the sequence of parts-of-speech tags they used.

In this deep learning course, we are going to extend our language model so that it no longer makes the Markov assumption. Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors. In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance.

We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! Suggested Prerequisites: matrix addition, multiplication probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Can write a neural network in Theano or Tensorflow TIPS (for getting through the course): Watch it at 2x. Take handwritten notes.

This will drastically increase your ability to retain the information. Write down the equations. If you don't, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. Write code yourself, don't just sit there and look at my code.

You can take Deep Learning: Recurrent Neural Networks in Python Certificate Course on Udemy.

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