16 Best Deep Learning Tutorial for Beginners and Advanced 2019

16 Best Deep Learning Tutorial for Beginners and Advanced 2019

Do you want to add deep learning as your skill? We are with the best Deep Learning Tutorial for Beginners and Advanced, course, and certification.

We are leaving in the era of machines. machines are everywhere now. It is replacing the traditional ways of working. From a simple alarm clock to artificial intelligence, people are using machines in every sector of life.

With the growth of using machines, the need to control and understand machines have grown. So, the skill of machine learning is in super demand.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

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16 Best Deep Learning Tutorial for Beginners & Advanced, Course, Class, Training and Certification 2019

All the companies all over the world prioritize Deep Learning skills in programmers and data scientists. The internet can offer you an uncountable amount of courses on deep learning.

We have searched and found the few best Deep Learning tutorial for beginners and advanced level. Here, are the best Deep Learning certification and training for you.

1. Deep Learning Specialization

Coursera is offering this special course for those who want to master Deep Learning and start a career in machine learning. This 100% online course will take 3 months to complete. It is an intermediate level course. Anyone with some basic understanding of deep learning can enroll in this Deep Learning tutorial.

The instructors of this Deep Learning specialization are Andrew Ng, Kian Katanforoosh and Younes Bensouda Mourri.

This Deep Learning specialization is consist of five courses. You have to complete all of these courses and the hands-on project successfully to earn your certificate on Deep Learning Specialization.

This certification course will teach you about the foundations of Deep Learning. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and so on.

They will teach you to use Python and TensorFlow while practicing Deep Learning. You will be applying deep learning in many case studies like healthcare, autonomous driving, sign language reading, music generation, and natural language processing.

So, get into this specialization if you want to break into AI.

The five courses under this specialization are discussed below.


a. Neural Networks and Deep Learning

This is the first-course among the five courses under the Deep Learning Specialization offered in Coursera. This intermediate course will take approximately 17 hours to complete. It has a rating of 4.9 out of 5.

This is the foundation course of Deep Learning. With this tutorial, you will be able to understand to major technology that drives Deep Learning. You will learn to build, train and apply fully connected deep neural networks. This online course will teach you how to implement efficient (vectorized) neural networks and the key parameters in a neural network’s architecture.

The syllabus of this course contains-

  • Introduction to deep learning
  • Neural Networks Basics
  • Shallow neural networks
  • Deep Neural Networks

This online tutorial will teach you the skills like Artificial Neural Network, Backpropagation, Python, and Programming Deep Learning. After finishing this Deep Learning tutorial, you will be able to apply Deep Learning into your own application.


b. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This second course under the Deep Learning Specialization is a beginner level online course. It has a rating of 4.9 out of 5. It takes approximately 14 hours to complete.

With this deep learning tutorial, you will be able to understand what drives the performance and be able to get good results more systematically. You will learn the best practices for building Deep Leaming applications.

You will learn about initialization, L2 and dropout regularization, Batch normalization, gradient checking. This online course will teach you how to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence.

Also, you will learn to implement a neural network in TensorFlow. The syllabus for this Deep Learning tutorial includes-

  • Practical aspects of Deep Learning
  • Optimization algorithms
  • Hyperparameter tuning, Batch Normalization, and Programming Frameworks

After this training, you will have some knowledge of Hyperparameter, Tensorflow, Hyperparameter Optimization, and Deep Learning.

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c. Structuring Machine Learning Projects

This is the third course under the Deep Learning Specialization. It is a beginner level online course. This tutorial has a rating of 4.8 out of 5 on Coursera. It takes approximately 7 hours to finish.

This online course contains some topics which most of the other courses on Deep Learning never teach. It has two “flight simulators” that let you practice decision-making as a machine learning project leader. You will acquire some “industry experience” while doing this online course.

With this Deep Learning tutorial, you will have an understanding of how to diagnose errors in a machine learning system and complex ML setting. You will have the knowledge on the most promising directions for reducing error. Moreover, you will know how to apply end-to-end learning, transfer learning, and multi-task learning.

As syllabus, this part of the Deep Learning specialization will follow two strategies.

  • ML Strategy (1) – with 13 videos
  • ML Strategy (2) – with 11 videos

This online course will show you the way to be a successful technical leader in AI.


d. Convolutional Neural Networks

This is the fourth course under the Deep Learning Specialization. This intermediate level course takes approximately 20 hours to complete. It has a rating of 4.8 out of 5 on Coursera.

With this online course, you will be building a convolutional neural network, including recent variations such as residual networks. This tutorial will teach you to apply convolutional networks to visual detection and recognition tasks. You will know to use neural style transfer to generate art. You will be able to apply these algorithms to a variety of images, videos, and other 2D or 3D data.

The syllabus for this online training contains-

  • Foundations of Convolutional Neural Networks
  • Deep convolutional models: case studies
  • Object detection
  • Special applications: Face recognition & Neural style transfer

With this Deep Learning tutorial, you will gain the skills of Facial Recognition System, Tensorflow, Convolutional Neural Network, and Artificial Neural Network


e. Sequence Models

This is the fifth and last course offered under the Deep Learning Specialization. It takes approximately 17 hours to complete. It is an intermediate level course which has a rating of 4.8 out of 5.

With this tutorial, you will understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. Moreover, you will learn how to apply sequence models to natural language problems, including text synthesis. Also, you will know to audio applications, like speech recognition and music synthesis.

The syllabus for this tutorial contains-

  • Recurrent Neural Networks
  • Natural Language Processing & Word Embeddings
  • Sequence models & Attention mechanism

The skills, that you will gain after this online course, are Recurrent Neural Network, Artificial Neural Network, Deep Learning, and Long Short-Term Memory (ISTM).

With this tutorial, your training on deep learning will be complete. You will receive a completion certificate. These five courses under the Deep Learning Specialization will help you to master Deep Learning.


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

Data scientist Kirill Eremenko and AI entrepreneur Hadelin de Ponteves, from SuperDataScience Team, are offering this deep learning tutorial for beginners. These two experts will teach you to create Deep Learning Algorithms in Python. This training course has a rating of 4.5 out of 5 and over 142,000 enrolled students.

Here, you will be provided with 22.5 hours on-demand video and 30 articles along with lifetime access to this course. With all these materials, you will be able to understand the intuition behind Artificial Neural Networks, Convolutional Neural Networks, Convolutional Neural Networks, Self-Organizing Maps, Boltzmann Machines, and AutoEncoders. Moreover, you will learn to apply all these in practice.

Deep Learning A-Z has some characteristics that give this tutorial some uniqueness. it has-

  • ROBUST STRUCTURE
  • INTUITION TUTORIALS
  • EXCITING PROJECTS
  • HANDS-ON CODING
  • IN-COURSE SUPPORT

This Deep Learning course will teach you to use TenorFlow and Python in deep learning. You will also know about Theano, Keras, and Scikit-learn to get a global vision of your learning. You will also get real-world case studies like churn modeling problem, image recognization, stock price prediction, fraud detection, recommender system, and more, throughout this online course.

Anyone who was interested in deep learning and some basic knowledge on Python programming language can enroll in this course. This Deep Learning tutorial for beginners will help you to get into the world of machine learning.


3. Machine Learning, Data Science and Deep Learning with Python

Founder of Sundog Education, Frank Kane, is offering this Deep Learning tutorial on Udemy. This online course will teach you about machine learning with data science, Tensorflow, artificial intelligence, and neural networks. It has a rating of 4.5 out of 5 and over 82,000 enrolled students.

With this online course, you will get your hands on 12 hours on-demand video and 3 articles with lifetime access. From these materials, you will learn to build artificial neural networks with Tensorflow and Keras. You will be able to implement machine learning at massive scale with Apache Spark’s MLLib. Overall, this Deep Learning tutorial will help you to gain some real-world experience.

This comprehensive online tutorial will provide you with 80 lectures that include hands-on Python course. The topics that are covered in this course are-

  • Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
  • Sentiment analysis
  • Image recognition and classification
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross-validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multivariate Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests

At the end of this course, you will get a final project to apply your skills. To enroll in this course, you need to have some prior coding experience and basic knowledge of Python programming language. The instructor here uses Microsoft Windows-based PC. This machine learning training will open new gates for your career.


4. AI & Deep Learning with TensorFlow

This Deep Learning in TenorFlow with Python Certification training fulfills the requirements of the industry. This online course on Edureka has a rating of 4.5 out of 5 and over 13,000 satisfied learners.

This Deep Learning tutorial has 30 hours online instructor-led sessions. It has real-life case studies and assignments for you. You will a cloud lab to practice your new skills on a pre-configured environment.

In this training, you will learn about what is AI, explore neural networks, understand Deep Learning frameworks, implement various machine learning algorithms using Deep Networks. You will learn about the different layers in neural networks do data abstraction and feature extraction using Deep Learning. You will receive rich hands-on training on Deep Learning in TensorFlow with Python.

The curriculum of this tutorial covers topics

  • Introduction to Deep Learning
  • Understanding Neural Networks with TenorFlow
  • Deep dive into Neural Network with TenorFlow
  • Master Deep networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Restricted Boltzmann Machine (RBM) and autoencoders
  • Keras API
  • TFLearn API
  • In-Class project

As prerequisites, you need to have some basic programming knowledge in Python and concept about machine learning. You will receive a completion certificate after finishing this training.


5. Deep Learning for Business

YONSEI University is offering this Deep Learning tutorial for beginners. This 100% online course take s approximately 8 hours to complete. Professor Jong-Mong Chung is offering this training. It has a rating of 4.3 out of 5 on Coursera.

This course is divided into three parts. The first part focuses on DL and ML technology-based future business strategy including details on new state-of-the-art products/services and open source DL software. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems. The last part is on four TensorFlow Playground projects, where you will gain experience in designing deep learning.

The syllabus for this course includes-

  • Deep Learning Products & Services
  • Business with Deep Learning & Machine Learning
  • Deep Learning Computing Systems & Software
  • Basics of Deep Learning Neural Networks
  • Deep Learning with CNN & RNN
  • Deep Learning Project with TensorFlow Playground

You will have the knowledge of Artificial Intelligence (AI), Artificial Neural Network, Machine Learning, and Deep Learning after finishing this Deep Learning certification course


6. Complete Guide to TensorFlow for Deep Learning with Python

Data scientist Jose Portilla is offering this Deep Learning tutorial for beginners. This online course will teach you to use Deep Learning framework in TenorFlow with Python. It has a rating of 4.4 out of 5 and over 52,000 enrolled students.

This tutorial will provide you with 14 hours on-demand video, 7 articles, and 5 downloadable resources, along with lifetime access. With these materials, you will learn to build your own Neural network from scratch. You will also learn to use TenorFlow for classification, regression task, image classification, time series analysis, solving unsupervised learning problems, create Generative Adversarial Networks and so on. You will get to know how to conduct Reinforcement Learning with OpenAI Gym.

This online course is a complete guide for you to know how to use Google’s TensorFlow framework to create artificial neural networks for Deep Learning. It is designed to balance theory and practical implementation. Moreover, you will get a complete Jupiter notebook guide of code and easy to reference slides and notes.

It has several exercises throughout the tutorial for more practice. TenorFlow is highly used among the major companies around the world. This online course will make you a master of TenorFlow in Deep Learning.


7. Data Science: Deep Learning in Python

Lazy Programmer Inc. is offering this Deep Learning tutorial on Udemy. You will learn neural network theory in-depth and code using Python and TenorFlow. It has a rating of 4.6 out of 5 and over 32,000 enrolled students.

With this online course, you will get your hands on 9.5 hours on-demand video with lifetime access. This deep learning tutorial will teach you everything about the neural network. Moreover, you will be building a neural network of your own in Python and Numphy. You will have a complete understanding of different types of the neural networks and the problems they solve.

You need to install Numphy AND Python for this online course. But don’t worry about that. This tutorial will teach you how to install TenorFlow. You need to have some knowledge of calculus, linear algebra, probability, Python coding, and Nymph coding.

The whole tutorial focuses on “how to build and understand” not just ” how to use”. You will be learning from experience that you will get throughout the training. You will be able to have a solid grasp on advanced topics like Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and many more.

8. Modern Deep Learning in Python

Lazy Programmer Inc. strikes again with a new Deep Learning tutorial. Here, you will learn about modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS. It has over 18,000 enrolled students. and a rating of 4.7 out of 5.

Here, you will find 9 hours on-demand video with lifetime access. This tutorial will teach you to apply momentum to backpropagation and adaptive learning rate procedures to train the neural network. You will learn about different libraries and will apply those in writing neural network.

This is a more advanced level course for those who are already comfortable with different programming languages.

Here, you will learn about batch and stochastic gradient descent,
momentum, adaptive learning rate techniques like AdaGrad, RMSprop, and Adam, and many other modern techniques. The instructor will get deep into TenorFlow in this training.

So, if you want to learn Deep Learning, then this course will take you in-depth of it and help you to understand it to the core of it.

9. Natural Language Processing with Deep Learning in Python

Lazy Programmer is here again with another advanced level course on Deep Learning. It has a rating of 4.6 out of 5 and over 21,500 enrolled students. You will find a complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis in this Deep Learning tutorial.

This online course will provide you with 13 hours on-demand video with lifetime access. These materials will give you with the knowledge of understanding and implementing Recursive Neural Tensor Networks and Recursive Neural Networks for sentiment analysis.

For this advanced course, you need to install and understand Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow. You will also need to know how to code a recurrent neural network from basic primitives in Theano.

This tutoreial will go through word2vec and Glove thoroughly. You will also get to look into NLP problems. Lastly, you will learn about recursive neural networks, which will finally help us to solve the problem of negation in sentiment analysis.

If you want more than just a superficial look at machine learning models, this course is for you.

10. Applied AI with DeepLearning

IBM is offering this deep learning tutorial on Coursera. It is an advanced level course which will take approximately 20 hours to complete. A group of experts will instruct this 100% online course.

This course begins with an introduction to the fundamentals of Linear Algebra and Neural Networks. You will also learn about different Deep Learning networks. Keras and TenorFlow take a big portion of this tutorial you will know about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models.

The syllabus for this Deep Learning tutorial covers-

  • Introduction to Deep Learning
  • Deep Learning frameworks
  • Deep Learning Applications
  • Scaling and deployment

With this tutoria,l you will have the skillds for Machine LearningDeep LearningLong Short-Term Memory (ISTM)Apache Spark. This online course will help you to properly use your learned skills.

11. An Introduction to Practical Deep Learning

A group of Intel engineers is offering this Deep Learning tutorial on coursera. It is an intermediate level course that has a rating of 4.5 out of 5. This online course will take approximatly12 hours to complete.

With this course, you will learn to explore important concepts in Deep Learning, to train deep networks using Intel Nervana Neon, to apply Deep Learning to various applications, and to explore new and emerging Deep Learning topics.

The syllabus for this tutorial covers-

  • Introduction to Deep Learning and Deep Learning Basics
  • Convolutional Neural Networks (CNN), Fine-Tuning and Detection
  • Recurrent Neural Networks (RNN)
  • Training Tips and Multinode Distributed Training
  • Hot Research and Intel’s Roadmap
  • Final Quiz

With the knowledge you gain from this Deep Learning tutorial, you will be able to understand the machine language of the machine and control them.

12. Deep Learning Prerequisites: Logistic Regression in Python

This is another course on Deep Learning that is presented by Lazy Programmer Inc. This course is for professionals and students. You will get to know about the theory behind logistic regression and code in Python. This online tutorial has a rating of 4.6 out of 5 and over 18,000 enrolled students.

The whole course comes with 6 hours on-demand video, with lifetime access. You will learn everything about logistic regression in this online course. This tutorial will teach you all to use Python in coding.

This Deep Learning tutorial is a lead-in to Deep Learning and neural networks. From the derivation of the solution to application problems, this tutorial will lead you from the ground up. For this training course, you just need to have Python and access to some Python libraries.

Here, you will find a lot of examples to show you how Deep Learning can be used. Throughout the whole course. you will be doing a project that will walk you through the real-world applications of Deep Learning.

This project will teach you how to predict user actions on a website given user data, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. You will also find another project in the end.

This Deep Learning certification will enhance your coding ability by teaching you data science.

13. Deep Learning Prerequisites: Linear Regression in Python

This Deep Learning tutorial will teach you linear regression. You will be able to build your own program in Python for data analysis. Lazy Programmer Inc is presenting this online tutorial to you on Udemy. It has a rating of 4.6 out of 5. It also has over 18,000 enrolled students.

Here, you will find 5.5 hours on-demand video, with lifetime access. This tutorial will teach you to derive and solve a linear regression problem. Then you will learn to apply linear regression to solve data science problem. Also, you will be building your own version of a linear regression model in Python.

This is a great introductory course for those who want to get into the field of Deep Learning, machine learning, data science, and statistics. In the beginning, the instructor will apply linear regression in Moor’s law. Then he will teach you how to create a machine learning model that can learn from multiple inputs. In the end, you will be discussing some practical machine learning issues that are useful while performing data analysis.

For this Deep Learning course, you need to know about the basic Python programming, the Gaussian distribution, probability and how to take a derivative using calculus.

14. Deep Learning: Convolutional Neural Networks in Python

This deep learning tutorial is presenting computer vision, data science and machine learning in Theano and TenorFlow. Lazy Programmer Inc is instructing this course. It has a rating of 4.7 out of 5 and over 16,000 enrolled students.

Here, you will be provided with 7.5 hours on-demand video, with life time access. With this online training, you will have a complete understanding of convolution. You will understand how it can be applied in audio effect, image effects, image classification, and many other things.

You will also be implementing Gaussian blur and edge detection, a simple echo effect in code. This certification will teach you how to implement a convolutional neural network in Theano and TenorFlow. Also, this tutorial will explain you the architecture of a Convolutional Neural Network (CNN).

This deep learning tutorial will teach you how to use deep learning for computer vision using convolutional neural networks. You will go in-depth of convolution here. And able to learn to build convolution filters for various applications on audio and image. Besides you will get a full view of the architecture of the convolution neural network.

Then the instructor will take you straight into coding. You will learn to extend deep neural networks. Moreover, this Deep Learning tutorial will teach you to turn the deep neural networks into CNN. Then, it will show you how convolutional neural networks, written in both Theano and TensorFlow, can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

You can install Python, Numpy, Scipy, Theano, and TensorFlow for free and use those applications for this training. This certification course focuses on “how to build and understand”, not just “how to use”. You will be teaching yourself through experimentations.

15. Zero to Deep Learning™ with Python and Keras

Data scientist Jose Portilla and Francesco Mosconi from Data Weekends are offering this Deep Learning tutorial for programming enthusiasts. You will learn to use Python and Keras to understand and build Deep Learning models for images, texts and more. This online course has a rating of 4.3 out of 5 and over 13,000 enrolled studetns.

In this tutorial, you will get 10 hours on-demand video and 6 articles, along with lifetime access. With these materials, you will literally learn A to Z about deep learning. You will Deep Learning to build predictive models, to supervised and unsupervised problems, to build and run convolutional and recurrent neural networks, and many other things. You will learn to use Python and Keras for building Deep Learning models.

This Deep Learning tutorial is designed for beginner and intermediate programmers and data scientists who already know Python. You will not have any problem to understand this tutorial.

The instructor of this course will start teaching you with a good review of Deep Learning and a recap of machine learning tools and techniques. You will get introduced to Artificial Intelligence techniques. Then, you will learn about several architecture including fully connected and running Convolutional and Recurrent Neural Networks.

The whole tutorial maintains a good balance between theory and practice. It even explains simple maths to you. You will receive a lot of exercise and sample code throughout the training. Overall, you will be able to design and train a variety of Neural Network models and use cloud computing to speed up training and improve your model’s performance.

So, if you are a software engineer or a data scientist who want to acquire a strong foundation on Deep Leaning, then you should enroll in this online course.

16. Unsupervised Deep Learning in Python

This Deep Learning tutorial will teach you Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA with Theano and TenorFlow. Data scientist lazy programmer Inc is presenting this course to you on Udemy. It has a rating of 4.6 out of 5 and over 12000 enrolled students.

In this online tutorial, you will have 10.5 hours on-demand video and full lifetime access to all of the materials. This tutorial will teach you about principal components analysis PCA. Here you will learn to derive the PCA algorithm, write the code for PCA, and understand the limitations of PCA and t-SNE. Also, learn to write an autoencoder in Theano and TensorFlow.

This online training will make you understand how stacked autoencoder are used in deep learning, the theory behind restricted Boltzmann machines, the contrastive divergence algorithm. You will be able to
write your own RBM and deep belief network in Theano and TenorFlow.

The instructor of this course already has many other courses on Deep learning data science and machine learning. In this specific unsupervised learning, he will teach you about clustering and density estimation.
This course starts with a very basic study of principal components analysis PCA and t-SNE. Then you will get into a special type of unsupervised neural network called autoencoder. It will lead you to a supervised deep neural network.

After that, you will get your hands on Restricted Boltzmann Machines (RBMs). It will show you an interesting way of training Restricted Boltzmann Machines known as Gibbs sampling, a special case of Markov Chain Monte Carlo, contrastive divergence, and many other things. Finally, the instructor will teach you to bring all the concepts together.

For a better understanding of this course you need to know calculus and linear algebra, have Python coding skills, and some experience with Numpy Theano and TenorFlow. All the materials used in this online tutorial can be downloaded and installed for free.

Like his any other courses, lazy programmer Inc focuses on “how to build and understand”, not just “how to use”.

There you go. these are the Best Deep Learning Tutorial, Online Course, and Certification for you. All these tutorials will provide you lifetime access to all of their online materials. That gives you the chance to go through your enrolled course as many time as you wish. You will also receive a completion certificate from these Deep Learning certification tutorial.

Now all you have to do is to go through the listed online courses above and chose one for yourself. You will find Deep Learning tutorial for beginners as well as for advanced learners. So, go and find a suitable Deep Learning course and get enrolled. You will be able to harness the skills of Deep Learning in no time that will enrich your resume.

 

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