10 Best Advanced Deep Learning Courses ( 2018 Updated )

Deep Learning and Computer Vision A-Z™ OpenCV, SSD & GANs

 

 

There are number of courses / certifications available to self-start your career in Deep Learning. These courses are given in online or offline. The main trouble students face is to choose the best out of these courses.

 

How to choose the right online course for you?

 

Choosing the right course is always a difficult task for any individual.

 

So I have selected all these Courses based on some basic Criteria:

  • Course Content/Description
  • Course Quality,
  • Cost,
  • Knowledgeable and Professional Trainers
  • Career Opportunities,
  • Highest Reviewed,
  • Best Sellers,
  • Highest Rated,
  • Newest etc

 

For most of these Courses you will Get :

1.) Full lifetime access

2.) Access on mobile and TV

3.) Certificate of Completion

4.) 30-Day Money-Back Guarantee

 

With the introduction of few newly introduced courses, it has turned out to be considerably more hard to make a right decision. The dread of putting in unworthy courses keeps to remain the greatest obstacle for students.

Below are the list of Top 10 Best Advanced Deep Learning Courses available as of now!

 

Top 10 Best Advanced Deep Learning Courses

 

 

1.) Deep Learning in Computer Vision

 

Deep Learning in Computer Vision

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.

The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. It covers both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.

 

Course Rating : 9.6 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

2.) Applied AI with DeepLearning

Applied AI with DeepLearning

This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

This course talks about about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, it covers how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

 

Course Rating : 9.6 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

3.) Master Computer Vision™ OpenCV3 in Python & Machine Learning

 

Master Computer Vision™ OpenCV3 in Python & Machine Learning

In this course you will Learn Computer Vision using OpenCV in Python – using the latest 2018 concepts and implement12 awesome projects! 

Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of the technology, from billion dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.

Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face & object recognition, image searching and especially in Self-Driving Cars!

As a result, the demand for computer vision expertise is growing exponentially!

 

Course Rating : 9.6 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

3.) Advanced Deep Learning with Keras

 

Advanced Deep Learning with Keras

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible.

This course introduce neural networks and the optimization techniques to train them. We’ll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, it present two types of neural architecture: convolutional and recurrent neural networks.

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

4.) Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

 

Deep Learning and Computer Vision A-Z™ OpenCV, SSD & GANs

Computer Vision is applied everywhere. From health to retail to entertainment – the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially.

Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?

With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice!

 

Course Rating : 9.6 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

5.) Deep Learning: Recurrent Neural Networks in Python

 

Deep Learning - Recurrent Neural Networks in Python

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.

All of the materials required for this course can be downloaded and installed for FREE.

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

5.) Natural Language Processing with Deep Learning in Python

 

Natural Language Processing with Deep-Learning in Python

 

In this course you will see 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.

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

6.) Deep Learning: Advanced NLP and RNNs

 

Deep Learning - Advanced NLP and RNNs

 

In this course you will be able to build applications for problems like:

  • text classification (examples are sentiment analysis and spam detection)
  • neural machine translation
  • question answering

 

This course take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

7.) Practical Neural Networks & Deep Learning in R

 

Practical Neural Networks & Deep Learning in R

In this course you will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and recurrent neural networks (RNN).

You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications.  

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

7.) Deep Learning: Advanced Computer Vision

 

Deep Learning - Advanced Computer Vision

In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.

You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)

We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.

Another very popular computer vision task that makes use of CNNs is called neural style transfer.

 

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

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

 

Improving Deep Neural Networks - Hyperparameter tuning, Regularization and Optimization

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 will also learn TensorFlow.

you will: – Understand industry best-practices for building deep learning applications. – Be able to effectively use the common neural network “tricks“, including initialization, L2 and dropout regularization, Batch normalization, gradient checking, – Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. – Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance – Be able to implement a neural network in TensorFlow..

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

9.) An Introduction to Practical Deep Learning

 

An Introduction to Practical Deep Learning

This course provides an introduction to Deep Learning, a field that aims to harness the enormous amounts of data that we are surrounded by with artificial neural networks, allowing for the development of self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading.

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

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

10.) Introduction to Deep Learning

 

Introduction to Deep Learning

 

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models.

 

Course Rating : 9.5 / 10

 

Course  Link :CLICK HERE for the Special Offer

 

 

FINAL WORDS

 

Since every one of these courses can be done on the web, you have the advantage of carrying on gaining from pretty much anyplace on the planet.

 

PS: Start taking these Best courses listed above. Perhaps, a few dollars course can change your Career for eternity. Invest Now and Reap Rewards later with Compounding.

 

Good Luck!

 

 

 

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