Before we start, you may be asking yourself, “What is deep learning?”
“Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”
Top 7 Best Online courses to Learn Deep Learning
1.Deep Learning Prerequisites: Linear Regression in Python
This course teaches you about one popular technique used in machine learning, data science, and statistics: linear regression. They cover the theory from the ground up: derivation of the solution, and applications to real-world problems. They show you how one might code their own linear regression module in Python.
Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you’ll be returning to it for years to come. That’s why it’s a great introductory course if you’re interested in taking your first steps in the fields of:
2.Ensemble Machine Learning in Python: Random Forest, AdaBoost
deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.
They do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.
3. Deep Learning Prerequisites: Logistic Regression in Python
This course is a lead-in to deep learning and neural networks – it covers a popular and fundamental technique used in machine learning, data science, and statistics: logistic regression. They cover the theory from the ground up: derivation of the solution, and applications to real-world problems. They show you how one might code their own logistic regression module in Python.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything.
4. Data Science: Deep Learning in Python – Udemy
This 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.
They 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. They show you how to code backpropagation in Numpy, first “the slow way”, and then “the fast way” using Numpy features.
They 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. They go beyond basic models like logistic regression and linear regression and show you something that automatically learns features.
5.Deep Learning: Convolutional Neural Networks in Python
This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You’ve already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU.
This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.
Here it uses larger color images at various angles – so things are going to get tougher both computationally and in terms of the difficulty of the classification task.
6.Data Science: Practical Deep Learning in Theano + TensorFlow
This course continues where 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 (and because of that, this course contains basically NO MATH), 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 and RMSprop which can also help speed up your training.
7. Deep Learning Nanodegree Foundation
This course provides a dynamic introduction to this amazing field, using weekly videos, exclusive projects, and expert feedback and review to teach you the foundations of this future-shaping technology.
Additionally I have included 3 Free videos.
- Deep Learning Lecturers: This is one of my favourite instructor “Nando de Freitas“. It includes 16 series.
2.) Videos from Deep Learning Summer School, Montreal 2015. These videos covers advanced topics in Deep Learning.
3.) Deep Learning with Python
What do you think about these 7 Online courses. Add comments below.