A study by the McKinsey Global Institute suggests that artificial intelligence could contribute an additional global economic activity of around $13 trillion by 2030, or about 16% higher cumulative GDP compared with today. Digital assistants like Google Assistant or Mycroft on Android smartphones, Siri on iPhones and iPads, and smart speakers like the Amazon Echo and Google Home are already using AI and its subfields, including deep learning for natural language processing and speech recognition, paving high demands for deep learning courses. 

An extension of machine learning, deep learning is all about neural networks, which imitate the way humans think and learn. The presence of trained deep learning experts is expected to become a crucial requirement for organizations that wish to drive innovation and remain competitive. 

However, it is surprising to learn that this emerging field of deep learning and artificial intelligence is still understaffed as per the McKinsey Global Institute, and neither data scientists nor software engineers could fill in the gaps because of the absence of specific skill sets.

To help you with sharpening your skills on the fundamentals as well as the advanced concepts of deep learning, we have listed some free as well as auditable deep learning courses. Take your pick from the deep learning courses as per your professional needs.

 

  1. Deep Learning for Business by Yonsei University on Coursera

Course Description

With this course, you can build business strategies and plan around DL and ML services and products. The course is divided into three parts-

  1. DL and ML technology based future business strategy 
  2. Core technologies of DL and ML systems, which include Neural Networks, Convolutional NN, and Recurrent NN
  3. Four TensorFlow Playground projects, for gaining experience on designing DL NNs

Course Details

Duration – 6 Hours

Skill Level – Beginner

Course Contents

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

 

2. Computational Neuroscience by the University of Washington on Coursera

Course Description

One of the most popular deep learning courses, Computational Neuroscience offers an introduction to basic computational methods for understanding the functioning of the human nervous system. It explores the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. It uses Matlab/Octave/Python demonstrations and exercises to offer a deeper understanding of concepts and methods.

Course Details

Duration – 26 Hours

Skill Level – Beginner

Course Contents

  • Introduction & Basic Neurobiology
  • What do Neurons Encode? Neural Encoding Models
  • Extracting Information from Neurons: Neural Decoding
  • Information Theory & Neural Coding
  • Computing in Carbon
  • Computing with Networks
  • Networks that Learn: Plasticity in the Brain & Learning
  • Learning from Supervision and Rewards

 

3. Neural Networks and Deep Learning by Coursera

Course Description

The course is a part of the Deep Learning Specialization and explores the foundations of deep learning.

Course Details

Duration – 20 Hours

Skill Level – Intermediate

Course Contents

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

 

4. Deep Neural Networks with PyTorch by IBM on Coursera

Course Description

You will learn how to develop deep learning models using Pytorch. It covers topics like Pytorch’s tensors, automatic differentiation packages, linear regression, logistic/softmax regression, feedforward deep neural networks, convolutional neural networks and other deep learning methods.

Course Details

Duration – 33 Hours

Skill Level – Intermediate

Course Contents

  • Tensor and Datasets
  • Linear Regression
  • Linear Regression PyTorch Waye
  • Multiple Input Output Linear Regression
  • Logistic Regression for Classification
  • Softmax Regression
  • Shallow Neural Networks
  • Deep Networks
  • Convolutional Neural Network

 

5. Intro to TensorFlow for Deep Learning via Udacity

Course Description

This course is a practical approach to deep learning for software developers to help create their own AI applications. With this course, you will get hands-on experience building state-of-the-art image classifiers and other deep learning models, use TensorFlow models in the real world, and use advanced techniques and algorithms to work with large datasets.  

Course Details

Duration – 2 Months

Skill Level – Intermediate

Course Contents

  • Introduction to Machine Learning
  • Your First Model: Fashion MNIST
  • Introduction to Convolutional Neural Networks (“CNNs”)
  • Going Further with CNNs
  • Transfer Learning
  • Saving and Loading Models
  • Time Series Forecasting
  • Introduction to TensorFlow Lite

 

6. Deep Learning with Tensorflow by IBM on edx

Course Description

You will learn how to apply Deep Learning with TensorFlow to unstructured data for solving real-world problems. This course will also cover the basic concepts of TensorFlow, its main functions, operations and the execution pipeline.

Course Details

Duration – 5 Weeks

Skill Level – Intermediate

Course Contents

Module 1 – Introduction to TensorFlow

Module 2 – Convolutional Neural Networks (CNN)

Module 3 – Recurrent Neural Networks (RNN)

Module 4 – Restricted Boltzmann Machine

Module 5 – Autoencoders

 

7. Deep Learning with Python and PyTorch by IBM on edx

Course Description

The course will help you to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorchDeep Learning library for building Neural Networks and Deep Learning models.

Course Details

Duration – 6 Weeks

Skill Level – Intermediate

Course Contents

Module 1 – Introduction to Pytorch

Module 2 – Linear Regression

Module 3 – Classification

Module 4 – Neural Networks

Module 5 – Deep Networks

Module 6 – Computer Vision Networks

 

8. Deep Learning in Computer Vision by National Research University Higher School of Economics on Coursera

Course Description

This course is part of the Advanced Machine Learning Specialization. It will cover topics like computer vision, modern deep learning models, image recognition, video recognition, motion estimation, human action recognition, new image generation, among others.

Course Details

Duration – 17 Hours

Skill Level – Advanced

Course Contents

  • Introduction to image processing and computer vision
  • Convolutional features for visual recognition
  • Object detection
  • Object tracking and action recognition
  • Image segmentation and synthesis

 

9. Applied AI with DeepLearning by IBM on Coursera

Course Description

The course will help you to understand different aspects of deep learning and models, and their usage by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

Course Details

Duration – 22 Hours

Skill Level – Advanced

Course Contents

Week 1 – Introduction to deep learning

Week 2 – DeepLearning Frameworks

Week 3 – DeepLearning Applications

Week 4 – Scaling and Deployment

 

10. Introduction to Deep Learning by National Research University Higher School of Economics on Coursera

Course Description

Introduction to Deep Learning is a part of the Advanced Machine Learning Specialization. It offers a basic understanding of modern neural networks and their applications in computer vision and natural language processing.

Course Prerequisites 

  • Basic knowledge of Python
  • Basic linear algebra and probability

Course Details

Duration – 32 Hours

Skill Level – Advanced

Course Contents

  • Introduction to optimization
  • Introduction to neural networks
  • Deep Learning for images
  • Unsupervised representation learning
  • Deep learning for sequences
  • Final Project

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