The editors at Solutions Review have compiled this list of the best deep learning courses on Coursera to consider if you’re looking to grow your skills.
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. Based on artificial neural networks and representation learning, deep learning can be supervised, semi-supervised or unsupervised. Deep learning models are commonly based on convolutional neural networks but can also include propositional f formulas or latent variables organized by layer.
With this in mind, we’ve compiled this list of the best deep learning courses on Coursera if you’re looking to grow your skills for work or play. Coursera is one of the top online education platforms in the world, partnering with more than 200 universities and companies to provide a range of learning opportunities. The platform touts more than 77 million learners around the globe. As you can see below, we broke the best deep learning courses on Coursera down into categories based on the recommended proficiency level. Each section also features our inclusion criteria. Click GO TO TRAINING to learn more and register.
The Best Deep Learning Courses on Coursera
Description: In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture, and apply deep learning to your own applications.
Description: 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.
Description: This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
Description: This course introduces you to two of the most sought-after disciplines in machine learning: deep learning and reinforcement learning. Deep learning is a subset of machine learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind neural networks, which are the basis of deep learning, as well as several modern architectures of deep learning.
Description: The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
Description: This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate. Students will learn about the fundamentals of linear algebra and neural networks. Then the instructors introduce the most popular DeepLearning frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow make up the greatest portion of this course.
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