Top 16 Best Online Deep Learning Courses to learn in 2023
What is Deep learning?
The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.
Hera re the some of the best deep learning courses you must learn in 2023 to boost your career in AI.
1. Deep Learning Nano degree (Udacity)
Who can take this course: This deep learning certification is best for students who have a basic working knowledge of Python programming. However, the course starts off with relatively simple lessons, so it’s certainly possible to learn programming hand-in-hand with this course. Prior knowledge in deep learning is not required.
Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.
2. Deep Learning Specialization (Coursera)
Who can learn this course: This deep learning certification program from Coursera is ideal for students who know basic Python programming and algebra. Prior knowledge in deep learning is considered beneficial, but not compulsory.
What you’ll learn: This deep learning course covers various topics in the field of A.I and deep learning, such as:
- Neural Networks (Convolutional)
- Hyperparameter tuning, Regularization, and Optimization
- Structuring Deep Learning Projects
- Sequence Modelling (in the context of natural language processing)
This online course was voted the best deep learning course by FloydHub – a hub for all things A.I.
Who can take this course: Anyone who wants to dive into Google’s TensorFlow system stands to benefit the most from this course. The course content is introductory in nature, so prior knowledge in programming is not compulsory (although it will be beneficial).
Who can learn this course: Students interested in getting into the thick of coding their own deep learning algorithms should take this course. Alternatively, those looking for a program that teaches deep learning training with PyTorch and TensorFlow will find lots to learn from this course. The material is relatively basic in nature, so this course could be considered beginner-friendly.
Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.
6. Deep Learning, by 3Blue1Brown (YouTube)
Who can learn this course: This deep learning course is unlike all others on this list. It’s very easy to follow, it does not require any prerequisite knowledge, and it’s suitable for absolutely anyone interested in deep learning and neural networks.
Who can take this course: Data engineers looking to gain some experience with deep learning are the ideal candidates for this course. The course requires you to have prior knowledge of the basics of deep learning algorithms alongside experience with Hidden Markov models.
8. Deep Learning with Keras (Pluralsight)
Who can learn this course: This deep learning course is basic in nature, but it’s still best suited for students who have some prior skills in programming (mainly Python).
What you’ll learn: The primary aim of this training program is to teach students how to use the Keras Deep Learning Library. Keras is one of the most useful resources for creating deep learning programs with Python, and this makes Jerry Kurata’s course very valuable for anyone looking to use deep learning with the Python programming language.
Here is the link to join this course — Deep Learning with Keras
9. Introduction to Deep Learning (Coursera)
What you’ll learn: This course teaches students about the basics of neural networks, the kinds of data that you can expect to use them on, and the applications you can create that use these processes. Learn about how your algorithms can generate content from context and generate actionable data from raw input. It also gives a succinct explanation of the role of deep learning in different directions of AI, and shows basic examples of each.
10. Full Stack Deep learning course
There are many great courses to learn how to train deep neural networks. However, training the model is just one part of shipping a deep learning project. This course teaches full-stack production deep learning:
- Formulating the problem and estimating project cost
- Finding, cleaning, labeling, and augmenting data
- Picking the right framework and compute infrastructure
- Troubleshooting training and ensuring reproducibility
- Deploying the model at scale
This course was originally taught as an in-person boot camp in Berkeley from 2018 – 2019. It was also taught as a University of Washington Computer Science PMP course in Spring 2020.
Here is the link to join this course —Full Stack Deep learning course.
11. Deep learning crash Course (Youtube Learning)
Instead of providing them with a comprehensive set of rules, we could show them some examples so that they can understand how the world works. That’s what machine learning does.
In this series, we will learn the fundamentals of machine learning, with a focus on deep learning. We will talk about where to find data, how to build models that can process data, and generate data as well.
12. Practical Deep Learning for Coders by fast.ai
This is Jeremy Howard’s classic course on deep learning. He is another awesome instructor in the field of Deep Learning along with Andrew Ng of Coursera and Kirill Eremenko on Udemy.
The best part of this course I that it’s very well structured and moves step by step, which helps to build complex deep learning and neural network concepts. There is also a book with the same title which you can buy on Amazon.
Here is the link to his book — Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD.
13. Building Advanced Deep Learning and NLP Projects [Educative]
In this course, you’ll not only learn advanced deep learning concepts, but you’ll also practice building some advanced deep learning and Natural Language Processing (NLP) projects. By the end, you will be able to utilize deep learning algorithms that are used at large in industry.
14. Deep Learning Book companion videos
A series of Deep learning Book companion videos by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
You can also find Deep learning lecture slides accompanying all chapters of this book. We currently offer slides for only some chapters. If you are a course instructor and have your own lecture slides that are relevant, feel free to contact us if you would like to have your slides linked or mirrored from this site.
This is another awesome online training course to learn Deep learning. This course provides the MOST in-depth look at neural network theory and how to code one with pure Python and Tensorflow.
If you ever wanted a course that can teach you how to create your own neural network from scratch, then this is the course you should join.
Deep Learning Course with TensorFlow Certification by Edureka is curated with the help of experienced industry professionals as per the latest requirements & demands. This Deep learning certification course will help you master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. In this Deep Learning training, you will be working on various real-time projects like Emotion and Gender Detection, Auto Image Captioning using CNN and LSTM, and many more.