One of the Best Online Convolutional Neural Network Courses is The Deep Learning Specialization. It is a fundamental curriculum that will equip you to participate in the creation of cutting-edge AI technology by helping you grasp the capabilities, problems, and repercussions of deep learning. You'll learn how to create and train neural network architectures including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers, as well as how to improve them with tactics like Dropout, BatchNorm, Xavier/He initialization, and more in this Specialization.

Prepare to use Python and TensorFlow to learn theoretical topics and their commercial applications, and to handle real-world problems like speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. You'll also get career guidance from deep learning specialists from business and academia along the way. You'll be able to construct and train deep neural networks, implement vectorized neural networks, discover architecture parameters, and apply deep learning to your applications at the conclusion of the course.

For constructing DL applications, you may utilize best practices to train and generate test sets and examine bias/variance, as well as typical NN methodologies, optimization algorithms, and neural network implementation in TensorFlow.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 5 months to complete

• Subtitles: English, Chinese (Traditional), Arabic, French, Ukrainian, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, Turkish, Spanish, Japanese

Coursera Rating: 4.9/5
Enroll here: https://www.coursera.org/specializations/deep-learning

Getting started with TensorFlow 2 is among the Best Online Convolutional Neural Network Courses. You'll learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating, and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models in this course. In practical, hands-on coding lessons, which you will be guided through by a graduate teaching assistant, you will put what you learn into practice right away.

There's also a series of automatically graded programming projects to help you brush up on your abilities. At the end of the course, you'll complete a Capstone Project in which you'll build an image classifier deep learning model from the ground up, bringing many of the ideas together. Tensorflow is an open source machine library that is one of the most popular deep learning frameworks. Tensorflow 2 is a significant step forward in product development, with an emphasis on simplicity of use for all users, from beginners to experts. This course is designed for those who are new to Tensorflow as well as those who have worked with Tensorflow 1.x.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 26 hours to complete

• Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

Coursera Rating: 4.9/5
Enroll here: https://www.coursera.org/learn/getting-started-with-tensor-flow2

Convolutional Neural Networks is also one of the Best Online Convolutional Neural Network Courses. In the fourth course of the Deep Learning Specialization, you'll learn about the evolution of computer vision and its intriguing applications, like autonomous driving, facial recognition, interpreting radiological pictures, and more.

You'll be able to build a convolutional neural network, including recent variations like residual networks, apply convolutional networks to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to a variety of image, video, and other 2D or 3D data by the end of the course.

The Deep Learning Specialization is a fundamental curriculum that will equip you to participate in the creation of cutting-edge AI technology by helping you grasp the capabilities, problems, and repercussions of deep learning. It paves the road for you to get the information and abilities you'll need to apply machine learning to your work, advance your technical career, and take the first step into the AI world.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 41 hours to complete
• Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish, Japanese

Coursera Rating: 4.9/5
Enroll here: https://www.coursera.org/learn/convolutional-neural-networks

This course will give you a basic understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, and so on), as well as show you how these models can be used to solve complex problems in a variety of industries, ranging from medical diagnostics to image recognition to text prediction.

In addition, we've included practice tasks that will allow you to put these data science models to work on real-world data sets. These practice activities will show you how to utilize PyTorch, an open source library used by prominent tech organizations in the machine learning area, to develop machine learning algorithms (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more). Duke University is offering the course. It boasts around 13,000 undergraduate and graduate students, as well as a world-class faculty dedicated to pushing knowledge forward. The institution is dedicated to putting knowledge to good use in the community, both on its North Carolina campus and across the world.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 15 hours to complete

• Subtitles: English

Coursera Rating: 4.7/5

Enroll here: https://www.coursera.org/learn/machine-learning-duke

Computer vision (CV) is an exciting topic of research that aims to automate the process of assigning meaning to digital pictures and videos. To put it another way, we're assisting computers in seeing and understanding the world around them! To complete CV tasks, a variety of machine learning (ML) algorithms and techniques can be used, and as ML becomes faster and more efficient, you are able to deploy these techniques to embedded systems.

This course will teach you how to use deep learning with neural networks to classify images and detect objects in images and videos, thanks to a collaboration between Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation. You'll be able to use these machine learning models to deploy them on embedded systems.

This course introduces the ideas and terminology needed to comprehend how convolutional neural networks (CNNs) function, as well as how to utilize them to categorize pictures and recognize objects. You will be able to train your own CNNs and deploy them on a microcontroller and/or single board computer through the hands-on projects.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 31 hours to complete
• Subtitles: English

Coursera Rating: 4.7/5
Enroll here: https://www.coursera.org/learn/computer-vision-with-embedded-machine-learning

You'll learn how to use TensorFlow, a popular open-source machine learning framework, to train a neural network for computer vision applications, as well as how to deal with real-world picture data and avoid overfitting with techniques like augmentation and dropout. You'll discover the skills you'll need to develop scalable AI-powered apps using TensorFlow in this four-course Professional Certificate program.

You'll be able to apply your new TensorFlow abilities to a variety of issues and projects once you've completed this program. This program will assist you in studying for the Google TensorFlow Certificate test and will help you go one step closer to earning the Google TensorFlow Certificate. You'll receive hands-on experience with 16 Python programming assignments in the DeepLearning.AI TensorFlow Developer Professional Certificate program.

By the end of this course, you'll be able to: use TensorFlow to build and train neural networks; use convolutions to improve your network's performance as you train it to recognize real-world images; and use natural language processing systems to teach machines to understand, analyze, and respond to human speech.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 4 months to complete

• Subtitles: English

Coursera Rating: 4.7/5
Enroll here: https://www.coursera.org/professional-certificates/tensorflow-in-practice

As one of the Best Online Convolutional Neural Network Courses, if you're a software engineer looking to create scalable AI-powered algorithms, you'll need to know this course. This course will teach you best practices for utilizing TensorFlow, a prominent open-source machine learning framework. It is part of the forthcoming Machine Learning with Tensorflow Specialization.

Andrew Ng teaches the most essential and foundational aspects of Machine Learning and Deep Learning in his Machine Learning course and Deep Learning Specialization. This new deeplearning.ai TensorFlow Specialization shows you how to put those ideas into practice using TensorFlow, so you can start constructing and deploying scalable models to real-world situations. It is recommended that you take the Deep Learning Specialization to have a better knowledge of how neural networks function.

Python coding experience and high school arithmetic skills are essential. Prior experience of machine learning or deep learning is beneficial but not needed. You'll learn how to use TensorFlow, a popular open-source machine learning framework, to design a simple neural network, train a neural network for a computer vision application, and grasp how to improve your neural network with convolutions.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 19 hours to complete
• Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, English, Spanish, Japanese

Coursera Rating: 4.7/5
Enroll here: https://www.coursera.org/learn/introduction-tensorflow

Are you interested in pursuing a career in Deep Learning? You've come to the right place. One of the Best Online Convolutional Neural Network Courses is Introduction to Deep Learning & Neural Networks with Keras. This course will introduce you to the topic of deep learning and will assist you in answering many common questions, such as what is deep learning and how do deep learning models compare to artificial neural networks. You'll learn about several deep learning models and use the Keras library to create your own deep learning model. Learners will be able to define what a neural network is, what a deep learning model is, and how they vary after finishing this course.

You may also show that you know how to use unsupervised deep learning models like autoencoders and limited Boltzmann machines. You may also learn how to use supervised deep learning models like convolutional neural networks and recurrent networks to demonstrate your knowledge. Finally, this might assist you in understanding how to use the Keras library to create deep learning models and networks.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 8 hours to complete
• Subtitles: French, Portuguese (European), Russian, English, Spanish

Coursera Rating: 4.7/5
Enroll here: https://www.coursera.org/learn/introduction-to-deep-learning-with-keras

This guided project course is part of the "Tensorflow for AI" series, which builds on the first course in the DeepLearning series. The AI TensorFlow Developer Professional Certificate will help learners improve their abilities and create more Tensorflow projects.

In this 1.5-hour project-based course, you'll learn about convolutions, how to apply filters to images, how to apply pooling layers, and how to practice convolution and pooling techniques on real images. You will receive a bonus deep learning project developed with Tensorflow at the conclusion of the project. You will have learnt how convolutions operate and how to design convolutional layers by the conclusion of this project, which will help you prepare for your own deep learning projects utilizing convolutional neural networks.

This class is for students who want to learn how to use Python to build convolutional neural networks with TensorFlow, as well as students who are currently enrolled in or have completed a basic deep learning course and are looking for a knowledge-based course on convolutions in images with TensorFlow. Additionally, by including this project in their portfolios, learners gain necessary information about developing convolutional neural networks and enhance their abilities in applying filters to pictures, which aids them in achieving their professional objectives.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Intermediate Level
• Approx. 1.5 hours to complete
• Subtitles: English

Coursera Rating: 4.6/5
Enroll here: https://www.coursera.org/projects/tensorflow-for-ai-applying-image-convolution

The course will show you how to use Pytorch to create deep learning models. The lecture will begin with the use of Pytorch's tensors and the Automatic Differentiation package. Then, in each session, you'll learn about different models, starting with the basics like linear regression and logistic/softmax regression. The role of different activation functions, normalization, and dropout layers are followed by Feedforward deep neural networks.

Then there will be a discussion of Convolutional Neural Networks and Transfer Learning. Finally, a variety of different Deep Learning techniques will be discussed. Learners will be able to describe and apply their understanding of Deep Neural Networks and associated machine learning methods after finishing this course. They will also be able to use Python libraries such as PyTorch to create Deep Neural Networks.

After completing this course, you will be able to comprehend the theory and understanding underlying Deep Neural Networks, Residual Nets, and Convolutional Neural Networks (CNNs); and build a deep learning model using Keras and Tensorflow 2.0 as a backend.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion• 100% online
• Intermediate Level
• Approx. 31 hours to complete
• Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Coursera Rating: 4.4/5
Enroll here: https://www.coursera.org/learn/deep-neural-networks-with-pytorch#about