Learn Deep Learning Computer Vision
This course is based on Deep Learning Computer Vision. You will get to learn all the state of the art techniques and Advance Computer Vision Algorithms like CNN, OpenCV, YOLO, SSD & GANs by Rajeev D. Ratan.
This course will teach you how to build a deep learning computer vision pipeline. It covers CNN, OpenCV and YOLO. You will also learn about GANs (Generative Adversarial Networks). The course covers never-before-seen practical examples of building such a pipeline
This course teaches you the fundamentals of deep learning for Computer Vision. You will learn about Convolutional Neural Networks (CNN), Stochastic Depth and Spatial Generative Adversarial Networks (SSD and GANs) using Python libraries like OpenCV, YOLO and TensorFlow.
This is one of the top courses on Udemy and is currently ranked as the #1 course in the Data Science category and is the most popular course in the Deep Learning Computer Vision category, with over 3500 reviews and 20,000 students.
This course is an introduction to Deep Learning Computer Vision for beginners. The course starts with some basic fundamentals of machine learning, neural networks and deep learning. Thereafter you will be introduced to Convolutional Neural Networks and its architecture along with training issues in CNNs like vanishing gradients & overfitting. You will then learn to train a few layers of CNN in your own home PC or laptop. After that you get introduced to one of the most popular convolutional neural networks which is YOLO (You Only Look Once). This model can be used for object detection in images. We then move towards domain adaptation i.e., how we can apply same techniques on different domains? This is achieved using transfer learning which was never taught in conventional universities until recently but nowadays becoming more and more popular because it enables us to apply what we learned from one domain on another similar but not exactly same domains e.g., Computer Vision on Handwriting Recognition, Dashboards etc.
Unsupervised and supervised deep learning models for computer vision applications. Learn about the following topics: Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Support Vector Machines and more. Understand the core concepts of deep learning, their applications in real-world scenarios and build your own neural networks with hands-on examples.
This course is a comprehensive Deep Learning Computer Vision™ course that takes you from the basics of Deep Learning and Convolutional Neural Networks (CNN) to state-of-the-art topics like YOLO, SSD and GANs. You will also learn how to apply them in real time on video with OpenCV and deep learning frameworks like TensorFlow, Keras and PyTorch.
Enroll in this course to learn about the different Deep Learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Auto Encoders and Generative Adversarial Networks. You will also get hands-on experience with OpenCV, You Only Look Once Algorithm (YOLO), Single Shot Detector (SSD) and Generative Adversarial Networks (GANs).
Learn how to program a Computer Vision system in Python with this comprehensive course. Beginning with basic concepts and terminology, you will create an image classification system using OpenCV, then move on to building a face detection and recognition system using Haar Cascade features. You will also learn about Binary Robust Independent Elementary Features before getting hands-on experience learning Deep Learning with Neural Networks. Finally this course covers object localization and segmentation with both Convolutional Neural Networks (Canny edge detector) and Selective Search as well as Object Detection using YOLOv3, SSD and GANs
AI with Python & TensorFlow
Learn Deep Learning Computer Vision and AI with Python & TensorFlow. This course is well suited for anyone who has prior knowledge of machine learning, data science and deep learning. You will learn about the most popular Deep Learning approaches for Computer Vision using Keras and TensorFlow, specially Convolutional Neural Networks(CNN), Recurrent Neural Networks(RNN) & Long Short Term Memory(LSTM). You will also learn about Automatic Scene Detection using YOLO, SSD and R-CNN based object detection models.
With deep learning, you can solve computer vision problems such as object detection, pedestrian detection, and face detection. This course is designed to give you a comprehensive overview of the most popular techniques in computer vision.
This course will provide you with a good understanding of computer vision and machine learning, using the most popular libraries like Tensorflow, Keras, Theano and Caffe (CUDA and CPU). Each topic is illustrated with the help of examples using code snippets in Python.
Learn to implement Deep Learning techniques for Computer Vision. The course will teach you about Convolutional Neural Networks and how to apply it on Computer Vision problems using OpenCV, YOLO, SSD & GANs.
Take the knowledge of Deep Learning Computer Vision to next level through this course! This course is designed by an expert programmer who used machine learning and AI expertise in his day to day work. Get into the field of Machine Vision with this course that includes most amazing and hot topics of Computer Vision like Convolutional Neural Networks, OpenCV, Python Programming YOLO (You Only Look Once) Algorithm and its applications. Discover the field of Deep Learning CNN as it was never before with practical examples in code!
Python & OpenCV
This course is the only online course that covers all aspects of deep learning computer vision. It uses Python, OpenCV and a bunch of other libraries. In this course you will learn about Convolutional Neural Networks and how to implement them. You will also learn about Recurrent Neural Networks and LSTM Networks. Finally you will also see how to use Generative Adversarial Networks for generating images of your choice with appropriate statistics (like human faces).
Leverage on deep learning computer vision to build your own face recognition and object detection for security. This course will help you master different Deep Learning architectures that you can use for any real-time applications, including self driving cars, autonomous robots, medical image processing and much more.
This course is designed to give you a complete end to end understanding of how to use Deep Learning, CNN and OpenCV libraries in computer vision. This course will cover the basics of Convolution Neural Networks (CNNs), Deep Belief networks, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). You’ll also get brief introduction to generative adversarial networks (GANs).
This is a comprehensive course that will help you design, implement and train your own deep learning models for Computer Vision applications using Keras and TensorFlow frameworks along with Python programming language. If you are interested to learn more about Deep Learning concepts, CNNs, RNNs, Neural Networks and Restricted Boltzmann Machines (RBM) along with their usage in real world projects like Image Classification, Object Detection and more from scratch then enroll into this course
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