Convolutional Neural Networks with TensorFlow in Python

Advanced neural networks: Master Computer Vision with Convolutional Neural Networks (CNN) and Deep Learning

This course is a fantastic training opportunity to help you gain insights into the rapidly expanding field of Machine Learning and Computer Vision through the use of Convolutional Neural Networks. Convolutional Neural Networks, or CNNs in short, are a subtype of deep neural networks that are extensively used in the field of Computer Vision. These networks specialize in inferring information from spatial-structure data to help computers gain high-level understanding from digital images and videos. That can be as simple a task as classifying an image to be a dog or a cat, but it can also explode in complexity as is the case with self-driving cars, for example. This is where most of the active Machine Learning research is concentrated right now, and CNNs are a crucial part of it. So, it is high time to up your game and master this piece of the Deep Learning puzzle.

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What you’ll learn

• Learn the fundamentals of Convolutional Neural Networks
• Perform Computer Vision and Machine Learning tasks
• Master working with TensorFlow and Tensorboard
• Understand kernels
• Get the hang of convolution and its role in CNNs
• Get familiar with L2 regularization and weight decay
• Grasp the concept of dropout
• Visualize networks and metrics using Tensorboard
• Approach multilabel classification
• Gain experience from a big real-world practical example
• Convert Images into Tensors
• Explore the concepts behind popular state-of-the-art CNN architectures

You May Also Need This Course: PyTorch for Deep Learning and Computer Vision

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