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[Udemy] Deep Learning for Computer Vision

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100% OFF Get Course

What you’ll learn

  • Primary and Superior Computer Vision
  • Synthetic Neural Community
  • Keras Instruments, Keras API Help
  • Picture Processing, CNN

Requirements

  • Python

Description

Computer imaginative and prescient is an space of deep studying devoted to decoding and understanding photographs. It’s used to assist train computer systems to “see” and to make use of visible data to carry out visible duties

Computer imaginative and prescient fashions are designed to translate visible knowledge based mostly on options and contextual data recognized throughout coaching. This allows fashions to interpret photographs  and apply these interpretations to predictive or resolution making duties.

Picture processing includes modifying or enhancing photographs to supply a brand new consequence. It will probably embrace optimizing brightness or distinction, growing decision, blurring delicate data, or cropping. The distinction between picture processing and pc imaginative and prescient is that the previous doesn’t essentially require the identification of content material.

Deep Learning is a part of a broader household of machine studying strategies based mostly on synthetic neural networks.

Deep-learning architectures corresponding to deep neural networks,  recurrent neural networks, convolutional neural networks have been utilized to fields together with pc imaginative and prescient, speech recognition, pure language processing, machine translation, bioinformatics, drug design, medical picture evaluation, materials inspection and board recreation packages, the place they’ve produced good outcomes

Synthetic neural networks (ANNs) had been impressed by data processing and distributed communication nodes in organic techniques. ANNs have varied variations from organic brains.

Keras is essentially the most used deep studying framework. Keras follows finest practices for decreasing cognitive load: it presents APIs, it minimizes the variety of person actions required for frequent use instances, and it gives clear & actionable error messages.

Following matters are coated as a part of the course

  • Introduction to Deep Learning
  • Synthetic Neural Networks (ANN)
    • Activation capabilities
    • Loss capabilities
    • Gradient Descent
    • Optimizer
  • Picture Processing
    • Convnets (CNN), hands-on with CNN
  • Gradients and Again Propagation – Arithmetic
    • Gradient Descent
    • Arithmetic
  • Picture Processing  / CV – Superior
    • Picture Knowledge Generator
    • Picture Knowledge Generator – Knowledge Augmentation
    • VGG16 – Pretrained community
    • VGG16 – with code enhancements
  • Purposeful API
    • Intro to Purposeful API
    • Multi Enter Multi Output Mannequin
  • Picture Segmentation
  • Pooling
    • Max, Common, World
  • ResNet Mannequin
    • Resnet overview
    • Resnet idea mannequin
    • Resnet demo
  • Xception
    • Depthwise Separable Convolution
    • Xception overview
    • Xception idea mannequin
    • Xception demo
    • Visualize Convnet filters

Who this course is for:

  • Python programmers, Machine Learning aspirants, Deep Learning Aspirants

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