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    Course content

    About Computer Vision Course in Hughes Springs

  • Course content
  • Google Drive folder

  • Plan of attack
  • Images and pixels
  • Cascade classifier - intuition
  • Loading and pre-processing the image
  • Face detection with Haarcascade and OpenCV
  • Haarcascades parameters 1
  • Haarcascades parameters 2
  • Eye detection with haarcascades
  • HOMERWORK – detecting other objects
  • Homework solution
  • HOG (Histograms of Oriented Gradients) – intuition
  • Face detection with HOG and Dlib
  • Face detection with CNN and Dlib
  • HOMEWORK – Haarcascade x HOG x CNN
  • Homework solution
  • Anaconda and PyCharm
  • Face detection on the webcam
  • Additional reading

  • Plan of attack
  • LBPH algorithm - intuition
  • Loading the faces dataset
  • Preprocessing the images
  • Training the LBPH classifier
  • Recognizing faces with LBPH
  • Evaluating the LBPH classifier
  • LBPH parameters
  • LBPH parameters – implementation
  • Detecting facial points
  • Detecting facial descriptors  
  • Calculating distances between faces
  • Recognizing faces with Dlib  
  • HOMEWORK
  • Homework solution
  • Face recognition on the webcam

  • Plan of attack
  • Object tracking vs. object detection
  • KCF and CSRT algorithms
  • Object tracking with KCF
  • Object tracking with CSRT
  • HOMEWORK
  • Homework solution
  • Additional reading

  • Plan of attack
  • Biological fundamentals
  • Artificial neuron
  • Perceptron
  • Weight update  
  • Introduction to multilayer neural networks
  • Activation functions
  • Hidden layer activation  
  • Output layer activation
  • Error calculation (loss function)
  • Basic algorithm
  • Gradient descent and derivative
  • Output layer delta
  • Hidden layer delta
  • Backpropagation and learning rate
  • Weight update with backpropagation 
  • Bias, error and multiple outputs
  • Hidden layers
  • Output layer with categorical data
  • Stochastic gradient descent
  • Deep learning
  • Pixels and neural networks
  • Importing the libraries
  • Extracting pixels from images  
  • Normalizing the data
  • Creating the train and test sets
  • Building and training the neural network
  • Evaluating the neural network
  • Saving and loading the network
  • Classifying one single image
  • Extracting features from images
  • Feature extraction with OpenCV  
  • Building and training the neural network
  • Evaluating the neural network
  • Saving, loading and classifying one single image

  • Plan of attack
  • Introduction to convolutional neural networks
  • Convolutional operation
  • Pooling
  • Flattening
  • Dense neural network
  • Importing the libraries
  • Loading the images
  • Creating the train and test dataset
  • Building and training the neural network
  • Evaluating the neural network
  • Saving and loading the network
  • Classifying one single image

  • Plan of attack
  • Transfer learning – intuition
  • Importing the libraries and dataset
  • Creating the train and test dataset
  • Pre-trained neural network
  • Creating the custom dense layer
  • Building and training the neural network
  • Evaluating the neural network
  • Fine tuning – intuition
  • Fine tuning – implementation and evaluation
  • Saving, loading and classifying one single image

  • Plan of attack
  • Importing the libraries and images
  • Creating the train and test dataset
  • Building and training the neural network
  • Saving and loading the model
  • Evaluating the neural network
  • Classifying one single image
  • Classifying multiple images
  • Classifying emotions in videos

  • Plan of attack
  • Autoencoders – intuition
  • Importing the libraries and dataset
  • Visualizing the images
  • Preprocessing the images
  • Building and training a linear autoencoder
  • Encoding the images
  • Decoding the images
  • Encoding and decoding the test images
  • Convolutional autoencoders

  • Plan of attack
  • YOLO – intuition
  • Downloading and compiling Darknet
  • Testing the detector
  • Darknet and GPU
  • Threshold and ext_output parameters
  • Detecting objects in videos

  • Plan of attack
  • Gestures and actions recognition – intuition
  • Importing the libraries and the image
  • Loading the pre-trained neural network
  • Predicting body points  
  • Detecting gestures in images
  • Detecting gestures in videos

  • Plan of attack
  • Deep dream – intuition
  • Loading the InceptionNet network
  • Loading and preprocessing the image
  • Getting the activations
  • Calculating the loss
  • Gradient ascent 1
  • Gradient ascent 2
  • Generating images

  • Plan of attack
  • Style transfer – intuition
  • Loading VGG19 network
  • Loading and pre-processing the images
  • Building the neural network 
  • Training the neural network  
  • Visualizing the result

  • Plan of attack
  • GANs – intuition
  • Loading the dataset
  • Building the generator 1
  • Building the generator 2
  • Building the discriminator
  • Calculating the loss
  • Training the GAN

  • Plan of attack
  • Image segmentation – intuition
  • Downloading the repository
  • Warning after Colab update
  • Importing the libraries
  • Loading the pre-trained neural network
  • Detecting objects
  • Removing the background  
  • Segmentation in videos
    • Why Should You Learn Computer Vision Course in Hughes Springs Training?

    The annual salary of an Computer Vision Course in Hughes Springs is $125k.

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    Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.

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    Certification

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    Program Core Credentials

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    Trained Students

    10000+
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    Success Ratio

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    Job Assistance

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    Track Week Days Course Duration Fast Track
    Week Days 40-45 Days 2 Hrs. Per Day Online
    Course Duration 7 Weekends 3 Hrs. Per Day Online
    Fast Track 8 Days 6+ Hrs. Per Day Online

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    Students Reviews

    Read genuine student reviews on ZebLearn’s expert-led SAP training and foreign language courses. Hear success stories from learners who gained certifications, skills, and career growth!

    Rajesh Sharma
    Data Scientist
    The Computer Vision course provided a comprehensive understanding of visual data analysis and machine learning techniques. The hands-on projects were particularly valuable in applying the concepts learned. This course has significantly enhanced my skills in developing computer vision applications.
    Priya Nair
    Machine Learning Engineer
    The Computer Vision course was well-structured and covered essential topics in depth. Learning about feature extraction and deep learning methods has been crucial for my work in developing image recognition systems. The practical exercises added immense value.
    Arjun Ghosh
    Research Scientist
    This course offered an in-depth exploration of computer vision techniques and their real-world applications. The training on image segmentation and object detection has been instrumental in my research projects. Highly recommended for anyone looking to specialize in this field.
    Snehal Patel
    Software Developer
    The Computer Vision course equipped me with the knowledge and skills needed to tackle complex visual data problems. The coverage of both traditional and modern techniques was comprehensive, and the project work helped solidify my understanding.
    Vivek Tiwari
    AI Developer
    Completing the Computer Vision course has broadened my expertise in visual data analysis. The courses practical approach and focus on real-world applications have greatly improved my ability to develop effective computer vision solutions for various industries.

    Computer Vision Course in Hughes Springs - Flexible batches for you

    Date Type Schedule Time
    SOLD OUT 18 March 2025 Weekend SAT - SUN (08 Week) 18:00 To 20:00
    Filling Img 23 March 2025 Weekday MON - FRI (08 Week) 08:00 To 10:00
    28 March 2025 Weekend MON - FRI (08 Week) 10:00 To 00:00

    Price  1,20,000

    Now  95,000

    Enroll Now, Pay Later

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