HCTPL

Deep Learning Course

“Deep learning models have multiple hidden layers between the input and output layers. These layers enable the model to learn complex hierarchical representations of data.Deep learning includes generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for generating new data samples, images, and text.”

  • Understanding the core concepts of deep learning
  • Artificial neural networks: structure, function, and learning process
  • Advantages and limitations of deep learning compared to traditional machine learning models
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  • Neurons and Activation Functions: The building blocks of neural networks
  • Loss Functions: Measuring the performance of a neural network
  • Optimizers: Algorithms for adjusting network weights to minimize loss
  • Convolutional Neural Networks (CNNs): Specialized for image recognition and computer vision tasks
  • Recurrent Neural Networks (RNNs): Effective for sequential data like text and time series analysis
  • Autoencoders: Unsupervised learning models for dimensionality reduction and data compression
  • TensorFlow or PyTorch: Introduction to a chosen deep learning library
  • Building and training deep learning models using the library
  • Data preparation and pre-processing for deep learning models
  • Handling missing values and outliers
  • Feature scaling and normalization
  • Encoding categorical variables
  • Preventing overfitting in deep learning models
  • Dropout and Batch Normalization techniques
  • Image Recognition: Applying CNNs for object detection, image classification, and image segmentation
  • Natural Language Processing (NLP): Utilizing RNNs for tasks like sentiment analysis, machine translation, and text generation
  • Other Applications: Exploring deep learning applications in finance, healthcare, and recommender systems
  • Techniques for training deep learning models efficiently
  • Hyperparameter tuning for optimal model performance
  • Monitoring and evaluating deep learning models
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Refund Policy

At Hari Cornucopia Tech Private Limited, we prioritize customer satisfaction. Therefore, we offer a refund policy to ensure that participants have peace of mind when enrolling in our courses. If a participant is dissatisfied after the first class, they are eligible for a refund. However, once they attend the second class, the refund policy becomes void.