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Keras Training

Keras Training

Welcome to the 5-day Keras Training program by KONCPT AI. This Corporate training is designed to provide participants with comprehensive knowledge and practical skills in using Keras, a powerful and user-friendly deep learning library in Python. The course includes lectures, hands-on exercises, and real-world projects to ensure a thorough understanding of deep learning concepts and Keras applications.

Description

Welcome to the 5-day Keras Training program by KONCPT AI. This Corporate training is designed to provide participants with comprehensive knowledge and practical skills in using Keras, a powerful and user-friendly deep learning library in Python. The course includes lectures, hands-on exercises, and real-world projects to ensure a thorough understanding of deep learning concepts and Keras applications.

Benefits of Attending This Training

  1. Practical Experience: Gain hands-on experience with deep learning projects.
  2. Expert Guidance: Learn from experienced deep learning practitioners.
  3. Comprehensive Curriculum: Covers both basic and advanced topics in Keras.
  4. Career Growth: Enhance your job prospects in AI and deep learning fields.
  5. Networking Opportunities: Connect with peers and industry experts.
  6. Certification: Receive a certificate of completion to validate your skills.

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Course Content


Introduction to Keras and Deep Learning

  1. Overview of deep learning and neural networks
  2. Introduction to Keras and its features
  3. Installing and setting up Keras
  4. Understanding Keras architecture and components
  5. Building your first neural network with Keras
  6. Hands-on exercises: Implementing basic neural networks
  7. Data preprocessing for deep learning
  8. Loading and preparing datasets with Keras
  9. Splitting data into training and validation sets
  10. Model evaluation and performance metrics

Building and Training Neural Networks

  1. Understanding layers and activation functions
  2. Configuring the model: Sequential and Functional APIs
  3. Compiling and training the model
  4. Hands-on exercises: Implementing feedforward neural networks
  5. Optimizers, loss functions, and metrics
  6. Overfitting and underfitting: Techniques to address them
  7. Regularization methods: Dropout, L1, and L2 regularization
  8. Hands-on exercises: Implementing regularization techniques
  9. Monitoring training with callbacks
  10. Saving and loading models

Convolutional Neural Networks (CNNs)

  1. Introduction to CNNs and their applications
  2. Understanding convolutional layers and pooling layers
  3. Building CNN architectures with Keras
  4. Hands-on exercises: Implementing CNNs for image classification
  5. Data augmentation techniques for improving model performance
  6. Transfer learning: Using pre-trained models
  7. Hands-on exercises: Implementing transfer learning with Keras
  8. Fine-tuning pre-trained models
  9. Evaluating and optimizing CNN models
  10. Case studies and real-world applications of CNNs

Recurrent Neural Networks (RNNs) and LSTM

  1. Introduction to RNNs and their applications
  2. Understanding RNN layers and architectures
  3. Long Short-Term Memory (LSTM) networks
  4. Hands-on exercises: Implementing RNNs for sequence prediction
  5. Gated Recurrent Units (GRUs) and their advantages
  6. Time series analysis and forecasting with RNNs
  7. Hands-on exercises: Implementing LSTM networks for time series data
  8. Combining CNNs and RNNs for advanced applications
  9. Evaluating and optimizing RNN models
  10. Case studies and real-world applications of RNNs

Advanced Topics and Real-World Projects

  1. Generative Adversarial Networks (GANs)
  2. Autoencoders and their applications
  3. Reinforcement learning with Keras
  4. Hands-on exercises: Implementing advanced deep learning models
  5. Model deployment: Serving Keras models in production
  6. Scaling and optimizing deep learning models
  7. Monitoring and maintaining deployed models
  8. Hands-on exercises: Building and deploying deep learning projects
  9. Future trends in deep learning with Keras
  10. Building a complete deep learning project from start to finish


Contact us

By the end of this 5-day training program, participants will have a solid foundation in using Keras for various deep learning tasks, from building simple neural networks to implementing advanced deep learning models and deploying them in real-world applications. Join us at KONCPT AI to enhance your deep learning skills and advance your career!

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