Deep Learning Training in Thimphu, Bhutan

Deep Learning training provides participants with an in-depth understanding of neural networks and their advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The course covers essential topics like activation functions, backpropagation, optimization techniques, and deep learning frameworks like TensorFlow and PyTorch. Participants learn to build, train, and fine-tune models for tasks such as image recognition, natural language processing, and time series forecasting. Hands-on exercises focus on preparing datasets, designing network architectures, and deploying deep learning models in real-world scenarios. By the end of the training, attendees will be equipped to harness the power of deep learning for cutting-edge applications across industries.

Introduction to Deep Learning and Neural Networks

1. Introduction to Deep Learning
• Overview of deep learning and its applications
• How deep learning differs from traditional machine learning
• Real-world applications: image classification, NLP, etc.
2. Neural Networks and Perceptrons
• Structure of a neural network
• Perceptron model and its limitations
• Activation functions: ReLU, Sigmoid, and Tanh
3. Training Neural Networks
• Forward propagation and backpropagation
• Loss functions and optimization techniques (Gradient Descent)
• Practical exercise
4. Using TensorFlow and Keras
• Introduction to TensorFlow and Keras libraries
• Setting up the environment for deep learning projects
• Practical exercise

Advanced Topics in Deep Learning

5. Improving Neural Network Performance
• Overfitting and underfitting in neural networks
• Techniques to improve model accuracy: Regularization, dropout, and data augmentation
• Practical exercise
6. Convolutional Neural Networks (CNNs)
• Introduction to CNNs and their architecture
• Applications in image classification and computer vision
• Practical exercise
7. Deep Learning for Text
• Introduction to Recurrent Neural Networks (RNNs)
• Applications in natural language processing
• Practical exercise
8. Hands-On Exercises and Evaluation
• Hands-on Exercises: Build and train a neural network model on a real dataset
• Evaluating model performance: metrics like accuracy, precision, and recall