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Free Certificate
Fundamentals of Deep Learning and Neural Networks
Join this free deep learning course to explore neural networks, model training, and AI applications. Get expert-led deep learning training, and hands-on insights, and earn a free certification.
28 hours of learning
Neural Networks
Development News Story
Deep Learning
KEY HIGHLIGHTS
What You Will Learn
Structure of Neural Networks
Explore the architecture of deep learning neural networks and understand how they are inspired by the human brain. Learn the fundamentals that form the foundation of AI-driven models.
Topics Covered:
- Neural Networks – Understand the core structure and function of neural networks.
- Introduction to Perceptron – Learn about perceptrons, the building blocks of neural networks.
- Perceptron Training – Explore how perceptrons are trained to recognize patterns in data.
- Working of a Neuron – Understand how artificial neurons process information in deep learning models.
FeedForward Neural Networks
Learn how information flows through deep learning neural networks from the input layer to the output layer. Explore the inference process and its role in AI applications.
Topics Covered:
- Info Flow Between Two Layers – Discover how data moves through different layers in a neural network.
- Inference in Neural Networks – Understand how neural networks make predictions based on input data.
- Basic Image Recognition – Explore how deep learning is used for image classification tasks.
Backpropagation in NN
Understand the backpropagation algorithm, a key training method for deep learning neural networks. Learn to implement backpropagation and build a neural network using NumPy.
Topics Covered:
- Training a Neural Network – Learn how neural networks learn from data through iterative training.
- Cost Function – Understand the role of cost functions in optimizing neural networks.
- Gradient Descent in Neural Networks – Explore how gradient descent is used for network optimization.
- Backpropagation Algorithm – Learn the step-by-step process of training deep-learning neural networks.
Modifications to NN
Discover best practices for optimizing deep learning neural networks. Learn about regularization techniques, dropout layers, and batch normalization, along with practical implementation using TensorFlow and Keras.
Topics Covered:
- Regularization Strategies – Understand methods to prevent overfitting in deep learning models.
- Dropouts – Learn how dropout layers improve neural network generalization.
- Batch Normalization – Explore how batch normalization enhances training efficiency.
- Building Neural Networks in Keras – Implement deep learning models using Keras.
Hyperparameter Tuning
Learn the essentials of gradient descent and explore advanced optimization techniques. Understand how hyperparameter tuning improves deep learning model performance.
Topics Covered:
- Defining a Loss Function – Learn how loss functions measure network performance.
- Gradient Descent – Understand the role of gradient descent in optimizing neural networks.
- AdaGrad – Explore AdaGrad, an adaptive learning rate optimization algorithm.
- RMSProp – Learn how RMSProp enhances gradient descent performance.
- Adam – Understand the Adam optimization algorithm and its advantages.
Deep Learning Neural Networks Certification
Earn and Share Your Certificate
Official & Verifiable
Receive a signed and verifiable e-certificate from upGrad upon successfully completing the course.
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