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US$20
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Duration: 2 Months
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Delivery mode: Online
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Group size: Individual
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Instruction language:
English,
German
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Certificate provided:
Yes
Neural networks are complex algorithms modeled after the human brain's structure and function, enabling computers to learn, adapt, and make decisions based on data.
*Key Components:*
1. Artificial Neurons (Nodes): Process inputs and transmit outputs.
2. Layers: Input, Hidden, and Output layers.
3. Connections (Synapses): Neurons communicate through weighted connections.
4. Activation Functions: Introduce non-linearity, allowing complex representations.
*How Neural Networks Work:*
1. Data Input: Feed data into the input layer.
2. Forward Propagation: Data flows through layers, processed by neurons.
3. Weight Adjustments: Backpropagation adjusts connection weights based on errors.
4. Optimization: Network minimizes loss function, improving performance.
*Types of Neural Networks:*
1. Feedforward Networks
2. Recurrent Neural Networks (RNNs)
3. Convolutional Neural Networks (CNNs)
4. Autoencoders
5. Generative Adversarial Networks (GANs)
*Applications:*
1. Image Recognition
2. Natural Language Processing (NLP)
3. Speech Recognition
4. Predictive Modeling
5. Robotics and Control Systems
*Advantages:*
1. Pattern Recognition
2. Adaptive Learning
3. Real-time Processing
4. Handling Non-Linear Relationships
*Challenges:*
1. Training Time
2. Overfitting
3. Interpretability
4. Data Quality Requirements
*Real-World Examples:*
1. Google's AlphaGo
2. Image classification (e.g., Facebook)
3. Speech assistants (e.g., Siri, Alexa)
4. Self-driving cars (e.g., Tesla)
*Resources:*
1. Andrew Ng's Coursera Course
2. Stanford CS231n: Convolutional Neural Networks
3. Neural Networks and Deep Learning by Michael Nielsen
Would you like more information on neural networks or specific applications?