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Course Outline
- Overview of Neural Networks and Deep Learning
- Understanding Machine Learning (ML) concepts
- The necessity of neural networks and deep learning
- Selecting appropriate networks for various problems and data types
- Training and validating neural networks
- Comparing logistic regression with neural networks
- Neural Networks
- Biological inspirations behind neural networks
- Neural Network fundamentals: Neurons, Perceptrons, and MLP (Multilayer Perceptron)
- Training MLPs using the backpropagation algorithm
- Activation functions: Linear, Sigmoid, Tanh, and Softmax
- Loss functions suitable for forecasting and classification tasks
- Key parameters: Learning rate, regularization, and momentum
- Building neural networks in Python
- Evaluating neural network performance in Python
- Deep Network Fundamentals
- Defining deep learning
- Deep network architecture: Parameters, layers, activation functions, loss functions, and solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders
- Deep Network Architectures
- Deep Belief Networks (DBNs): Architecture and applications
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Networks (CNNs)
- Recursive Neural Networks
- Recurrent Neural Networks (RNNs)
- Overview of Python Libraries and Interfaces
- Caffe
- Theano
- TensorFlow
- Keras
- MXNet
- Selecting the appropriate library for specific problems
- Building Deep Networks in Python
- Choosing the right architecture for a given problem
- Hybrid deep networks
- Network training: Selecting libraries and defining architecture
- Network tuning: Initialization, activation functions, loss functions, and optimization methods
- Avoiding overfitting: Detecting and addressing overfitting issues in deep networks, including regularization
- Evaluating deep network performance
- Python Case Studies
- Image recognition using CNNs
- Anomaly detection with autoencoders
- Time series forecasting using RNNs
- Dimensionality reduction with autoencoders
- Classification with RBMs
Requirements
Familiarity with machine learning, system architecture, and programming languages is recommended.
14 Hours
Testimonials (1)
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
