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Course Outline
- Overview of neural networks and deep learning
- The concept of Machine Learning (ML)
- The necessity of neural networks and deep learning
- Selecting appropriate networks for different problems and data types
- Training and validating neural networks
- Comparing logistic regression with neural networks
- Neural Networks
- Biological inspirations for neural networks
- Neural Network fundamentals – Neurons, Perceptrons, and MLP (Multilayer Perceptron)
- Training MLP – The backpropagation algorithm
- Activation functions – Linear, Sigmoid, Tanh, Softmax
- Loss functions suitable for forecasting and classification
- Key parameters – Learning rate, regularization, momentum
- Building neural networks in Python
- Evaluating neural network performance in Python
- Basics of Deep Networks
- What is deep learning?
- Architecture of Deep Networks – Parameters, Layers, Activation Functions, Loss Functions, Solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders
- Deep Network Architectures
- Deep Belief Networks (DBN) – Architecture and applications
- Autoencoders
- Restricted Boltzmann Machines
- Convolutional Neural Networks
- Recursive Neural Networks
- Recurrent Neural Networks
- Overview of available Python libraries and interfaces
- Caffe
- Theano
- TensorFlow
- Keras
- MxNet
- Selecting the appropriate library for a specific problem
- Building deep networks in Python
- Choosing the right architecture for a given problem
- Hybrid deep networks
- Training networks – Selecting appropriate libraries and defining architecture
- Tuning networks – Initialization, activation functions, loss functions, optimization methods
- Avoiding overfitting – Detecting overfitting issues in deep networks and applying regularization
- Evaluating deep networks
- Case studies in Python
- Image recognition using CNNs
- Anomaly detection with Autoencoders
- Time series forecasting with RNNs
- Dimensionality reduction using Autoencoders
- Classification using RBMs
Requirements
Familiarity with machine learning concepts, systems architecture, and programming languages is advantageous.
14 Hours
Testimonials (2)
The adaptation of exos to our context and the consideration of our request
Amel Guetat - EURO-INFORMATION DEVELOPPEMENTS
Course - Fraud Detection with Python and TensorFlow
Machine Translated
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
