Bizi tercih ettiğiniz için teşekkür ederiz. Ekip üyelerimiz en kısa sürede sizlerle iletişime geçecektir.
Rezervasyonunuzu gönderdiğiniz için teşekkür ederiz! Ekibimizden bir yetkili kısa süre içinde sizinle iletişime geçecektir.
Eğitim İçeriği
- 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
Kurs İçin Gerekli Önbilgiler
Familiarity with machine learning, system architecture, and programming languages is recommended.
14 Saatler
Danışanlarımızın Yorumları (1)
Eğitim organize edilmiş ve iyi planlanmış oldu ve bu eğitimden sistematik bilgi edinmiş ve incelediğimiz konulara iyi bir bakışla ayrıldım.
Magdalena - Samsung Electronics Polska Sp. z o.o.
Eğitim - Deep Learning with TensorFlow 2
Yapay Zeka Çevirisi
