Neural computing – Data scienceのトレーニングコース




14 時間 休憩を含む通常に 2 日間がかかります


Knowledge/appreciation of machine learning, systems architecutre and programming languages are desirable



Machine Translated


  1. Overview of neural networks and deep learning
    • The concept of Machine Learning (ML)
    • Why we need neural networks and deep learning?
    • Selecting networks to different problems and data types
    • Learning and validating neural networks
    • Comparing logistic regression to neural network
  2. Neural network
    • Biological inspirations to Neural network
    • Neural Networks– Neuron, Perceptron and MLP(Multilayer Perceptron model)
    • Learning MLP – backpropagation algorithm
    • Activation functions – linear, sigmoid, Tanh, Softmax
    • Loss functions appropriate to forecasting and classification
    • Parameters – learning rate, regularization, momentum
    • Building Neural Networks in Python
    • Evaluating performance of neural networks in Python
  3. Basics of Deep Networks
    • What is deep learning?
    • Architecture of Deep Networks– Parameters, Layers, Activation Functions, Loss functions, Solvers
    • Restricted Boltzman Machines (RBMs)
    • Autoencoders
  4. Deep Networks Architectures
    • Deep Belief Networks(DBN) – architecture, application
    • Autoencoders
    • Restricted Boltzmann Machines
    • Convolutional Neural Network
    • Recursive Neural Network
    • Recurrent Neural Network
  5. Overview of libraries and interfaces available in Python
    • Caffee
    • Theano
    • Tensorflow
    • Keras
    • Mxnet
    • Choosing appropriate library to problem
  6. Building deep networks in Python
    • Choosing appropriate architecture to given problem
    • Hybrid deep networks
    • Learning network – appropriate library, architecture definition
    • Tuning network – initialization, activation functions, loss functions, optimization method
    • Avoiding overfitting – detecting overfitting problems in deep networks, regularization
    • Evaluating deep networks
  7. Case studies in Python
    • Image recognition – CNN
    • Detecting anomalies with Autoencoders
    • Forecasting time series with RNN
    • Dimensionality reduction with Autoencoder
    • Classification with RBM








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