Description
• The concepts of Neural Networks and Machine Learning. Weak and Strong AI. Artificial neuron model.
• Typical neural networks workflow: features, layers, tensors, gradients, loss function, back propagation
• Types of problems: Regression, Classification, Object detection, Segmentation, Super Resolution, Image generation
• The feasibility of Machine Learning for science and research
• Advantages of Python and PyTorch for Neural Networks development
• Tensor objects in Python and PyTorch, interoperation with NumPy
• Obtaining tensor gradients in PyTorch with Autograd
• The structure of neural network implementation in PyTorch: definition, loss function, backprop, weights update
• Our first neural network in PyTorch, by example of Binary Classification
• Python environments: Linux and Windows, pip, Anaconda, Jupyter Notebook
• Training and evaluation of Binary Classification in PyTorch: “Cat or Dog?”
• PyTorch optimization algorithms in the context of neural network training
• The generic structure of optimization algorithms: parameters, optimization step, funciton evaluation closure
• The SGD algorithm explained
• Convolutional neural networks for classification problems
• Deploying LeNet to handwritten digits recognition for ZIP codes
• Deploying Mobilenetv2 imagenet classification
