Machine Learning Workshop: go to https://meetings.aip.de/event/6/

• 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

• 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

• 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

• 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

• 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

An overview of activation function types and their applications: ReLU, Sigmoid, Tanh
• Cross Entropy: efficient loss beyond MSE
• Softmax - multiclass logistic function
• Intersection over union metric

• Using PyTorch datasets to organize the data for batch training
• Unet structure. Semantic Image segmentation

• An overview of GPU performance in various applications
• Brief intercomparison of different types of accelerators
• Key programming principles to achieve high GPU performance in Machine Learning
• Benchmarking CNNs on different GPUs: cnn-benchmarks test suite

• An overview of GPU-enabled libraries for feature extraction: torchvision.transform, Pandas, OpenCV, CUBLAS, CUFFT

• Performance limitations of pure Python code
• Implementing high-performance C++/Fortran/OpenCL extensions to PyTorch with pybind11

• Analyzing GPU efficiency for training and inference, by example of VoiceLoop – a neural text-to-speech processor

An overview of activation function types and their applications: ReLU, Sigmoid, Tanh
• Cross Entropy: efficient loss beyond MSE
• Softmax - multiclass logistic function
• Intersection over union metric

• Using PyTorch datasets to organize the data for batch training
• Unet structure. Semantic Image segmentation

• An overview of GPU performance in various applications
• Brief intercomparison of different types of accelerators
• Key programming principles to achieve high GPU performance in Machine Learning
• Benchmarking CNNs on different GPUs: cnn-benchmarks test suite

• An overview of GPU-enabled libraries for feature extraction: torchvision.transform, Pandas, OpenCV, CUBLAS, CUFFT

• Performance limitations of pure Python code
• Implementing high-performance C++/Fortran/OpenCL extensions to PyTorch with pybind11

• Analyzing GPU efficiency for training and inference, by example of VoiceLoop – a neural text-to-speech processor

An overview of activation function types and their applications: ReLU, Sigmoid, Tanh
• Cross Entropy: efficient loss beyond MSE
• Softmax - multiclass logistic function
• Intersection over union metric

• Using PyTorch datasets to organize the data for batch training
• Unet structure. Semantic Image segmentation

• An overview of GPU performance in various applications
• Brief intercomparison of different types of accelerators
• Key programming principles to achieve high GPU performance in Machine Learning
• Benchmarking CNNs on different GPUs: cnn-benchmarks test suite

• An overview of GPU-enabled libraries for feature extraction: torchvision.transform, Pandas, OpenCV, CUBLAS, CUFFT

• Performance limitations of pure Python code
• Implementing high-performance C++/Fortran/OpenCL extensions to PyTorch with pybind11

• Analyzing GPU efficiency for training and inference, by example of VoiceLoop – a neural text-to-speech processor

An overview of activation function types and their applications: ReLU, Sigmoid, Tanh
• Cross Entropy: efficient loss beyond MSE
• Softmax - multiclass logistic function
• Intersection over union metric

• Using PyTorch datasets to organize the data for batch training
• Unet structure. Semantic Image segmentation

• An overview of GPU performance in various applications
• Brief intercomparison of different types of accelerators
• Key programming principles to achieve high GPU performance in Machine Learning
• Benchmarking CNNs on different GPUs: cnn-benchmarks test suite

• An overview of GPU-enabled libraries for feature extraction: torchvision.transform, Pandas, OpenCV, CUBLAS, CUFFT

• Performance limitations of pure Python code
• Implementing high-performance C++/Fortran/OpenCL extensions to PyTorch with pybind11

• Analyzing GPU efficiency for training and inference, by example of VoiceLoop – a neural text-to-speech processor

An overview of activation function types and their applications: ReLU, Sigmoid, Tanh
• Cross Entropy: efficient loss beyond MSE
• Softmax - multiclass logistic function
• Intersection over union metric

• Using PyTorch datasets to organize the data for batch training
• Unet structure. Semantic Image segmentation

• An overview of GPU performance in various applications
• Brief intercomparison of different types of accelerators
• Key programming principles to achieve high GPU performance in Machine Learning
• Benchmarking CNNs on different GPUs: cnn-benchmarks test suite

• An overview of GPU-enabled libraries for feature extraction: torchvision.transform, Pandas, OpenCV, CUBLAS, CUFFT

• Performance limitations of pure Python code
• Implementing high-performance C++/Fortran/OpenCL extensions to PyTorch with pybind11

• Analyzing GPU efficiency for training and inference, by example of VoiceLoop – a neural text-to-speech processor

An overview of activation function types and their applications: ReLU, Sigmoid, Tanh
• Cross Entropy: efficient loss beyond MSE
• Softmax - multiclass logistic function
• Intersection over union metric

• Using PyTorch datasets to organize the data for batch training
• Unet structure. Semantic Image segmentation

• An overview of GPU performance in various applications
• Brief intercomparison of different types of accelerators
• Key programming principles to achieve high GPU performance in Machine Learning
• Benchmarking CNNs on different GPUs: cnn-benchmarks test suite

• An overview of GPU-enabled libraries for feature extraction: torchvision.transform, Pandas, OpenCV, CUBLAS, CUFFT

• Performance limitations of pure Python code
• Implementing high-performance C++/Fortran/OpenCL extensions to PyTorch with pybind11

• Analyzing GPU efficiency for training and inference, by example of VoiceLoop – a neural text-to-speech processor