Schedule

Suggested Readings:

• Few things to know about Machine Learning
• How Alpha Go works

• Useful Stable diffusion prompts

• Deep learning frameworks

Suggested Readings:

• How to setup up Conda for deep learning
• How to work with google Colab
• Managing Conda environement

Suggested Readings:

• MNIST dataset
• Loss functions visualizatio
• Visualize optimization algorithms
• CIFAR-10 dataset.

Suggested Readings:

• MNIST dataset
• Fast Library for Approxmiation Nearest neighbors
• Recognizing and Learning Object Categories Deep Learning using Linear Support Vector Machines

Suggested Readings:

• Why Momentum works
• Adaptive SubGradient methods
• Adam: A method for stochatic optimization
• On the convergence of the Adam method

Suggested Readings:

• Tensor MLP playground
• Activations function in neural networks
• Dropout

Suggested Readings:

• Pytorch tutorials
• Deep Learning with PyTorch: A 60 Minute Blitz
• Pytorch Custom Model Step by Step
• Create a custom dataloader
• Building a custom data loader

Suggested Readings:

• Soumith benchmarks for CONV performance
• ConvNetJS CIFAR-10 demo allows you to play with ConvNet architectures and see the results and computations in real time, in the browser.
• Caffe, one of the popular ConvNet libraries.
• State of the art ResNets in Torch7

Suggested Readings:

• Multi Object Tracking using Transformers