Dive into Deep Learning Workshop (part 2)

• Discuss regularization strategies such as batch norm, layer norm, and dropout layers
• Explore more complex model architectures, block structures, and applications of neural networks that are common in industry and academia
• Investigate how deep learning can be applied to different data modalities and for a wider variety of machine learning tasks
• Apply this knowledge to implement a deep learning model in PyTorch, based on technical specifications

General Kickoff Meeting

• Meet the team
• Learn more about our educational workshops
• Hear more regarding our new project
• Answer questions regarding recruitment process

A.I. Literacy Workshop

• Distinguish between data science, machine learning, and artificial intelligence and their applications
• Provide examples of A.I. applications in industry and identify the appropriate tools for solving various kinds of problems
• Introduce the challenges of working with A.I. systems and the importance to data and model literacy in the machine learning engineering process

Data Preprocessing Workshop

• Learn to perform basic exploratory data analysis (EDA) and data visualization
• Identify outliers, handle missing values, and perform other common data operations such as normalization, interpolation, and filtering
• Understand the intuition behind various preprocessing techniques for both categorical and continuous features
• Apply EDA and data preprocessing techniques to a novel data set without context

Classical ML Workshop

• Introduce the scikit-learn API and common practices in the field of machine learning
• Provide intuition for various classical machine learning techniques regarding their complexity, performance, and effectiveness in the context of different applications
• Explore concepts such as feature selection, model selection, hyperparameter tuning, performance metrics, and the bias/variance trade-off
• Apply this knowledge to a real-world dataset in a competition-style activity

Neural Networks for Novices Workshop (part 1)

• Examine deep learning concepts such as tensors, tensor operations, gradient descent, and backpropagation
• Define and discuss hyperparameters in the context of deep learning models, including learning rate, batch size, epochs, layers, hidden units, optimizers, and activation functions
• Interpret loss and accuracy curves to identify overfitting during the training process
• Apply this knowledge to a real-world dataset using TensorFlow