𝐆𝐎𝐃𝐒 4.0 𝐁𝐨𝐨𝐭𝐜𝐚𝐦𝐩 :Workshop 1 : Introduction to Computer Vision

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The Introduction to Deep Learning with PyTorch workshop provided a comprehensive overview of deep learning concepts and key neural network architectures. The session began with an introduction to deep learning, explaining its significance and applications. The instructor then covered various models, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) for image processing, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data, Auto-Encoders for unsupervised learning, and Transformers for state-of-the-art natural language processing and vision tasks.

A key part of the workshop focused on pre-trained models available in platforms such as TensorFlow Hub, PyTorch Hub, Keras, and NVIDIA NGC. These hubs provide ready-to-use models that can be fine-tuned for various applications, making deep learning more accessible and efficient.

In the hands-on session, participants explored a practical implementation using a Google Colab notebook focused on doogy_door.ipynb. The instructor guided us through data preprocessing, model building with PyTorch, training and evaluating deep learning models, and fine-tuning hyperparameters. The session allowed us to apply theoretical concepts in a real-world scenario, reinforcing our understanding of deep learning techniques.

This workshop was an excellent opportunity to gain both theoretical knowledge and hands-on experience with PyTorch, enhancing our ability to build and train deep learning models effectively.