Explore the fundamentals of Deep Learning in this comprehensive 58-minute conference talk from NDC Conferences. Gain a clear understanding of key concepts such as back-propagation, gradient descent, loss functions, and optimizers. Learn how to train a deep learning model for object recognition through a practical example. Discover the inner workings of neural networks, including hidden layers, weights, biases, and activation functions. Understand the importance of evaluating performance using cross-entropy and the role of optimizers in back-propagation. Delve into the process of initializing weights, updating them using Stochastic Gradient Descent, and the significance of partial differentials. Get introduced to TensorFlow and Keras as tools for implementing deep learning models. Perfect for those looking to demystify AI and machine learning concepts, this talk provides a solid foundation for beginners and a refresher for experienced practitioners in the field of Deep Learning.