Summary
In this comprehensive exploration of DL, we embarked on a journey through the intricate landscapes of NNs, optimization algorithms, and fundamental concepts that underpin this transformative field. We began our voyage by deciphering NN fundamentals, understanding the building blocks of DL, and uncovering the power of activation functions, weight initialization, and embeddings. As we delved deeper, we navigated the seas of optimization, unraveling the intricacies of gradient descent, learning rates, and various optimization algorithms that guide the training of NNs. We also shed light on the vanishing and exploding gradient problems, which are crucial challenges to overcome in the pursuit of effective training.
Our odyssey continued with a tour of common network architectures, from CNNs mastering image analysis to RNNs and LSTMs excelling in sequential data tasks. We encountered the creative minds behind GANs, explored the power of transformers in NLU, and marveled at the...
