Learn how to train a neural network with just one parameter, exploring the basics of optimization. Dive into the process of fine-tuning this single parameter to improve model performance and achieve your goals effectively.
Discover the essence of deep learning optimization algorithms, ranging from the foundational Gradient Descent to the advanced strategies of AdaGrad and Adam. Dive into their intricacies and learn how they shape the training process, leading to optimal model performance and convergence.
Selecting the best activation function is critical for effective neural network design. For hidden layers, ReLU is commonly used in CNNs and MLPs, while sigmoid and tanh suit RNNs. Output layer activation depends on the task: linear for regression, sigmoid for binary classification, softmax for multi-class, and sigmoid for multi-label classification.
In our daily lives, we effortlessly recognize faces and understand voices, tasks that seem almost second nature to us. But explaining how we do these things to machines is not easy. So, how do we make machines think? Can we teach them using examples? Think of it like this: just as we fuel our brains with energy, do we need to feed machine learning algorithms to make them learn? Machine learning models are made up of mathematical structures that allow them to map input to output. Imagine, you want to teach a machine to recognize faces in photos. You'd give it tons of pictures with faces labeled 'face' and pictures without labeled 'face'. The machine learns by looking at these examples, figuring out patterns, and then making its guesses whether a new picture has a face or not. Now, let's dive deeper and understand what an artificial neural network is, drawing inspiration from the intricate workings of biological neurons to construct models that simulate learning processes.
Understanding linear and non-linear activation functions is crucial in deep learning. Linear functions maintain a simple relationship between input and output, suitable for regression tasks. Non-linear functions like ReLU, sigmoid, and tanh introduce complexity, enabling networks to capture intricate patterns in the data. grasping these distinctions is essential for effective model design and optimization.
In the rapidly evolving landscape of Artificial Intelligence (AI), the journey from symbolic AI to the emergence of Deep Learning has been marked by significant milestones. This exploration delves into the historical context, the challenges encountered in the early days of AI, and the transformative breakthroughs that paved the way for the prominence of Deep Learning.