1 Introduction to Deep Learning

⇦ Back to Transfer learning and fine-tuning

2 Transfer Learning Fundamentals ⇨

### What is Deep Learning?

Deep learning is a subset of artificial intelligence where neural networks are used to mimic the way the human brain works. These neural networks learn from large amounts of data to recognize patterns and make decisions. Deep learning has revolutionized many fields like image recognition, natural language processing, and autonomous driving.### Neural Networks and Activation Functions

Neural networks are the building blocks of deep learning. They are composed of layers of interconnected nodes called neurons. Each neuron takes inputs, applies a mathematical operation, and passes the output to the next layer. Activation functions, like ReLU or sigmoid, introduce non-linearity to the network, allowing it to learn complex patterns.### Training Deep Neural Networks

Training deep neural networks involves feeding them with labeled data, called supervised learning. The network learns to map the inputs to the correct outputs through a process called backpropagation. During training, the network adjusts its weights and biases to minimize the difference between predicted and actual outputs.### Supervised vs. Unsupervised Learning

In supervised learning, the model is trained on labeled data, where the correct answers are provided. This allows the network to learn the relationship between inputs and outputs. On the other hand, unsupervised learning deals with unlabeled data, where the model needs to find patterns and structures on its own.### Applications of Deep Learning

Deep learning finds applications in various fields, such as healthcare (diagnosis), finance (fraud detection), and robotics (object recognition). Convolutional Neural Networks (CNNs) are used for image recognition tasks, while Recurrent Neural Networks (RNNs) excel in sequence data processing like language translation.### Challenges and Future Directions

Despite its success, deep learning faces challenges like interpretability and generalization to new data. Researchers are working on improving algorithms and exploring novel architectures to overcome these hurdles. The future of deep learning looks promising with continued advancements in the field. This lesson provides a glimpse into the fascinating world of deep learning, showcasing how neural networks can revolutionize industries and bring about innovative solutions to complex problems. From training models to recognizing patterns, deep learning offers a powerful toolset for creating intelligent systems that can learn from data and make informed decisions.Now let's see if you've learned something...

2 Transfer Learning Fundamentals ⇨