Deep Learning Study Guide 2026
Everything you need to pass the Deep Learning exam in one place: the exam format, every topic to study, real practice questions with explanations, flashcards, and full-length practice tests. Free, no sign-up needed.
📋 Deep Learning Exam Format at a Glance
📚 Deep Learning Topics to Study (22)
✍️ Sample Deep Learning Questions & Answers
1. What does 'fine-tuning' mean in the context of transfer learning?
Fine-tuning involves taking a pre-trained model and continuing its training on a new (often smaller) dataset so it adapts to the target task while retaining prior knowledge.
2. What is the hidden state in an RNN?
The hidden state is a learned vector representation that encodes relevant information from all previous time steps and is updated at each new input.
3. What is a GRU (Gated Recurrent Unit) and how does it differ from an LSTM?
GRUs combine the forget and input gates into a single update gate and merge the cell and hidden states, reducing parameters while achieving comparable performance to LSTMs on many tasks.
4. What is data augmentation used for when training CNNs?
Data augmentation applies random transforms like flips, crops, and color jitter to training images, exposing the model to more variation and reducing overfitting.
5. In a supervised learning task, the number of neurons in the output layer should match the number of classes (where the number of classes is larger than 2). Is this statement true or false?
This statement is false. While for multi-class classification (more than two classes) using a softmax activation, the number of output neurons typically matches the number of classes, this is not universally true. For binary classification (two classes), a single output neuron with a sigmoid activation function is often sufficient. This neuron outputs the probability of one class, with the other class's probability being 1 minus that value.
6. Which architecture of neural network would be more suited to address an image identification challenge (recognizing a cat in a photo)?
A Convolutional Neural Network (CNN) is the most suitable architecture for image identification challenges like recognizing a cat in a photo. CNNs are specifically designed to process image data, utilizing convolutional layers to automatically learn hierarchical spatial features such as edges, textures, and shapes. Their architecture, including pooling and weight sharing, makes them highly effective at recognizing patterns and objects within images, outperforming other network types for visual tasks.