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Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.


Which of the following is a subset of machine learning?
How many layers Deep learning algorithms are constructed?
The first layer is called the?
CNN is mostly used when there is an?
Which of the following is/are Common uses of RNNs?
Which neural network has only one hidden layer between the input and output?
RNNs stands for?
Deep learning algorithms are _______ more accurate than machine learning algorithm in image classification
Which of the following is well suited for perceptual tasks?
Which of the following is/are Limitations of deep learning?
The input image has been converted into a matrix of size 28 X 28 and a kernel/filter of size 7 X 7 with a stride of 1. What will be the size of the convoluted matrix?
Which of the following statements is true when you use 1×1 convolutions in a CNN?
Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes (p1, such that sum of p over all n equals to 1?
The number of nodes in the input layer is 10 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are
In which of the following applications can we use deep learning to solve the problem?
Assume a simple MLP model with 3 neurons and inputs= 1,2,3. The weights to the input neurons are 4,5 and 6 respectively. Assume the activation function is a linear constant value of 3. What will be the output ?
In a simple MLP model with 8 neurons in the input layer, 5 neurons in the hidden layer and 1 neuron in the output layer. What is the size of the weight matrices between hidden output layer and input hidden layer?
Which of the following would have a constant input in each epoch of training a Deep Learning model?
In CNN, having max pooling always decrease the parameters?
Sentiment analysis using Deep Learning is a many-to one prediction task
total questions: 25





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