Q. Show What does the analogy “AI is the new electricity” refer to? AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before. AI is powering personal devices in our homes and offices, similar to electricity. Similar to electricity starting about 100 years ago, AI is transforming multiple industries. Through the “smart grid”, AI is delivering a new wave of electricity.
Q. Which of these is NOT a reason for Deep Learning recently taking off? We have access to a lot more data. Neural Networks are a brand new field. We have access to a lot more computational power. Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
Q. Recall this diagram of iterating over different ML ideas. Which of the statements below is false?  Being able to try out ideas quickly allows deep learning engineers to iterate more quickly. Faster computation can help speed up how long a team takes to iterate to a good idea. It is faster to train on a big dataset than a small dataset. Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).
Q. When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False? True False
Q. Which one of these plots represents a ReLU activation function? Left Right
Q. Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False? True False
Q. A demographic dataset with statistics on different cities' population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False? True False
Q. Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? It can be trained as a supervised learning problem. It is strictly more powerful than a Convolutional Neural Network (CNN).
Q. Assuming the trends described in the below figure are accurate (and hoping you got the axis labels right), which of the following are true? Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly. Increasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
Are RNNs good for data with temporal component?Unlike feed-forward neural networks, RNNs use feedback loops, such as backpropagation through time, throughout the computational process to loop information back into the network. This connects inputs and is what enables RNNs to process sequential and temporal data.
What are RNNs good for?Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step. RNN's are mainly used for, Sequence Classification — Sentiment Classification & Video Classification. Sequence Labelling — Part of speech tagging & Named entity recognition.
What is the best neural network model for the temporal data?1 Answer. The correct answer to the question “What is the best Neural Network model for temporal data” is, option (1). Recurrent Neural Network. And all the other Neural Network suits other use cases.
What is true about RNNs?Recurrent neural networks (RNNs) are the state of the art algorithm for sequential data and are used by Apple's Siri and Google's voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data.
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