About Pascal's Triangle

Pascal's Triangle is a triangular array of binomial coefficients. Each number is the sum of the two numbers directly above it. Named after Blaise Pascal, this triangle has applications in algebra, probability, and combinatorics.

Uses: It's useful for calculating coefficients in binomial expansions, combinatorial problems, and generating patterns.

Spring - IoC Container

Intuition:

Spring's Inversion of Control (IoC) Container is like a factory that creates and manages objects (called beans) in a Spring application. Instead of creating objects manually in your code, you let the IoC Container do it for you. This allows your code to be more flexible and easier to manage.

Approach:

1. Configuration: You define what objects (beans) you need and how they should interact with each other. This is done through configuration files (XML or Java-based annotations).
2. Dependency Injection: The IoC Container automatically injects the required dependencies into the beans, meaning it provides the necessary objects to other objects. You don't need to manually create or connect them.
3. Lifecycle Management: The container also manages the lifecycle of these beans, such as their creation, initialization, and destruction.

Example:

Imagine you're building a car, and the car needs an engine. Instead of you manually assembling the engine and connecting it to the car, you just tell the IoC Container, "Hey, I need a car, and it needs an engine." The IoC Container then finds an engine, puts it in the car, and hands you the fully assembled car.

Benefits:

1. Decoupling: Your code is less tightly connected, making it easier to change or replace parts.
2. Easier Testing: Since dependencies are injected, it's easier to test components in isolation.
3. Flexibility: You can easily switch out components without changing much code.

Conclusion:

Spring's IoC Container simplifies object management in your application by taking care of object creation, dependency injection, and lifecycle management, allowing you to focus on writing business logic rather than managing dependencies.

Deep Learning and Reinforcement Learning Week 6 All Quiz

Practice: Recurrent Neural Networks 

1. (True/False) Recurrent Neural Networks are a class of neural networks that allow previous outputs to be used as inputs while having hidden states.

A) True
B) False

ANSWER= (A) True

 

2. (True/False) Recurrent Neural Networks are well suited in applications in which the context is important and needs to be incorporated in the prediction.

A) True
B) False

ANSWER= (A) True

 

3. These are the two main outputs of a recurrent neural network:

A) Prediction and state
B) Prediction and parameters
C) Prediction and recurrence
D) Prediction and learning rate

ANSWER= (A) Prediction and state

Practice: LSTM and GRU  

1. (True/False) The main motivation behind LSTM is to make it easier to keep information from distant past in current memory without reinforcement.

A) True
B) False

ANSWER= (A) True

 

2. RNNs are augmented with the following Gate Units:

A) Enter gate, leave gate, print gate
B) Input gate, forget gate, output gate
C) Store gate, remove gate, feed forward gate
D) Recursive gate, keep gate, reinforce gate

ANSWER= (B) Input gate, forget gate, output gate

 

3. Select the correct assertion regarding the gate units of RNNs:

A) The gate units control how long the events will stay in memory.
B) The gate units control if the events will stay in memory.
C) The gate units control how many items can be stored in memory.
D) A and B

ANSWER= (D) A and B

 

Practice: Regularization  

1. Which regularization technique can shrink the coefficients of the less important features to zero?

A) L2
B) Dropout
C) Batch Normalization
D) L1

ANSWER= (D) L1

 

2. (True/False) Batch Normalization tackles the internal covariate shift issue by always normalizing the input signals, thus accelerating the training of deep neural nets and increasing the generalization power of the networks.

A) True
B) False

ANSWER= (A) True

 

3. Regularization is used to mitigate which issue in model training?

A) Underfitting
B) High bias and low variance
C) Overfitting
D) Both underfitting and overfitting

ANSWER= (C) Overfitting

 

Week 6 Final Quiz  

1. (True/False) RNN models are mostly used in the fields of natural language processing and speech recognition.

A) True
B) False

ANSWER= (A) True

 

2. (True/False) GRUs and LSTM are a way to deal with the vanishing gradient problem encountered by RNNs.

A) True
B) False

ANSWER= (A) True

 

3. (True/False) GRUs will generally perform about as well as LSTMs with shorter training time, especially for smaller datasets.

A) True
B) False

ANSWER= (A) True

 

4. (True/False) The main idea of Seq2Seq models is to improve accuracy by keeping necessary information in the hidden state from one sequence to the next.

A) True
B) False

ANSWER= (A) True

 

5. (True/False) The main parts of a Seq2Seq model are: an encoder, a hidden state, a sequence state, and a decoder.

A) True
B) False

ANSWER= (B) False

 

6. Select the correct option, in the context of Seq2Seq models:

A) The Greedy Search algorithm selects one best candidate as an input sequence for each time step while the Beam Search produces multiple different hypothesis based on the output from the encoder.
B) The Beam Search algorithm selects one best candidate as an input sequence for each time step while the Greedy Search produces multiple different hypothesis based onthe output from the encoder.
C) The Greedy Search algorithm selects one best candidate as an input sequence for each time step while the Beam Search produces multiple different hypothesis based on conditional probability.
D) The Beam Search algorithm selects one best candidate as an input sequence for each time step while the Greedy Search produces multiple different hypothesis based on conditional probability.

ANSWER= (C) The Greedy Search algorithm selects one best candidate as an input sequence for each time step while the Beam Search produces multiple different hypothesis based on conditional probability.

 

7. Which is the gating mechanism for RNNs that include a reset gate and an update gate?

A) GRUs
B) LSTMs
C) Refined Gate
D) Complex Gate

ANSWER= (A) GRUs

8. LSTM models are among the most common Deep Learning models used in forecasting. These are other common uses of LSTM models, except:

A) Speech Recognition
B) Machine Translation
C) Image Captioning
D) Generating Images
E) Anomaly Detection
F) Robotic Control

ANSWER= (D) Generating Images