A) Python: Python ranks first in the latest annual ranking of popular programming languages. The increasing adoption of machine learning worldwide is a major factor contributing to its growing popularity. Python is relatively easy to learn, scalable and open source. Python has many awesome visualization packages and useful core libraries like Numpy, scipy, pandas, matplotlib, seaborn, sklearn which really makes your work very easy and empower the machines to learn.
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B) R Programming Language: R language can be used by non-programmers including data miners, data analysts, and statisticians. R programming language is a fantastic choice when it comes to crunching large numbers and is the preferred choice for machine learning applications that use a lot of statistical data. With user-friendly IDE’s like RStudio and various tools to draw graphs and manage libraries – R is a must-have programming language in a machine learning engineer’s toolkit.
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C) Java and JavaScript: Though Python and R continue to be the favourites of machine learning enthusiasts, Java is gaining popularity among machine learning engineers. Using Java for machine learning projects makes it easier for machine learning engineers to integrate with existing code repositories.
D) Julia: Julia is a high-level programming language for computational science and numerical analysis. It comes with a large mathematical feature library, a parallel and distributed execution program, a sophisticated compiler, and numerical precision. Julia is intended to overcome the limitations of Python and other computational programming and data processing languages and applications.
E) Go: Go is an open source and C-style programming language supported by Google. Go is a perfect low-level language for developers who want to work in the area of systems programming. It has a lot of the same features as C and C++, but without the complicated syntax and steep learning curve.
A) Many Ideas Of Deep Learning (Neural Networks) Have been Around For Decades. Why Are These Ideas Taking Off Now?
Two Of The Biggest Drivers Of Recent Progress Have Been: 1) Data Availability And 2) Computational Scale.
E) Cost Function quantifies the error between actual values and predicted values and presents it in the form of a single real number.
F) Gradient descent is an iterative optimization algorithm for finding the local minimum of a function.
The cost function may consist of many minimum points. The gradient may settle on any one of the minima, which depends on the initial point (i.e initial parameters(theta)) and the learning rate. Therefore, the optimization may converge to different points with different starting points and learning rate.
H) Alpha – The Learning Rate: We have the direction we want to move in, now we must decide the size of the step we must take.
Overfitting refers to the phenomenon where a neural network models the training data very well but fails when it sees new data from the same problem domain.
I) Regularization refers to a set of different techniques that lower the complexity of a neural network model during training, and thus prevent the overfitting.
I) Dropout: This is the one of the most interesting types of regularization techniques. It also produces very good results and is consequently the most frequently used regularization technique in the field of deep learning
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