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Would you like a detailed Deep Learning Roadmap? If so, you should read this article. The Deep Learning Roadmap will be presented in its entirety in this post. Additionally, you’ll discover some of the top resources for learning Deep Learning ideas.
Let’s look at the Skills Required for Deep Learning before we talk about the Deep Learning Roadmap.
Day by day, deep learning gains popularity. Along with machine learning, understanding deep learning is crucial. Consequently, you need the following 6 talents in order to master deep learning:
- Math abilities.
- coding abilities.
- Skills in data engineering.
- expertise in machine learning.
- understanding of DL algorithms.
- understanding of DL frameworks
Mathematical abilities are the initial step or competence in deep learning. It aids in your comprehension of how machine learning and deep learning algorithms operate.
You must study the following topics in mathematics:
- Statistics & Probability.
- Linear Algebra.
- Calculus.
Let’s now examine how your understanding of all these topics can benefit your machine learning and deep learning efforts. Before that, though, let me to be clear: Don’t imagine that you can plunge into deep learning without first understanding about machine learning.
a) Statistics and Probability
There is a theory known as Bayes in probability. The Naive Bayes Algorithm uses this to classify our data. Probability Distribution is the next. This will enable you to estimate the potential frequency of an occurrence. Additionally, you must grasp how hypothesis testing and sampling operate.
b) Linear algebra
Matrices and vectors are the two key elements in linear algebra that are employed in deep learning and machine learning. Both of these are widely utilised in deep learning. Image recognition use matrices. You utilise matrices to represent the images you use for image recognition.
C) Calculus
There are two types of calculus: integral calculus and differential calculus. They aid in calculating the likelihood of certain events. For instance, the Naive Bayes algorithm may be used to determine the posterior probability.
If you want to become an expert in deep learning, you must establish strong programming abilities. You have a wide variety of programming languages to select from. Deep learning’s most popular programming languages are:
The best programming languages for machine learning and deep learning, however, are Python and R. You should study Python or R, in my opinion.
So, if you’re a newbie, I suggest learning Python.
You ought to possess some data wrangling abilities. There is a vast quantity of data used in deep learning. As a result, you should be familiar with handling this data. Skills in Data Wrangling include:
a) Data Preparation
For data pre-processing, the following procedures are necessary:
- Cleaning.
- Parsing.
- Correcting.
- Consolidating.
b) Extraction, Transformation, and Load (ETL)
You should be able to retrieve data from a local server or the internet. You must be knowledgeable with data transformation. Transforming data entails putting it in an appropriate, respectable format. You must understand how to load the data into your application since loading is the following step.
c) understanding of databases
Since data is the foundation of deep learning, you should be familiar with databases. You must be familiar with Oracle Database, MySql, and NoSql.
The ability to understand machine learning algorithms is the next most crucial one. Because you need a foundational understanding of machine learning algorithms in order to master deep learning. learn at least a few well-known machine learning algorithms.
- Naive Bayes.
- Support Vector Machine.
- K nearest Neighbour.
- Linear Regression.
- Decision Tree.
- Random Forest.
These algorithms come into two categories: clustering and classification.
There are two types in classification: classification and regression. Data are divided into multiple groups by classification algorithms, whilst data are predicted via regression.
Data is divided up into multiple clusters during clustering based on certain comparable qualities.
You must learn a deep learning algorithm after learning a machine learning method. The prevalent and well-liked Deep Learning algorithms are:
- Artificial Neural Network (ANN).
- Convolutional Neural Network (CNN).
- Recurrent Neural Network (RNN).
- Generative Adversarial Network.
- Deep Belief Network.
- Long Short Term Memory Network.
After mastering these algorithms, you ought to learn how to:
- Choose an issue.
- Select an algorithm that is suited for your task.
- Use one or more algorithms to create a model.
- Make your model as accurate as possible.
You ought to be familiar with deep learning frameworks.
The most well-liked Deep Learning framework-
- TensorFlow.
- Theano.
- Skitkit Learn.
- PyTorch.
- Microsoft Cognitive Toolkit.
Let’s talk about a few frameworks in more depth now:
a) Tensorflow
The most popular framework for machine learning and deep learning is called Tensorflow. It is a collection of open-source applications. It is used to compute numerical values using a data flow diagram.
b) Theano
You can define, improve, and assess mathematical operations with the aid of Theano. Popular libraries include KERAS, BLOCKS, and LASAGNE.
c) Scikit Learn
It is based on libraries that are already in use, including NUMPY, SCIPY, and MATPLOTLIB. With 23,000 Github commits, it began as a GOOGLE SUMMER OF CODE.
Start working on deep learning projects after acquiring all the necessary abilities. You will learn more as you complete additional assignments.
To become a Deep Learning Expert, just these abilities are needed. You’ve taken the first step toward deep learning, congratulations.
The most crucial thing, though, is to continually developing your abilities by taking on increasingly difficult difficulties.
You will learn more about deep learning as you practise more. So after finishing these stages, keep going and look for new problems to tackle.
In this article, I have discussed a step-by-step Deep Learning Roadmap & Learning paths. If you have any doubts or queries, feel free to ask me in the comment section.
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