# Roadmap to Deep Learning in 2021

##### This article talks about what road map you should follow in order to meet the demand and be successful in the field.

Before entering the article check this link to learn Machine learning and the importance of the road map, because it is particularly important to learn Machine learning before Deep learning Road map to Machine learning 2021.

Hands down Data Science is the most interesting technology that attracts the students towards it. Do you guys think ‘Is Machine Learning enough to get a job with respect to data science?’. Well, I will say nowadays it will be difficult because companies are looking for candidates who have knowledge about both Deep Learning and Machine learning. It is good to learn both deep learning and machine learning. A few years earlier only machine learning is enough to bag a job but technology develops every day.

Deep Learning has immense importance in our development and living because deep learning technology provides many advanced skills that are necessary in the real world. I know many of you have doubts about learning the most happening technology Deep Learning. Do not worry I am here to make the learning journey easier for you. Let’s dive in and know more about the pre-requisites of deep learning.

**Deep Learning:**

Deep Learning is a set of functions and algorithms that try to mimic the human brain while processing data. Algorithms present in deep learning solve complex problems with the help of Neural networks which work in an equivalent way like neurons in the brain.

**Importance of Deep Learning**

Machine learning is one of the most useful technology to predict the results by processing the data in Artificial Intelligence. Deep learning is like the core subset of Machine learning that uses advanced Artificial neural networks to capture high-dimensional data and to solve many complex problems that cannot be solved using Machine learning. The quality of processing a large amount of data or features makes Deep learning more important and powerful. Deep learning can be able to learn without the help of human monitoring it.

Before learning anything, we should have a curated list of resources and pre-requisites to learn.

**Roadmap to Deep Learning:**

Deep learning is one of the most used Machine learning approaches. So, to learn Deep learning a proper technique, and a road map is necessary to follow. Now we will see the most understandable way to learn Deep learning.

**Step-1: Applied Math**

Mathematics is definitely the foundation of the technology we use nowadays. This link leads to the Mathematics behind Machine learning Mathematics for ML. Applied math is the first thing you should learn while learning Deep learning. The few topics in applied math that are building blocks of Deep learning are Linear algebra, Probability, Statistics, and Calculus.

**Step-2: Programming Skills**

Programming skills are important if we want to convey our thoughts and procedure of training to a system. Like humans communicating through a spoken language in a quite similar way, we can communicate with computers through a programming language. To implement Deep learning algorithms we majorly use Python, MATLAB, R, Java, etc.

Among all these programming languages I would refer to learn python because it is very easy to understand and simple.

**Step-3: Machine Learning Basics**

To become an expert in Deep learning, the first thing we should have is a hands-on experience in Machine learning because Deep learning is a special part of Machine learning. In Machine learning, we will get know about how to fit the data, how to identify the patterns. Machine learning is nothing but the application of complex statistics with the help of computers.

Topics to learn in Machine learning are Training algorithms, Overfitting, Underfitting, Supervised learning algorithms, Bayesian algorithms, and Unsupervised algorithms.

**Step-4: Neural Networks**

Neural networks are the foundation of Deep learning. This is the first topic you will learn in Deep Learning. Researchers thought that can we make the machine learn in such a way that human being learns and this thought lead to the invention of Neural networks which mimics the human brain.

In this topic, we will learn about the input layer, an output layer, and hidden layers. We will also learn about forwarding and backpropagation of data within these layers in a network.

In the next step, we will learn the first technique in Deep learning that is Artificial neural networks.

**Step-4 Artificial Neural Networks**

ANN is the must learn technique in Deep learning if we want to deploy any project. First, we should know what is ANN and how it works. Then we should learn how forward propagation and backward propagation work in ANN. Off-late we will learn about loss function and the optimizers to optimize it, and weight techniques involved in a network. We use ANN majorly in Image processing, through this link you will get an about ANN and its applications in Image processing Image processing using ANN.

To get more clarity about ANN, start doing a project based on this.

**Step-5 Convolutional Neural Networks**

After learning ANN here comes CNN that is quite similar to ANN but with an extra layer called Convolutional layers and the filters present within these layers. In CNN backpropagation, forward propagation, and loss function all are the same as in ANN. Here we will learn about the Convolutional layer and what is the importance of involving it in the network.

We use Convolutional neural networks to classify images, and reduce the size of images, etc. This link takes you to a detailed explanation of Convolutional neural networks.

**Step-6: Recurrent Neural Network**

RNN is one of the most important parts of Neural networks. We RNN to process the sequential data. RNN is quite similar to CNN but RNN is used to process sequential values and generalize them. The major applications of RNN are in Speech recognition and in Natural language processing (NLP).

Bidirectional RNN’s, Computational Graphs, Deep Recurrent Networks, Recursive Networks, Encoder-Decoder, LSTM, and GRU. These are a few topics you will learn in RNN.

**Step-7: Transfer Learning**

Transfer learning is one of the most powerful techniques in Deep Learning. It transfers the knowledge it has gained in one problem to another problem. It makes Deep learning much easier to apply in real-life. In Transfer learning, we will learn a few techniques like XCEPTION, RESNET50, VGG16, VGG19.

This link helps you to understand Transfer learning in a better way. Transfer Learning.

**Applications of Deep Learning**

There are enamours applications to Deep learning in our life. Let’s see a few of them which are more important.

- Object Detection – This the most important and useful application of Deep learning.
- Self-Driving Cars
- Natural Language Processing (NLP)
- Medical Department
- Fraud Detection

**Conclusion**

To learn Deep Learning these are the few steps you should follow. And hope your learning journey will be easier.

Thanks for reading!