Over the last few years, Deep Learning has proven itself to be the game-changer. This area of data science is the only one responsible for advancements in machine learning and artificial intelligence. From academic researches to self-driving cars, Deep Learning is found in all possible aspects nowadays.
Deep Learning is a complex and vast field that consists of several components. It cannot be mastered in a day and hence it will take several months if you want to dig deeper into this field. Core knowledge of linear algebra and calculus is very important before learning this area.
This article explores the basics of deep learning for beginners.
1| Basic of Machine Learning
This is the path that will lead you to the door of deep learning. It basically consists of three types of learning, supervised, unsupervised and reinforced learning. Techniques like linear regression, logistic regression are required in deep learning. If you are familiar with machine learning and want to try your hands on you can click here.
2| Introducing Deep Learning
The first task is to know the frameworks of deep learning. Deep Learning is mainly concerned with algorithms that are inspired by artificial neural networks. Various courses and online videos are available nowadays for understanding Deep Learning that you must not pass on this learning opt. Here is an online course for you to understand this subfield of machine learning more precisely. Avail here.
If you like to know more about deep learning, you can go through this book. This book by Michael Nielson is a great start for learning from the initials. Click here for the book.
3| Knowing Neural Networks
A neural network contains a layered design that includes an input layer, an output layer, and a hidden layer. It functions as the neurons in the human brain like receiving inputs and produce an output. You must know how the data can be handled as well as pre-processed, regularisation techniques, hyperparameter technique, data augmentation, etc. The functions of the artificial neural networks are used in deep learning that helps in speech recognition, image recognition, etc.
If you want to know more about Neural Networks, please go ahead and click here.
4| Basics of Convolution Neural Networks
Image Source: towardsdatascience.com
Convolution Neural Network starts playing a crucial role in deep learning. It is widely used image recognition and classifications, object detections, facial recognition, etc. Basically, it takes an image as an input, process and classifies under certain attributes. In deep learning, CNN models are used to train and test in such a way that an input image will pass through a series of convolution layers with layers, pooling, fully connected layers and then classifying the object with probabilistic values between 0 and 1.
For more information on CNN, please click here.
5| Understanding The Sequence Models
It is important to understand how to build the models like Recurrent Neural Networks (RNNs) and using the variants like Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTMs) if you want to dig deep a little more. Learning these will help you in audio applications, speech recognition, music synthesis, etc.
Want to know more about sequence models. Click here.
6| Unsupervised Deep Learning
This is a complex one but has many advantages and also has the potential to unlock the unsolvable problems that were done previously. Unsupervised Deep Learning is used to solve the issues created by supervised learning like biasing and the numerous manual efforts to make the algorithms work. Autoencoder neural network is an unsupervised deep learning algorithm.
This tutorial from Stanford will help you understand unsupervised deep learning a little more.
7| Learning Natural Language Processing
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This domain is concerned with understanding human languages and has emerged high nowadays with many benefits. It deals with constructing computational algorithms to automatically analyzation and representation of human language, can also be used for machine translation, dialogue generation, etc.
The link provided will help you to understand NLP more precisely. Go ahead and click here.
8| Deep Reinforcement Learning
We came to know the potential of deep reinforcement learning when the reinforcement learning algorithm is combined with deep learning method and AlphaGo was created, that defeated the strongest Go players.
Here is a quick guide to learn more about Deep Reinforcement Learning.
9| Generative Models
This is a powerful model for learning any distribution of data using unsupervised learning. This model is used to generate new data points with some variations by learning the true data distributions. There are two most efficient methods, they are Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE).
To know more about the model, click here.
10| Make It Practical
To implement deep learning, you must know how to use Python. It’s totally cool if you are not aware of implementing Python. We are here to help. Click here. There are loads of libraries available where you can try your hands on and be the master.
Also, if you plan to improve your deep learning skills and want to build your own deep learning server from scratch. Please go ahead and click here.
