Deep Learning Tutorials and Courses

Deep learning is an artificial learning function that works the same as the human brain. It processes the data and creates patterns that allow making decisions based on the created patterns and gathering data. Deep learning is considered a part of machine learning that consists of the networks that allow unsupervised learning from unstructured or the unlabeled data. Deep learning is commonly known as deep neural learning. It has uses in areas like fraud detection, money laundering, and more. 

Deep learning or hierarchical learning is a part of wide machine learning and is based on the layers used in any artificial neural network. Deep learning can be supervised, semi-supervised, or unsupervised. It depends on how one grasps the logic and basics to get started. Deep learning is based on functions and algorithms that allow solving complex problems efficiently.

Getting started with the basics will take around 4-6 weeks. However, if one wants a strong knowledge of deep learning, it may take around 26 weeks.

It is highly recommended to learn the concepts of machine learning first. Machine learning will allow understanding the algorithms working and how they help the machines process the data and make predictions. 

Machine learning is based on the maths fundamentals, AI works on more rigorous maths like statistics, and deep learning is based on machine learning methods. ML only includes one layer of calculation, but deep learning processes the previous layer's results to predict the result. Thus machine learning has to be the first choice.

Below is the list of the best machine and deep learning books:

• Deep Learning (mathematical concepts) by Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Deep Learning with Python by Francois Chollet (beginners and intermediate programmers)
• The Hundred-Page Machine Learning Book by Andriy Burkov
• Deep Reinforcement Learning Hands-On by Maxim Lapan
• The Book of Why by Judea Pearl, Dana Mackenzie.
• Machine Learning Yearning by Andrew Ng.
• Interpretable Machine Learning by Christoph Molnar.

Data science allows working around with data to get insights. It requires tools that are beyond machine learning like data analysis, slicing, and dicing the data. 

Machine learning is a part of the data science based on the data for prediction.

Both are somehow different, and it is your choice that you want to learn first.

If you want to stick to machine learning, then no data science tools or techniques are required. 

But if you work in the industry, then you have to have data knowledge first before going for machine learning techniques.

You can go for either online courses or the text references for deep learning content from beginner to the advanced level. Before starting, you should have basic maths knowledge like- linear algebra, statistics, and calculus. If you are a beginner, then you can go to Andrew Ng's maths training. Below are some courses that you can consider sites like hackr.io, Coursera, Udacity, and more.

Learn the required tools for deep learning and practice codes to produce data.

Below are the steps that you can follow to create a deep learning algorithm:

First, define the problem in detail and look for the desired solution.

Gather data related to the problem.

Then choose a measure to apply

Then set a readily available evaluation protocol.

Prepare the data correctly.

Split the data for different test cases.

Implement the solution with a simple example.

Validate the implementation.

Then work on the algorithm.

Below are the benefits of using deep learning:

It allows us to create new features using the provided features in the training datasets.

It has advanced data processing models that help to analyze data in an improved manner.

It provides the best results using unstructured data.

There is no need to label the data while processing.

It provides high-quality results for prediction.