Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep Learning? | Simplilearn

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This Deep Learning tutorial is designed for beginners who want to learn Deep Learning from scratch. We will look at where Deep Learning is applied and what exactly this term means. We’ll see how Deep Learning, Machine Learning, and AI are different and why Deep Learning even came into the picture. We will then proceed to look at Neural Networks, which are the core of Deep Learning. Before we move into the working of Neural Networks, we’ll cover activation and cost functions. The video will also introduce you to the most popular Deep Learning platforms. We wrap it up with a demo in TensorFlow to predict if a person receives a salary above or below 50k. Now, let us get started and understand Deep Learning in detail.

Below topics are explained in this Deep Learning tutorial:
1. Applications of Deep Learning
2. What is Deep Learning
3. Why is Deep Learning important
4. What are Neural Networks
5. Activation function
6. Cost function
7. How do Neural Networks work
8. Deep Learning platforms
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow

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Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you’ll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial Neural Networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for Deep Learning and contains powerful tools to help you build and implement artificial Neural Networks.
Advancements in Deep Learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in Deep Learning models, learn to operate TensorFlow to manage Neural Networks and interpret the results. According to, the median salary for engineers with Deep Learning skills tops $120,000 per year.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement Deep Learning algorithms, understand Neural Networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional Neural Networks, recurrent Neural Networks, training deep networks and high-level interfaces
4. Build Deep Learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial Neural Networks
6. Troubleshoot and improve Deep Learning models
7. Build your own Deep Learning project
8. Differentiate between machine learning, Deep Learning and artificial intelligence

There is booming demand for skilled Deep Learning engineers across a wide range of industries, making this Deep Learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this Deep Learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in Deep Learning

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Thanks for watching the video. Cheers!

Sudeept Das says:

Can you please send me the code in

Sudeept Das says:

Hi , I am getting an error while creating the training model . Please help me in fixing the error.
Code: model.train(input_fn= get_input_fn(df_train,num_epochs=None, n_batch = 128, shuffle= False), steps=1000)
Error: ValueError: 'linear/linear_model/native country/weights/part_0/PartitionedInitializer' is not a valid scope name

Hüseyin Sadiç says:

At the end we found 'new' value like 625 and 1936. What does that mean and how can we interpret this results.

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