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Neural Network Tutorial | Artificial Neural Network Tutorial | Deep Learning Tutorial | Simplilearn

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This Neural Network tutorial will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a usecase implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into this video to understand how a neural network actually work.

Below topics are explained in this neural network Tutorial:

1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case – To classify between the photos of dogs and cats

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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.

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

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
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4. Statisticians with an interest in deep learning

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44 thoughts on “Neural Network Tutorial | Artificial Neural Network Tutorial | Deep Learning Tutorial | Simplilearn”

  1. Machine Learning is the Future and yours can begin today. Comment below with you email to get our latest Machine Learning Career Guide. Let your journey begin.
    Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you. Also, if you would like to have the dataset for implementing the use case shown in the video, please comment below and we will get back to you. Thanks watching the video. Cheers !!

  7. I did all the steps as mentioned.
    classifier.fit_generator(training_set,

    steps_per_epoch = 4000,

    epochs = 25,

    validation_data = test_set,

    validation_steps = 10)
    But in prediction, I am always getting cat even tough I have give dog's photo.
    Please let me know what could have gone wrong? The above code took around more than 15 hours to run. Do I need to increase the steps_per_epoch or any other parameter??

  17. Thanks for sharing the Machine learning algorithm. I am a beginner, the tutorial is very helpful for me to understand Machine learning. Could you please share the Datasets used algorithms and python codes. My email address is kuhamba@yahoo.com. Thanking in advance.

  18. Hi, great vid – thank you! I’m a recent subscriber 👍, are you able to send the data set and code (so I can copy and paste to Python) to my email? Would be great to try this one out 👍. David.p.marshall@hotmail.co.uk thank you in advance 👍

  20. One thing I love about this youtube channel is its concern and responsive nature. I have viewed so many series of this channel and I always get astonished looking at the focus of the team to solve people's doubt and replying. Kudos for that. You people are doing a great job.

  21. i was working on this project on ibm cloud and i stuck on the step where we provide path for training data please comment how to set a path of file in IBM cloud.

  22. there was an error in 2nd last cell stating ('Some keys in session_kwargs are not supported at this time: %s', dict_keys(['matrics'])) can you please temm me what it means i an new to this.

  23. I greatly like the video, just like all the others that I have watched in this playlist. Kindly send me the dataset that you used in this video

  33. A waste of time like other tutorials, at the beginning everything is clear but when you start coding random numbers and functions popup from nowhere, the resolution image at the beginning is 28×28, and after that, you changed it to 64, WHY!!

    *Why most the values are the result of 2^n !?
    *What is Dense !?
    *Why always binary examples, I believe switching to three outputs will make a huge difference
    *If we have 33 images and a batch size of 32 what will happen?
    *Why the target size is 64,64 it's only a cat or a dog
    ….

    Is there a hidden federation that prevents people from giving the required information when it comes to AI?

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