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Deep Learning In 5 Minutes | What Is Deep Learning? | Deep Learning Explained Simply | Simplilearn



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This video on “What is Deep Learning” provides a fun and simple introduction to its concepts. We learn about where Deep Learning is implemented and move on to how it is different from machine learning and artificial intelligence. We will also look at what neural networks are and how they are trained to recognize digits written by hand. We further look at some popular applications of Deep Learning. So, let’s dive into the world of Deep Learning with this video.

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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 payscale.com, 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

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-Is-Deep-Learning-6M5VXKLf4D4&utm_medium=Tutorials&utm_source=youtube

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23 thoughts on “Deep Learning In 5 Minutes | What Is Deep Learning? | Deep Learning Explained Simply | Simplilearn
  1. Hi everyone, exactly a week ago, we conducted a contest in this video. The answer to the question is given below:
    The correct order of working for Neural Network with explanation:

    B. The weighted sum of inputs is calculated

    A. The Bias is added

    D. The result is fed to an activation function

    C. Specific Neuron is activated

    Explanation: In a neural network, each neuron in one layer is connected to other neurons in the corresponding layers. These connections carry random weights. The weighted sum of the inputs is calculated, and an additional input in the form of bias is added (w * x + b). The result of this is fed to an activation function. Based on a particular threshold value, only those neurons get activated that crosses the threshold value.

    We are pleased to announce the 3 lucky winners who got the right answer for our quiz:

    1. Divya Maskar
    2. Kartik Singhal
    3. Michael Ipinolu
    Congratulations to all the winners! They've won an Amazon voucher worth INR 500.

  2. the answer is B-A-D-C coz first the weights are calculated and the bias is added then this are given as inputs to activation record and specific neurons are activated : )

  3. The answer is C. Thanks for the video it makes some much sense now. 🙂
    B-A-D-C
    Very clearly explained. Good Didactic.
    The Answer in order of correctness is B.A.D.C

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