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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:

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4. Statisticians with an interest in Deep Learning

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Thank you so much sir your explanation is excellent & well organized the order of answer is BADC

SIR I have one request can you give me this presentation for future reference plzzz

B then A D and finally C

Answer: BADC

BADC

B. A. D. C

the result of the quiz is in order od A-B-D-C

C

BADC

B A D C order of implementation of cnn

B,A,D,C

B A C D

B-A-D-C

Oh my mischief ;it's actually BADC

The correct order according to me is CBAD

"Learning" implies "Understanding" among people. What we call Deep Learning is a misnomer. It ought to be called Deep Sorting. And Deep Matching for convolutional Neural Networks.

Context adds meaning to a behavior.

Thanks for the crisp explanation, i understood it completely. Alas , i don't think my current laptop meets the requirements for such heavy work.

B, A, D, C

CBAD

B)Weighted Sum of inputs is calculated

A)Bias is added

D)Result is fed into activation function

C)Specific Neuron is activated

b

I think B->A->D->C. Right?

Thank you for this. I'm curious about the statistical angle of the weighted sums and bias. Are these akin to regression coefficients and the bias associated with underfitting/overfitting that is adjusted with regularization in machine learning?

B

A

D

C

BADC

C-B-A-D

The answer is B. The weighted sum of the inputs is calculated

A. The bias is added

C. Specific neuron is activated

D. The result is fed to an activation function

BADC

Thank you for the response and providing correct answer. it's enlightening. Keep up.

A->B->D->C

Answered many questions for me. Thank you!

Correct ans.

B A D C

Thank you. I am fully satisfied with this video. Thank you friend. The answer is BADC. Also, congratulations winners

Why do you have a hand in the video? Is obviously not actually drawing the pictures or text lol

B-A-D-C

Basic phyton please

I think the answer was B,A,D,C

Quiz:

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