This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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.
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
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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.
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
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
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.
And 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:
Learn more at: https://www.simplilearn.com/
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Summary:
There are three parts in this presentation.
A. Why do we need Convolutional Neural Network
- Problems we face today
- Solutions for problems
B. LeNet Overview
- The origin of LeNet
- The result after using LeNet model
C. LeNet Techniques
- LeNet structure
- Function of every layer
In the following Github Link, there is a repository that I rebuilt LeNet without any deep learning package. Hope this can make you more understand the basic of Convolutional Neural Network.
Github Link : https://github.com/HiCraigChen/LeNet
LinkedIn : https://www.linkedin.com/in/YungKueiChen
Las aplicaciones de Inteligencia Artificial como Machine Learning y Deep Learning se han convertido en parte importante en nuestras vidas. Los productos que compramos, si somos o no aptos para un préstamo bancario, las películas o series que Netflix nos recomienda, coches autoconducidos, reconocimiento de objetos, etc; toda esa información es dirigida hacia nosotros por estos algoritmos.
En la actualidad, estos campos de estudio son los más apasionantes y retadores en computación debido a su alto nivel de complejidad y gran demanda en el mercado. En esta presentación vamos a conocer y aprender a diferenciar estos conceptos, ya que son herramientas inevitables para el mejoramiento de la vida humana.
A continuación, te presentamos algunos de los temas específicos que se expondrán:
- Contexto de ML y DL en Inteligencia Artificial.
- Machine Learning.
- Supervised Learning.
- Unsupervised Learning.
- Deep Learning.
- Artificial Neural Network.
- Convolutional Neural Networks.
- Aplicaciones en ML y DL.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
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.
And 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:
Learn more at: https://www.simplilearn.com/
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Summary:
There are three parts in this presentation.
A. Why do we need Convolutional Neural Network
- Problems we face today
- Solutions for problems
B. LeNet Overview
- The origin of LeNet
- The result after using LeNet model
C. LeNet Techniques
- LeNet structure
- Function of every layer
In the following Github Link, there is a repository that I rebuilt LeNet without any deep learning package. Hope this can make you more understand the basic of Convolutional Neural Network.
Github Link : https://github.com/HiCraigChen/LeNet
LinkedIn : https://www.linkedin.com/in/YungKueiChen
Las aplicaciones de Inteligencia Artificial como Machine Learning y Deep Learning se han convertido en parte importante en nuestras vidas. Los productos que compramos, si somos o no aptos para un préstamo bancario, las películas o series que Netflix nos recomienda, coches autoconducidos, reconocimiento de objetos, etc; toda esa información es dirigida hacia nosotros por estos algoritmos.
En la actualidad, estos campos de estudio son los más apasionantes y retadores en computación debido a su alto nivel de complejidad y gran demanda en el mercado. En esta presentación vamos a conocer y aprender a diferenciar estos conceptos, ya que son herramientas inevitables para el mejoramiento de la vida humana.
A continuación, te presentamos algunos de los temas específicos que se expondrán:
- Contexto de ML y DL en Inteligencia Artificial.
- Machine Learning.
- Supervised Learning.
- Unsupervised Learning.
- Deep Learning.
- Artificial Neural Network.
- Convolutional Neural Networks.
- Aplicaciones en ML y DL.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
Lecture conducted by me on Deep Learning concepts and applications. Discussed FNNs, CNNs, Simple RNNs and LSTM Networks in detail. Finally conducted a hands-on session on deep-learning using Keras and scikit-learn.
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
This Deep Learning interview questions and answers presentation will help you prepare for Deep Learning interviews. This presentation is ideal for both beginners as well as professionals who are appearing for Deep Learning, Machine Learning or Data Science interviews. Learn what are the most important Deep Learning interview questions and answers and know what will set you apart in the interview process.
Some of the important Deep Learning interview questions are listed below:
1. What is Deep Learning?
2. What is a Neural Network?
3. What is a Multilayer Perceptron (MLP)?
4. What is Data Normalization and why do we need it?
5. What is a Boltzmann Machine?
6. What is the role of Activation Functions in neural network?
7. What is a cost function?
8. What is Gradient Descent?
9. What do you understand by Backpropagation?
10. What is the difference between Feedforward Neural Network and Recurrent Neural Network?
11. What are some applications of Recurrent Neural Network?
12. What are Softmax and ReLU functions?
13. What are hyperparameters?
14. What will happen if learning rate is set too low or too high?
15. What is Dropout and Batch Normalization?
16. What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
17. Explain Overfitting and Underfitting and how to combat them.
18. How are weights initialized in a network?
19. What are the different layers in CNN?
20. What is Pooling in CNN and how does it work?
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.
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
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, and GANs, along with a simple yet complete neural network.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
23. INTRODUCTION
• Traditional pattern recognition models use hand-
crafted features and relatively simple trainable
classifiers.
• This approach has the following limitations:
• It is very tedious and costly to develop hand-crafted features
• These hand-crafted features are usually highly dependent on
one application, and cannot be transferred easily to other
applications – ie, they are not generalizable.
Hand-crafted
Feature
Extractor
“Simple”
Trainable
Classifier
Output
26. DEEP LEARNING
• Deep learning (a.k.a. representation learning)
seeks to learn rich hierarchical representations (i.e.
features) automatically through multiple stages of
a feature learning process.
Low-level
features
Output
Mid-level
features
High-level
features
Trainable
classifier
Feature visualization of convolutional net trained on ImageNet
(Zeiler and Fergus, 2013)
27. LEARNING HIERARCHICAL
REPRESENTATIONS
• Hierarchy of representations with increasing levels
of abstraction. Each stage is a kind of trainable
nonlinear feature transform.
• Image recognition
• Pixel → edge → shape → part → object
• Text
• Character → word → word group → clause → sentence
→ story
• Speech
• Sample → spectral band → sound→ …→ phoneme →
Low-level
features
Output
Mid-level
features
High-level
features
Trainable
classifier
Increasing levels of abstraction
29. THE MAMMALIAN VISUAL CORTEX IS
HIERARCHICAL
• It is good to be inspired by
nature, but not too much.
• We need to understand
which details are
important, and which
details are merely the
result of evolution.
• Each module in Deep
Learning transforms its
input representation into a
higher-level one, in a way
similar to the mammalian
visual cortex.
(van Essen and Gallant, 1994)
54. SUPERVISED LEARNING
• Convolutional Neural Network
• Sequence Modelling
• Why do we need RNN?
• What are RNNs?
• RNN Extensions
• What can RNNs can do?
55. CONVOLUTIONAL NEURAL NETWORK
• Input can have very high dimension. Using a fully-connected
neural network would need a large amount of parameters.
• Inspired by the neurophysiological experiments conducted
by [Hubel & Wiesel 1962], CNNs are a special type of neural
network whose hidden units are only connected to local
receptive field. The number of parameters needed by CNNs
is much smaller.
Example: 200x200 image
a) fully connected: 40,000
hidden units => 1.6
billion parameters
b) CNN: 5x5 kernel, 100
feature maps => 2,500
parameters
56. THREE STAGES OF A
CONVOLUTIONAL LAYER
1. Convolution stage
2. Nonlinearity: a nonlinear
transform such as rectified
linear or 𝐭𝐚𝐧𝐡
3. Pooling: output a summary
statistics of local input, such
as max pooling and average
pooling
57. CONVOLUTION OPERATION IN CNN
• Input: an image (2-D array) x
• Convolution kernel/operator(2-D array of learnable parameters):
w
• Feature map (2-D array of processed data): s
• Convolution operation in 2-D domains:
60. POOLING
• Common pooling operations:
• Max pooling: reports the maximum output within a
rectangular neighborhood.
• Average pooling: reports the average output of a
rectangular neighborhood (possibly weighted by
the distance from the central pixel).
61. DEEP CNN: WINNER
OF IMAGENET 2012
• Multiple feature maps per convolutional layer.
• Multiple convolutional layers for extracting features at different
levels.
• Higher-level layers take the feature maps in lower-level layers as
input.
(Alex et al., 2012)
62. SEQUENCE MODELLING
• Why do we need RNN?
• What are RNNs?
• RNN Extensions
• What can RNNs can do?
63. WHY DO WE NEED RNNS?
The limitations of the Neural network (CNNs)
• Rely on the assumption of independence among the
(training and test) examples.
• After each data point is processed, the entire state of the
network is lost
• Rely on examples being vectors of fixed length
We need to model the data with temporal or
sequential structures and varying length of
inputs and outputs
• Frames from video
• Snippets of audio
• Words pulled from sentences
64. Recurrent neural networks (RNNs) are connectionist models
with the ability to selectively pass information across sequence
steps, while processing sequential data one element at a time.
The simplest form of fully recurrent
neural network is an MLP with the
previous set of hidden unit activations
feeding back into the network along with
the inputs
ℎ 𝑡 = 𝑓𝐻 𝑊𝐼𝐻 𝑥 𝑡 + 𝑊𝐻𝐻ℎ(𝑡 − 1)
𝑦 𝑡 = 𝑓𝑂(𝑊𝐻𝑂ℎ(𝑡))
𝑓𝐻 and 𝑓𝑂 are the activation function for
hidden and output unit; 𝑊𝐼𝐻, 𝑊𝐻𝐻, and
𝑊𝐻𝑂 are connection weight matrices which
are learnt by training
Allow a ‘memory’ of previous inputs to persist in
the network’s internal state, and thereby
influence the network output
WHAT ARE RNNS?
65. An unfolded recurrent network. Each node represents a layer of network units at a single time
step. The weighted connections from the input layer to hidden layer are labelled ‘w1’, those from
the hidden layer to itself (i.e. the recurrent weights) are labelled ‘w2’ and the hidden to output
weights are labelled‘w3’. Note that the same weights are reused at every time step. Bias weights
are omitted for clarity.
WHAT ARE RNNS?
• The recurrent network can be converted into a feed-
forward network by unfolding over time
66. ℎ 𝑡 = 𝑓(𝑊 𝑥ℎ 𝑥 𝑡 + 𝑊ℎℎℎ 𝑡−1 + 𝑏ℎ)
ℎ 𝑡 = 𝑓(𝑊 𝑥ℎ 𝑥 𝑡 + 𝑊ℎℎℎ 𝑡−1 + 𝑏ℎ)
𝑦𝑡 = 𝑊ℎ𝑦ℎ 𝑡 + 𝑊ℎ𝑦ℎ 𝑡 + 𝑏 𝑦
training
sequence
forwards and
backwards to
two separate
recurrent
hidden layers
past and future context
determines the output
An unfolded
BRNN
RNN EXTENSIONS: BIDIRECTIONAL
RECURRENT NEURAL NETWORKS
Traditional RNNs only model the dependence of the current state
on the previous state, BRNNs (Schuster and Paliwal, 1997) extend
to model dependence on both past states and future states.
For example: predicting a missing word in a sequence you want to
look at both the left and the right context.
67. A gating mechanism of
the LSTM , which
generates the current
hidden state by the paste
hidden state and current
input ..It contains five
modules: input gate, new
memory cell, forget gate,
final memory generation,
and output gate.
RNN EXTENSIONS: LONG SHORT-TERM
MEMORY
The vanishing gradient problem prevents standard RNNs from learning
long-term dependencies. LSTMs (Hochreiter and Schmidhuber, 1997)
were designed to combat vanishing gradients through
a gating mechanism.
68. RNN EXTENSIONS:
LONG SHORT-TERM MEMORY
LSTMs contain information outside the normal flow
of the recurrent network in a gated cell. Information
can be stored in, written to, or read from a cell, much
like data in a computer’s memory. The cells learn
when to allow data to enter, leave or be deleted
through the iterative process of making guesses,
back-propagating error, and adjusting weights via
gradient descent.
Conclusions on LSTM
69. RNN EXTENSIONS:
LONG SHORT-TERM MEMORY
Why LSTM can combat the vanish gradient problem
LSTMs help preserve the error that can be back-
propagated through time and layers. By
maintaining a more constant error, they allow
recurrent nets to continue to learn over many time
steps (over 1000), thereby opening a channel to
link causes and effects remotely
70. What can RNNs can do?
Machine Translation Visual Question Answering
71. In machine translation, the input is a sequence
of words in source language, and the output is
a sequence of words in target language.
Encoder-decoder architecture for machine translation
Encoder: An
RNN to
encode the
input
sentence into
a hidden state
(feature)
Decoder: An RNN with
the hidden state of the
sentence in source
language as the input
and output the
translated sentence
MACHINE TRANSLATION
72. VQA: Given an image and a natural language
question about the image, the task is to provide
an accurate natural language answer
VISUAL QUESTION ANSWERING (VQA)
Picture from (Antol et al., 2015)
73. The output is to be conditioned on both image and
textual inputs. A CNN is used to encode the image and a
RNN is implemented to encode the sentence.
VISUAL QUESTION ANSWERING
75. AUTOENCODERS
An Autoencoder is a
feedforward neural network
that learns to predict the input
itself in the output.
𝑦(𝑖)
= 𝑥(𝑖)
• The input-to-hidden part
corresponds to an encoder
• The hidden-to-output part
corresponds to a decoder.
76. DEEP AUTOENCODERS
• A deep Autoencoder is
constructed by
extending the encoder
and decoder of
autoencoder with
multiple hidden layers.
• Gradient vanishing
problem: the gradient
becomes too small as
it passes back through
many layers
Diagram from (Hinton and Salakhutdinov, 2006)
79. TRAINING DENOISING
AUTOENCODER ON MNIST
The following pictures show the difference between the
resulting filters of Denoising Autoencoder trained on
MNIST with different noise ratios.
No noise (noise ratio=0%) noise ratio=30%
Diagram from (Hinton and Salakhutdinov, 2006)
80. DEEP REINFORCEMENT LEARNING
• Reinforcement Learning
• Deep Reinforcement Learning
• Applications
• Playing Atari Games
• AlphaGO
81. REINFORCEMENT LEARNING
What’s Reinforcement Learning?
Environment
Agent
{Observation, Reward} {Actions}
• Agent interacts with an environment and learns by maximizing
a scalar reward signal
• No labels or any other supervision signal.
• Previously suffering from hand-craft states or representation.
82. POLICIES AND VALUE FUNCTIONS
• Policy 𝜋 is a behavior function selecting actions given
states
𝑎 = 𝜋(s)
• Value function 𝑄 𝜋
(s,a) is expected total reward 𝑟 from
state s and action a under policy 𝜋
“How good is action 𝑎 in state 𝑠?”
83. APPROACHES TO
REINFORCEMENT LEARNING
• Policy-based RL
• Search directly for the optimal policy 𝛑∗
• Policy achieving maximum future reward
• Value-based RL
• Estimate the optimal value function 𝐐∗(s,a)
• Maximum value achievable under any policy
• Model-based RL
• Build a transition model of the environment
• Plan (e.g. by look-ahead) using model
86. ABOUT THE SPEAKER
• Tarun Sukhani has 16 years of both academic and industry experience as a data scientist over the
course of his career. Starting off as an EAI consultant in the USA, Tarun was involved in a number
of integration/ETL projects for a variety of Fortune 500 and Global 1000 clients, such as BP
Amoco, Praxair, and GE Medical Systems. While completing his Master's degree in Data
Warehousing, Data Mining, and Business Intelligence at Loyola University Chicago GSB in 2005,
Tarun also worked as a BI consultant for a number of Fortune 500 clients at Revere Consulting, a
Chicago-based boutique IT firm focusing on Data Warehousing/Mining projects. Within the
industry, he worked on ETL/Data Science/Machine Learning projects at Profitera, Experian, Atex,
E-Radar, ICarAsia, and Max Money. Some of the tools employed during his career included Oracle
BI, Informatica, Cognos, Business Objects, Pentaho, Apache Mahout, Spark MLib, Apache UIMA
and Temis. Tarun continues to work within the BI space, most recently focusing his time on
Deep/Reinforcement Learning projects within the Fintech sector.
• Tarun has worked on parametric statistical modeling as well within the Data Science and Big Data
Science space, using tools such as SciPy in Python and R and R/Hadoop for Big Data projects.
• Tarun is the principal Data Science trainer for iTrain Asia Singapore.
• Tarun has his own consulting firm - Abundent is a Digital Transformation and Big Data Analytics
company specializing in modernizing IT infrastructure and development practices across a wide
range of industries.
92. ASIA’S TOP DIGITAL TECH TRAINING POWERHOUSE.
TRAINING YOU TO MEET THE CHALLENGES OF TOMORROW HEAD ON
iTrain enables continuous brain gain through in-demand skills trainings that help
you stay ahead in the digital economy
iTrain (M) Sdn Bhd
C-L19-8, KL Trillion, 338 Jalan Tun Razak, 50400 Kuala Lumpur
Tel: +603 21661879 Email: info@itrain.com.my Web: www.itrain.com.my
93. UPCOMING TRAINING
INTRODUCTION TO DEEP LEARNING WITH NVIDIA GPUS
FEBRUARY 21ST - 23RD, 2018
MAGIC CYBERJAYA
9.00 AM - 5.00 PM
Supported by MaGIC with 50% early bird discount. Limited to 20 seats
only. First come first serve
Registration Link:
https://events.bizzabo.com/deeplearningintro