​•             Understand what is convolution.
•             Understand how convolution is going to apply in audio effects.
•             Understand how convolution is going to apply in image effects.
•             Implement Gaussian edge and blur detection in code.
•             Implement a simple echo effect in code
•             Understand how convolution helps in image classification.
•             Understand and basics and core of the architecture of a convolutional neural network (CNN)
•             Implement a convolutional neural network in TensorFlow
•             Implement a convolutional neural network in Theano.

​This course is all about deep learning, how to use deep learning various computer visions using convolutional neural networks. Convolution neural networks are the state of the art when it comes to image classification. This neural network beats vanilla deep networks at tasks like MNIST.
 
In this course we are going to take a closer look at the StreetView House Number (SVHN) dataset - which uses bigger color images at various angles - so things are going to get tougher both in terms of the difficulty of the classification task and computationally as well. But will show and teach you that convolutional neural networks, or CNNs, are much more than capable of handling this task!
 
We are going to go in-depth on CNNs; convolution is a central part of this type of neural network and thus contains a huge value in software industry. It has more applications than you might imagine, such as modeling artificial organs like the heart and the pancreas. We are going to teach you how to build convolutional filters that can be applied to audio, like the echo effect, build filters for image effects, like the Gaussian blur and edge detection.

Understand the intuition behind Artificial Neural Networks
Apply Artificial Neural Networks in day to day projects
Understand the basics behind Convolutional Neural Networks
Apply Convolutional Neural Networks in practice
Apply Recurrent Neural Networks in regular projects
Understand the basics behind Self-Organizing Maps, Boltzmann Machines, and AutoEncoders

This decade is all about Artificial intelligence. Artificial intelligence is growing exponentially and gaining popularity worldwide. Self-driving cars are clocking up millions of miles and update its database every second, IBM Watson is diagnosing patients better than armies of doctors in the hospitals and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. Artificial intelligence has a wide variety of usage nowadays 
 
But the further AI advances, the more complex problems and challenges occur, and only with the help of Deep Learning we can solve such complex problems and that's why it’s the heart of Artificial intelligence.


​Why Deep Learning A-Z?
 
1. ROBUST STRUCTURE
The first and foremost thing we focused on is giving the course a robust and simple structure at the same time. Deep Learning is very broad and complex and to navigate through this maze you need a clear cut and global vision about the topics and the fundamentals.
 
That's why the tutorials are grouped into two volumes, representing the two fundamental branches of Deep Learning: Unsupervised Deep Learning and Supervised Deep Learning. With each volume focusing on three different algorithms, this is the best structure to mastering Deep Learning in an effective way.
 
2. INTUITION TUTORIALS
Our main focus is an intuitive *feel* for the concepts behind Deep Learning algorithms. So many courses and books just bombard you with a lot of math theory and coding... But they forget to explain, why and how these things done
 
With our online tutorials you are able to understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see a comprehensive overall development in your problem solving department.
 
3. EXCITING PROJECTS
Throughout this session you will be working on Real-World datasets and projects to be able to cope up and solve Real-World business problems easily and more efficiently. In this course we will solve following problems.
•             Boltzmann Machines to create a Recommender System
•             Recurrent Neural Networks to predict Stock Prices
•             Convolution Neural Networks for Image Recognition
•             Artificial Neural Networks to solve a Customer Churn problem
•             Self-Organizing Maps to investigate Fraud

This introduces the field of deep learning using the Python language (which is now a day very interesting language) and the most powerful library of Keras. Moreover, this book is written by Keras creator & also Google AI researcher, François Chollet. This book will be going to builds your understanding through very intuitive explanations and practical examples

Additionally, Purchase of this print book includes a free eBook in PDF format and also available in Kindle and in ePub formats from Manning Publications.

Officially Published - 28 October 2017
Total no of pages-384
About the Technology

We all know that Machine learning has made wonderful progress in the recent few years. From unusable speech and image recognition, human accuracy, machines that couldn't beat a serious Go player, defeating a world champion. Do you know! That behind this progress is deep learning. Deep learning is the technology behind photo tagging systems at Facebook and Google.so, whenever you upload any group pic it automatically tags your friends which are in the pic, self-driving cars, speech recognition systems on your smartphone, text classification, question answering, text-to-speech, and optical character recognition, etc. Also applicable to a wide range of artificial intelligence problems.


Deep Learning with Python is structured around a series of examples of practical codes which illustrate each new concept introduced and also demonstrate best practices. After ending the book, you will become a Keras expert and, you'll have the knowledge and hands-on skills and becomes capable to apply deep learning in your own projects.

What is inside the book (Deep Learning Course)?
•          First principles of Deep learning (Deep Learning Introduction)
•          You can Set-up your own deep-learning environment
•          You can create Image-classification models and many other models also
•          Learn about how deep learning is used in text and sequences
•          Text and image generation Neural style transfer
•          Examples like practical codes are also available (Deep Learning Frameworks)
•          Introduction to Keras library in detailed
•          In the end, you have a clear difference between Deep Learning and AI
•        You will be going to explore challenging concepts and practice with different applications in computer vision, natural language processing, and generative models.

Author’s Name: Adam Gibson, Josh Patterson
Year of Publishing: August 2017
Total number of Pages in Book: 532
Language: English
Category of the Book: Data Mining

About the Book:

Recently interest in machine learning has reached the highest point, lofty expectations often scuttle projects before they get very far. You may also think that How can machine learning especially deep neural networks can make a real difference in your organization? The hands-on guide (which is provided) not only provides the most practical information available on the subject, but also helps you to get started for building efficient deep learning networks.

Authors have provided the theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing workflows on production-class. From examples of real world, one can very smoothly learn deep learning algorithms and strategies for having training in deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

• 1. In general, Dive into machine learning concepts as well as in deep learning
• 2. Understand how neural network fundamentals evolved deep learning neural networks
• 3. Including Convolutional and Recurrent also explore the major deep network architectures
• 4. Learn Mapping in the right problem to specific deep networks
• 5. Walk through the specific deep network architectures and fundamentals of tuning general neural networks
• 6. For different data types with DataVec, DL4J’s workflow tool Use vectorization techniques
• 7. You will be going to learn how to use DL4J natively on Spark and Hadoop

Author: Douwe Osinga
Categories: Computers-Cybernetics: Artificial Intelligence
Publishing Year: 2018
Language: English
Total no of Pages: 252

You should not have to be frightened with Deep learning. Recently, many analyses have done on this machine-learning method which required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter into this field. With the recipes in this cookbook, you'll be going to learn how to solve deep-learning problems for generating and classifying text, music, images and many more.

Description of the book:

Every chapter consists of several topics needed to complete a single project, such as music recommending system, training, and many more. Moreover, the author provides many techniques in every chapter to help you if you’re stuck. Examples are also written in Python code which is available on GitHub as a set of Python notebooks. In the end, you will get the following knowledge:
•          Build an inverse image search service by reusing pre-trained networks
•          Based on Wikipedia links build a movie recommender system
•          Detect index song collections and music styles
•          Visualizing the internal state of AI and learn how AIs see the world
•          To serve real users you can create applications
•          Compare how autoencoders, LSTMs and GANs, generate icons
•          To calculate text similarity by Using word embedding
•          For pieces of text build a model to suggest emoji

>  The application for neural style transfer
>  The state-of-the-art computer vision topics 
>  Usages of detection algorithms for objects like SSD
>  You will learn usages of state of the art convolutional neural nets such as Inception, ResNet and VGG
>  You will learn the  application of transfer learning 

This course is very different, and you will be just impressed by seeing how much material has already covered in this book. This is one of the most exciting courses and it really shows how fast and how far deep learning has come over the years.

In this course, you’ll see how we can turn a convolutional neural network (CNN) into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You were going to bridge the gap between the basic convolutional neural network(CNN) architecture, to modern, novel architectures such as VGG, ResNet, and Inception. 


You were also going to apply these to the images of blood cells, and create a system that is a better medical expert than others. This brings up a fascinating idea: that the doctors of the future are not humans, but robots. Use of CNN's is also called a neural style transfer (Another viral computer vision task).
This course is very different, and you will be just impressed by seeing how much material has already covered in this book. This is one of the most exciting courses and it really shows how fast and how far deep learning has come over the years.
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You were going to bridge the gap between the basic CNN architecture, to modern, novel architectures such as VGG, ResNet, and Inception. 


You were also going to apply these to the images of blood cells, and create a system that is a better medical expert than others. This brings up a fascinating idea: that the doctors of the future are not humans, but robots. Use of CNN's is also called a neural style transfer (Another viral computer vision task).

>  Develop deep learning neural networks with Tensorflow and Keras.
> Perform python machine learning to classify images and  sentiment analysis in python using ddeep learning in python.
> Understand the basics of  reinforcement learning - and build a Pac-Man bot as a deep learning example.
> Perform python machine learning at massive scale with deep learning framework Apache Spark's MLLib.
> Develop a  deep learning tensorflow movie recommender system using item-based and user-based collaborative filtering.

The content of course comes from stringent analysis of most  requirements in data scientist job listings from the biggest tech employers. Topics covered includes  machine learning , artificial intelligence , difference between deep learning vs machine learning,  tensorflow examples etc.

• Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras.
• Convolutional neural networks with TensorFlow and Keras.
• Deep learning with python
• Sentiment analysis  with python
• Image recognition and classification with python
• Regression analysis
• K-Means Clustering
• Principal Component Analysis
• Train/Test and cross validation
• Bayesian Methods
• Decision Trees and Random Forests
• Support Vector Machines
• Reinforcement Learning
• Collaborative Filtering
• Ensemble Learning
• Term Frequency / Inverse Document Frequency
• Experimental Design and A/B Tests

​

> Be able to harness the power and capabilities Of R  programming language for practical Data Science and able to perform deep learning with R and neural networks  with R.
> Gain mastery on the theory and concepts of Artificial Neural Networks (ANN) and understand  deep learning neural networks (DNN).
>  Execute Deep Learning with R and understand various  deep learning algorithms.
> Master the execution of both  Artificial Neural Networks (ANN) &  Deep Learning Neural Networks (DNN) using the H2o  deep learning R package .
> Execute and master Deep Learning Neural Networks (DNN)   Machine Learning classification & regression problems in R with a live deep learning example.

After learning this course you will be able perform  data reading & cleaning  to finally implementing powerful Artificial Neural Networks (ANN) and deep learning applications and perform evaluation  using deep learning ai in R.

Also other benefits:

• Introduction to powerful R deep learning packages like h2o and MXNET.
• Introduction to deep learning neural networks(DNN), convolution neural networks (CNN) and recurrent neural networks (RNN).
• Mastery to apply to apply these deep learning frameworks to real life deep learning examples including credit card fraud data, tumor data, images among others for classification and regression applications.  

Learning this course will provide you the entrance  to the entire Neural Networks in R and Deep Learning Kingdom!

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