Deep Learning With Python Illustrated Guide For Beginners And Intermediates: The Future Is Here!
- Length: 198 pages
- Edition: 1
- Language: English
- Publisher: Independently published
- Publication Date: 2018-11-06
- ISBN-10: 1729388159
- ISBN-13: 9781729388150
- Sales Rank: #2634009 (See Top 100 Books)
☆★Deep Learning With Python Illustrated Guide For Beginners And Intermediates “Learn By Doing Approach☆★
☆★The Future Is Here! Keras with Tensorflow Backend☆★
Deep learning originates from a broader family of machine learning, including supervised and unsupervised learning
The python programming language is one of the most popular languages for programmers in the 21st century.
This programming language has been a fundamental cornerstone in a lot of technology we use today. -Things we take for granted on a daily basis. Developing both desktop and web applications, and more interestingly enough has been used to accomplish many artificial intelligence feats.
The world is constantly changing and evolving and it appears machine learning could be the way of the future! As we speak technology on a massive scale is being developed to replace mundane and repetitive tasks humans interface with everyday through the use of “deep learning”. Ultimately, this means less human errors and a more efficient ways of operating for many corporations.
You can potentially become the next big startup! Develop software, web development tools and many more online ventures!
Companies That Use Python Currently:
- Dropbox
- Yahoo
- IBM
- Mozilla
- Quora
Why Programmers Choose To Use Python?
- Readable & Maintainable Code
- Dynamic Type System
- Compatible with Major Platforms and Systems
- Robust Standard Library
- Simplifies Complex Software Development
- Test Driven Development
- Highly Sought After Skill-Set For Employers
Invest in your knowledge base by buying your copy right now. The greatest investment you can make is an investment in yourself! Python will pave the road of technological advancements and very much so shape the world we live in. Become apart of this global progression towards advanced technology through the use of “deep learning”.
What You’ll Learn
- What is deep learning
- Theory of Artificial Neural Network
- Artificial Neural Network with Keras
- Image Classification with Convolutional Neural Network
- Environment Setup
- Natural Language Processing
- Evaluating and Tuning the ANN
- Sequence Modeling
- And, much, much more!
By the end of this book you will have grasped the fundamentals of python programming & deep learning! There is also illustrations to go along to help you understand and retain the info on a much more profound level. Picture diagrams have scientifically proven to accelerate the learning process by over 120%!
Prerequisites Introduction Where to Find the Datasets? Chapter 1 Introduction What is deep learning? History of Deep Learning Advantages of Deep Learning Disadvantages of Deep Learning Applications of Deep Learning Conclusion Chapter 2 Environment Setup Downloading and Installing Anaconda Running your First Program Chapter 3 Theory of Artificial Neural Network How The Human Brain Works Perceptron The Activation Function Multilayer Perceptron How ANN Learns? Conclusion Chapter 4 Implementing Artificial Neural Network with Keras Importing Required Libraries Importing the Dataset Data Analysis Data Preprocessing One Hot Encoding the Output Scaling the Data Importing Keras and Subsequent Libraries Adding Input and Hidden Layers Adding the Output Layers Training the Neural Network Making Predictions and Evaluating the Algorithm Chapter 5 Evaluating and Tuning the ANN Cross-Validation Implementing Cross-Validation with Keras 1. Importing Required Libraries 2. Importing the Dataset 3. Data Analysis 4. Data Preprocessing 5. One Hot Encoding the Output 6. Scaling the Data Grid Search Implementing Grid Search with Keras Chapter 6 Introduction to Convolutional Neural Network How Computers See Images? The Convolution Operation ReLu Operation Pooling Flattening Fully Connected Layer Conclusion Chapter 7 Image Classification with Convolutional Neural Network Classifying Cats and Dogs Images Chapter 8 Introduction to Recurrent Neural Network Types of Memories in Human Brain What is an RNN? Applications of an RNN Steps of a Recurrent Neural Network Conclusion Chapter 9 Time Series Analysis with RNN Downloading the Data Data Analysis Importing the Libraries Loading the Dataset Scaling the Data Data Preprocessing Creating RNN (LSTM) Testing and Making Predictions Evaluating the Algorithm Conclusion Chapter 10 Natural Language Processing with RNN (LSTM) Text Classification using RNN (LSTM) LSTM Architecture LSTM in Practice Spam and Ham Email Classification Chapter 11 The sequence to Sequence Modeling with LSTM What is Sequence to Sequence Modelling? Sequence to Sequence Architecture Creating a Chatbot Using Sequence to Sequence Model Conclusion What to do next?
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