Company: Packt Publishing Author: Abilash Nelson Full Title: Deep Learning And Neural Networks Using Python - Keras: The Complete Beginners Guide Year: 2019 Language: English Genre: Educational: Big data Skill Level: Beginner Price: €18.19 - Files: MP4 Time: 11:06:27 Video: AVC, 1920 x 1080 (1.778) at 30.000 fps, 600 kbps Audio: AAC at 126 Kbps, 2 channels, 44.1 KHz Deep learning and data science using a Python and Keras library - The complete guide from beginner to professional. The world has been obsessed with the terms "machine learning" and "deep learning" recently. We use these technologies every day, with or without our knowledge. Ranging from Google suggestions, to translations, ads, movie recommendations, friend suggestions, sales and customer experiences. There are tons of other applications too so there’s no wonder that deep learning and machine learning specialists, along with data science practitioners, are the most sought-after talent in the current technology world. But the problem is that, when you think about learning these technologies, there is a common misconception that it’s a prerequisite to study lots of maths, statistics, and complex algorithms. It’s almost like someone making you believe that you must learn the working of an internal combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user-friendly control pedals extending from the engine like the clutch, brake, accelerator, steering wheel, and so on. And with a bit of experience, you can easily drive a car. The basic know-how about the internal working of the engine is of course an added advantage while driving a car, but it’s not mandatory. Similarly, in our deep learning course, we have a perfect balance between learning the basic concepts and the implementation of the built-in deep learning classes and functions from the Keras library using the Python programming language. These classes, functions and APIs are just like the control pedals from the car engine that we can use easily to build an efficient deep-learning model. Let’s see how this course is organized and an overview about the list of topics included. Overall, this is a basic to advanced crash course in deep learning neural networks and convolutional neural networks using Keras and Python. Once completed, it’s sure to sky-rocket your current career prospects as this in-demand skill is the technology of the future. There is a day in the near future itself, when deep learning models will out-perform human intelligence. So be ready and let’s dive into the world of thinking machines. Style and Approach: Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest. Learn: ✓ Deep learning ✓ Neural networks using Python Features: ✓ Learn data science using a Python and Keras Library ✓ Learn convolutional neural networks using Python Lessons: 01. Course Intro and Table of Contents 02. Deep Learning Overview 03. Chosing ML or DL for your project 04. Preparing Your Computer 05. Python Basics 06. Installing Theano Library and Sample Program to Test 07. TensorFlow library Installation and Sample Program to Test 08. Keras Installation and Switching Theano and TensorFlow Backends 09. Multi-Layer Perceptron Concepts 10 Training Neural Network - Steps and Terminology 11. First Neural Network with Keras - Understanding Pima Indian Dataset 12. Training and Evaluation Concepts Explained 13. Pima Indian Model - Steps Explained 14. Pima Indian Model - Performance Evaluation 15. Understanding Iris Flower Dataset 16. Developing the Iris Flower Model 17. Understanding the Sonar Returns Dataset 18. Developing the Sonar Returns Model 19. Sonar Model Perfomance Improvement 20. Understanding the Boston Housing Dataset 21. Developing the Boston Housing Baseline Model 22. Boston Performance Improvement 23. Save the Trained Model as JSON File (Pima Indian Dataset) 24. Save and Load Model as YAML File - Pima Indian Dataset 25. Load and Predict using the Pima Indian Model 26. Save Load and Predict using Iris Flower Dataset 27. Save Load and Predict using Sonar Dataset 28. Save Load and Predict using Boston Dataset 29. Checkpointing Models 30. Plotting Model Behaviour History 31. Dropout Regularisation 32. Learning Rate Schedule using Ionosphere Dataset 33. Convolutional Neural Networks – Introduction 34. Downloading the MNIST Handwritten Digit Dataset 35. Multi-Layer Perceptron Model using MNIST 36. Convolutional Neural Network Model using MNIST 37. Convolutional Neural Network Model using MNIST - Part 2 38. Large CNN using MNIST 39. Load Save and Predict using MNIST 40. Introduction to Image Augmentation using Keras 41. Augmentation using Sample Wise Standardization 42. Augmentation using Feature Wise Standardization and ZCA Whitening 43. Augmentation using Rotation and Flipping 44. Saving Augmentation for MNIST 45. CIFAR-10 Object Recognition Dataset - Understanding and Loading 46. Simple CNN using CIFAR-10 Dataset 47. Simple CNN using CIFAR-10 Dataset - Part 2 48. Simple CNN using CIFAR-10 Dataset – Coding 49. Train and Save CIFAR-10 Model 50. Load and Predict using CIFAR-10 CNN Model Our members see more. Join us! ------------- Our members see more. Join us!