Deep Learning Online Course by Frank Kane
| Learning Experience | 8.9 |
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Deep Learning Online Course: Get hands-on with hotter technology. Frank Kane demystifies the exciting field of Deep Learning and Artificial Neural Networks.
About the Deep Learning Online Course
It’s hard to imagine a hotter technology than deep learning, artificial intelligence, and artificial neural networks. If you’ve got some Python experience under your belt, this Deep Learning Online Course “Deep Learning and Neural Networks with Python” by Frank Kane will demystify this exciting field with all the major topics you need to know. So, let’s begin with a background on Deep Learning and Artificial Neural networks and why Python is the preferred programing language.
What is Deep learning?
Deep Learning is a subset of machine learning inspired by the structure and function of the brain called artificial neural networks. At its simplest, deep learning trains a computer to perform human-like tasks, such as recognizing speech, identifying images, or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.
What is an Artificial Neural Network?
As the “neural” part of their name suggests, they are brain-inspired systems that are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns that are far too complex or numerous for a human programmer to extract and teach the machine to recognize.
This has been possible by the arrival of a technique called “backpropagation,” which allows networks to adjust their hidden layers of neurons in situations where the outcome doesn’t match what the creator is hoping for — like a network designed to recognize dogs, which misidentifies a cat, for example.
Another important advance has been the arrival of deep learning neural networks, in which different layers of a multilayer network extract different features until it can recognize what it is looking for. (Source: on ANN)
What kind of tasks can a neural network do?
Neural networks can do much more from making cars drive autonomously on the roads to generating shockingly realistic CGI faces, to machine translation, to fraud detection, to reading our minds, to recognizing when a cat is in the garden and turning on the sprinklers; neural nets are behind many of the biggest advances in A.I.
Why Python?
From development to deployment and maintenance, Python helps developers be productive and confident about the software they’re building. Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language.
This Deep Learning online course covers
- Artificial Neural Networks
- Multi-Layer Perceptions
- Tensorflow
- Keras
- Convolutional Neural Networks
- Recurrent Neural Networks
In addition to the class project, the learner will get hands-on with some smaller activities and exercises:
- Building neural networks for handwriting recognition
- Learning how to predict a politician’s political party based on their votes
- Performing sentiment analysis on real movie reviews
- Interactively constructing deep neural networks and experimenting with different topologies
A few hours is all it takes to get up to speed and learn what all the hype is about.
Syllabus
- Introduction to the Deep learning online course
- History of Artificial neural network
- Hands-on in the Tensorflow playground
- Deep learning details
- Introduction to Tensorflow
- Using Tensorflow for handwriting recognition
- Introducing Keras
- Using Keras to learn Political Affiliations
- Convolutional Neural Networks (CNN’s)
- Using CNN’s for handwriting recognition
- Recurrent Neural Networks (RNN’s)
- Using RNN’s for Sentiment Analysis
- Transfer learning
- The Ethics of Deep learning
- Deep learning project (intro., solution and more)
Project Description
As a final project for the deep learning course, you will create an artificial neural network using Keras that classifies mammogram results as benign or malignant.
Get more info. on Deep learning course
Note: Your review matters
If you have already done this course, kindly post your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Description
About the Deep Learning Online Course
It’s hard to imagine a hotter technology than deep learning, artificial intelligence, and artificial neural networks. If you’ve got some Python experience under your belt, this Deep Learning Online Course “Deep Learning and Neural Networks with Python” by Frank Kane will demystify this exciting field with all the major topics you need to know. So, let’s begin with a background on Deep Learning and Artificial Neural networks and why Python is the preferred programing language.
What is Deep learning?
Deep Learning is a subset of machine learning inspired by the structure and function of the brain called artificial neural networks. At its simplest, deep learning trains a computer to perform human-like tasks, such as recognizing speech, identifying images, or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing.
What is an Artificial Neural Network?
As the “neural” part of their name suggests, they are brain-inspired systems that are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns that are far too complex or numerous for a human programmer to extract and teach the machine to recognize.
This has been possible by the arrival of a technique called “backpropagation,” which allows networks to adjust their hidden layers of neurons in situations where the outcome doesn’t match what the creator is hoping for — like a network designed to recognize dogs, which misidentifies a cat, for example.
Another important advance has been the arrival of deep learning neural networks, in which different layers of a multilayer network extract different features until it can recognize what it is looking for. (Source: on ANN)
What kind of tasks can a neural network do?
Neural networks can do much more from making cars drive autonomously on the roads to generating shockingly realistic CGI faces, to machine translation, to fraud detection, to reading our minds, to recognizing when a cat is in the garden and turning on the sprinklers; neural nets are behind many of the biggest advances in A.I.
Why Python?
From development to deployment and maintenance, Python helps developers be productive and confident about the software they’re building. Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These add to the overall popularity of the language.
This Deep Learning online course covers
- Artificial Neural Networks
- Multi-Layer Perceptions
- Tensorflow
- Keras
- Convolutional Neural Networks
- Recurrent Neural Networks
In addition to the class project, the learner will get hands-on with some smaller activities and exercises:
- Building neural networks for handwriting recognition
- Learning how to predict a politician’s political party based on their votes
- Performing sentiment analysis on real movie reviews
- Interactively constructing deep neural networks and experimenting with different topologies
A few hours is all it takes to get up to speed and learn what all the hype is about.
Syllabus
- Introduction to the Deep learning online course
- History of Artificial neural network
- Hands-on in the Tensorflow playground
- Deep learning details
- Introduction to Tensorflow
- Using Tensorflow for handwriting recognition
- Introducing Keras
- Using Keras to learn Political Affiliations
- Convolutional Neural Networks (CNN’s)
- Using CNN’s for handwriting recognition
- Recurrent Neural Networks (RNN’s)
- Using RNN’s for Sentiment Analysis
- Transfer learning
- The Ethics of Deep learning
- Deep learning project (intro., solution and more)
Project Description
As a final project for the deep learning course, you will create an artificial neural network using Keras that classifies mammogram results as benign or malignant.
Get more info. on Deep learning course
Note: Your review matters
If you have already done this course, kindly post your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Specification:
- Skillshare
- Sundog Education
- Online Course
- Self-paced
- Intermediate
- Less Than 24 Hours
- Paid Course (Paid certificate)
- English
- Deep learning Keras Machine learning Natural language processing TensorFlow
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