Deep learning has been getting a considerable amount of attention in the digital world, which is why we have developed this course in deep learning with python to help you incorporate deep learning techniques in your projects using the python language. No matter how minimal of a knowledge you have in deep learning, if you have the interest for it, this course is all you need!
This course will start off by walking you through the concept of deep learning, along with its libraries, to familiarise you with it. Backpropagation, which is used to calculate derivatives, will be thoroughly delved in through this course, along with an insight into deep learning with Theano to optimize a simple model in pure Theano. You will also be enlightened on keras and how it can be used to make Theanos even easier to use.
After learning how to load and reuse pre-trained models using Theano through this course. You will taught on the differences between recurrent and convolutional layers to comprehend how to train a sentiment analysis model for text. The Google’s learning library, Tensorflow, will also be duly discussed through this course, along with guidelines on how to caption it to further enhance your knowledge on it. By the end of this course, you will have a well-grounded knowledge on deep learning to incorporate it in your present and future projects.
Global Edulink offers the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in an IT or corporate environment. You can study at your own pace at Global Edulink and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules and free resources which include Free CV writing pack, free career support and course demo to make your learning experience more enriching and rewarding.
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lectures at any time.
This course is aimed at individuals looking to master python to develop applications involving deep learning techniques
Learners must be age 16 or over and should have basic understanding of the English Language, numeracy, literacy and ICT.
The course is assessed online with a final, multiple-choice test, which is marked automatically. You will know instantly whether you have passed the course.
This certificate will improve your candidature for a range of jobs in the web development industry. You can also use this certificate to progress in your career by demanding for a salary increment or job promotion from your employer. Listed below are few of the jobs this certificate will benefit you in, along with the average UK salary per annum according to https://www.payscale.com/career-path-planner,
|Section 1: Head First into Deep Learning|
|1.1. The Course Overview||00:00:00|
|1.2. What Is Deep Learning?||00:00:00|
|1.3. Open Source Libraries for Deep Learning||00:00:00|
|1.4. Deep Learning “Hello World!” Classifying the MNIST Data||00:00:00|
|Section 2: Backpropagation and Theano for the Rescue|
|2.1. Introduction to Backpropagation||00:00:00|
|2.2. Understanding Deep Learning with Theano||00:00:00|
|2.3. Optimizing a Simple Model in Pure Theano||00:00:00|
|Section 3: Keras – Making Theano Even Easier to Use|
|3.1. Keras Behind the Scenes||00:00:00|
|3.2. Fully Connected or Dense Layers||00:00:00|
|3.3. Convolutional and Pooling Layers||00:00:00|
|Section 4: Solving Cats Versus Dogs|
|4.1. Large Scale Datasets, ImageNet, and Very Deep Neural Networks||00:00:00|
|4.2. Loading Pre-trained Models with Theano||00:00:00|
|4.3. Reusing Pre-trained Models in New Applications||00:00:00|
|Section 5: "for" Loops and Recurrent Neural Networks in Theano|
|5.1. Theano “for” Loops – the “scan” Module||00:00:00|
|5.2. Recurrent Layers||00:00:00|
|5.3. Recurrent Versus Convolutional Layers||00:00:00|
|5.4. Recurrent Networks –Training a Sentiment Analysis Model for Text||00:00:00|
|Section 6: Bonus Challenge and TensorFlow|
|6.1. Bonus Challenge – Automatic Image Captioning||00:00:00|
|6.2. Captioning TensorFlow – Google’s Machine Learning Library||00:00:00|
|Mock Exam : Deep Learning with Python||00:40:00|
|Final Exam : Deep Learning with Python||00:40:00|