Deep Learning Project Course

Brisbane.AI

<H2>DEEP LEARNING PROJECT COURSE</H2>
<P>This course is designed to take participants from an introduction of artificial intelligence<BR>(AI) and machine learning concepts and tools, to a practical understanding of deep learning. The full<BR>course begins with an intensive 2-day introductory course, followed by a 12-week technical project<BR>course.<BR></P>
<H3>WHO IS THE COURSE FOR?</H3>
<P>The course is for anyone in early stage development of their AI skills, whether it be for a<BR>startup, as part of a company’s upskilling move, or for personal projects and understanding. The<BR>course is geared towards developing a practical understanding of deep learning and, as such,<BR>participants learn by applying their skills to their own real-life project.</P>
<H3>PREREQUISITES</H3>
<P>It is highly recommended to have a basic understanding of coding, especially Python. The intensive<BR>course will refresh all other necessary skills.</P>
<H3>COURSE DATES</H3>
<P>2-day intensive: 9am - 5pm 24-25th March<BR>12-weel project course: Tuesdays 6-8pm, 27th March - 12th June</P>
<H3>COST</H3>
<P>2-day intensive (alpha): free<BR>12-week project course (beta): $100</P>
<P><BR></P>
<H2>INTRODUCTORY TECHNICAL AI COURSE</H2>
<H3>DAY 1 OVERVIEW:<BR>INTRODUCTION TO AI</H3>
<P>The introduction will provide a general overview of the large field of AI, with a particular focus on<BR>machine learning and deep learning. Each of the fields will be further explained through their past,<BR>current and possible future use cases. Using intuitive examples, we will explore how AI learns and the<BR>data it requires to do so.</P>
<H3>MACHINE LEARNING &amp; DEEP LEARNING TECHNIQUES</H3>
<P>Machine learning techniques, such as SVM and K nearest neighbour, will be initially explored and<BR>contrasted to newer deep learning techniques. The three main modes of learning, namely supervised,<BR>unsupervised, and reinforcement learning, will be discussed in terms of their use cases. Using<BR>supervised learning as a reference, more technical concepts of neural network layers will be discussed,<BR>as well as how these combine to achieve different architectures.</P>
<H3>DAY 2 OVERVIEW:<BR>MATHEMATICAL CONCEPTS IN AI</H3>
<P>This refresher will cover the core concepts in mathematics on which AI is built. Participants will learn<BR>how useful math concepts, such as matrix operations and logarithms, will allow for a deep<BR>understanding of AI. These concepts will be demonstrated with a high-level walk-through of how<BR>functions such as backpropagation, gradient descent, and activation functions work to make deep<BR>learning possible.</P>
<H3>AI TOOLKIT</H3>
<P>Turning to practical implementation, the software and toolboxes that will be essential for deep<BR>learning will be introduced. This includes Python, its most useful libraries, and related programmes<BR>such as Jupyter. The use of established frameworks for deep learning, including TensorFlow and<BR>PyTorch, as well as different cloud service providers (Amazon Web Services, Microsoft Azure, Google<BR>Cloud) will be explored.</P>
<H3>OPTIONAL EXTRA: AWS SETUP</H3>
<P>AWS professional staff will present the AI tools available on their platform and guide participants<BR>through account setup and AWS navigation.</P>
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<H2>TECHNICAL AI PROJECT COURSE</H2>
<H3>OBJECTIVES:<BR>BUILD A PRACTICAL UNDERSTANDING - FAST.AI COURSE</H3>
<P>Participants will work through the Fast.AI course, guided by continued mentoring throughout the<BR>course to help successfully complete the hands-on exercises. Mentors will also provide extra<BR>conceptual explanations through mini presentations throughout the course.<BR>Topics include:<BR> Image classification<BR> Convolutional Neural Networks (CNN)<BR> Fine tuning neural networks<BR> Embeddings and data<BR> Natural Language Processing (NLP)<BR> Recurrent Neural Networks (RNN)</P>
<H3>BUILD YOUR OWN DEEP LEARNING PROJECT</H3>
<P>Participants will form teams at the start of the course with others who share interests in a similar<BR>project field. Participants need not arrive with a fully developed project idea but will work with their<BR>team and mentors to develop a project throughout the 12-week programme.<BR>The projects can be on anything in which deep learning would be a good analysis option. While<BR>participants are encouraged to pursue their own projects and use relevant data, online data sets are<BR>provided to ensure that every participant can complete a project. The mentoring team will be<BR>following along with the progress and aiding the teams when necessary.<BR>Finally, we finish the project course with an AWS Pitch Night! This is the chance to show off how far<BR>participants came in just 12 weeks.. and win prizes!<BR></P>
<P><BR>This course is being provided with sponsorship from the awesome team at AWS. </P>

AWS, Level 8

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Deep Learning Project Course

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