The AI Practitioner: Optimizing AI Solutions
Artificial Intelligence
| Expert
- 14 Videos | 38m 39s
- Includes Assessment
- Earns a Badge
Optimization is required for any AI model to deliver reliable outcomes in most of the use cases. AI Practitioners use their knowledge of optimization techniques to choose and apply various solutions and improve accuracy of existing models. In this course, you'll learn about advanced optimization techniques for AI Development, including multiple optimization approaches like Gradient Descent, Momentum, Adam, AdaGrad and RMSprop optimization. You'll examine how to determine the preferred optimization technique to use and the overall benefits of optimization in AI. Lastly, you'll have a chance to practice implementing optimization techniques from scratch and applying them to real AI models.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedefine AI optimization and its importance in relation to the AI Practitioner rolespecify the types of AI optimization and describe key differences in the approachesidentify key benefits of and improvements that can be achieved by AI optimizationdescribe the principle of Gradient Descent optimization in AI and cases in which it is useddescribe the principle of Stochastic Gradient Descent optimization in AI and specify cases in which it is useddescribe the principle of Momentum optimization in AI and specify cases in which it is used
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describe the principle of AdaGrad optimization in AI and specify cases in which it is useddescribe the principle of RMSprop optimization in AI and specify cases in which it is useddescribe the principle of Adam optimization in AI and specify cases in which it is useddescribe the principle of AdaMax optimization in AI and specify cases in which it is usedimplement Gradient Descent Optimization algorithm from scratch using Python libraries and describe how algorithm convergence achieves loss minimization goalimplement AdaGrad Optimization algorithm from scratch using Python libraries and specify formatting for inputs and outputs of the computationsummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 27sUP NEXT
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2.AI Optimization Overview2m 30s
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3.Types of AI Optimization3m
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4.Benefits of AI Optimization2m 35s
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5.Gradient Descent Optimization in AI3m 44s
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6.Stochastic Gradient Descent Optimization in AI2m 37s
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7.Momentum Optimization in AI2m 8s
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8.AdaGrad Optimization in AI2m 12s
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9.RMSprop Optimization in AI3m 7s
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10.Adam Optimization in AI3m 3s
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11.AdaMax Optimization in AI3m 10s
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12.Applying Gradient Descent Optimization3m 56s
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13.Applying AdaGrad Optimization4m 21s
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14.Course Summary50s
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