New course with Google: Pair Programming with a Large Language Model

DeepLearningAI · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

The course covers pair programming with large language models, using the Palm API, and prompting LLMs for coding assistance, in collaboration with Google.

Full Transcript

I'm excited to introduce pair programming with a large language model built in partnership with Google and taught by Florence Moroni you may have heard of prompting a large language model with instructions to say build a web page in this course you learn about even more powerful ways that you can use an LM to be your coding partner to help you analyze debug and improve your code your instructor Lawrence is lead AI Advocate at Google so his full-time job is to help developers build AI applications and so he has experience with many Rich systematic ways that developers are using LMS for coding assistance there's a lot more that developers can get out of an LM than you might think thank you so much Andrew in this course you're going to learn how to use the Palm API and you're going to learn how to prompt the llm to provide insights into your code where it can act as an assisted developer assistant to the developer this is true I hope you enjoy the course

Original Description

Enroll today: https://bit.ly/46fh1Mj Learn how to use LLMs to enhance, debug, understand, and document your code in this new course built in collaboration with Google, and taught by Laurence Moroney. By using LLMs as pair programmers, you can get quick and effective assistance on a wide range of tasks, from catching bugs to integrating complete code snippets. In this course, you’ll gain access to the PaLM API and get hands-on experience that will prepare you to use an LLM to: - Simplify and improve your code - Write test cases - Debug and refactor your code - Reduce technical debt - Explain and document complex code written in any coding language Ready to take the next step toward becoming a more productive software engineer? Learn more: https://bit.ly/46fh1Mj
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 9 Gradient Checking (C2W1L13)
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 10 Gradient Checking Implementation Notes (C2W1L14)
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 11 Learning Rate Decay (C2W2L09)
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 12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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 13 Mini Batch Gradient Descent (C2W2L01)
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 14 The Problem of Local Optima (C2W3L10)
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 15 Exponentially Weighted Averages (C2W2L03)
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 16 Tuning Process (C2W3L01)
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 17 Understanding Exponentially Weighted Averages (C2W2L04)
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 18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
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 19 Gradient Descent With Momentum (C2W2L06)
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 20 Normalizing Activations in a Network (C2W3L04)
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 21 Hyperparameter Tuning in Practice (C2W3L03)
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 22 Adam Optimization Algorithm (C2W2L08)
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 23 RMSProp (C2W2L07)
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 24 Fitting Batch Norm Into Neural Networks (C2W3L05)
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 25 Why Does Batch Norm Work? (C2W3L06)
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 26 Batch Norm At Test Time (C2W3L07)
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 27 Softmax Regression (C2W3L08)
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 28 Deep Learning Frameworks (C2W3L10)
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 29 Neural Network Overview (C1W3L01)
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 30 Training Softmax Classifier (C2W3L09)
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 31 Why Deep Representations? (C1W4L04)
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 32 Gradient Descent For Neural Networks (C1W3L09)
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 33 Neural Network Representations (C1W3L02)
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 34 TensorFlow (C2W3L11)
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 35 Activation Functions (C1W3L06)
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 36 Explanation For Vectorized Implementation (C1W3L05)
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 37 Getting Matrix Dimensions Right (C1W4L03)
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 38 Understanding Dropout (C2W1L07)
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 39 Building Blocks of a Deep Neural Network (C1W4L05)
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 40 Why Non-linear Activation Functions (C1W3L07)
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 41 Computing Neural Network Output (C1W3L03)
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 42 Backpropagation Intuition (C1W3L10)
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 43 Train/Dev/Test Sets (C2W1L01)
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 44 Deep L-Layer Neural Network (C1W4L01)
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 45 Random Initialization (C1W3L11)
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 46 Other Regularization Methods (C2W1L08)
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 47 Normalizing Inputs (C2W1L09)
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 48 Derivatives Of Activation Functions (C1W3L08)
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 49 Parameters vs Hyperparameters (C1W4L07)
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 50 Vectorizing Across Multiple Examples (C1W3L04)
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 51 What does this have to do with the brain? (C1W4L08)
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 52 Dropout Regularization (C2W1L06)
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 53 Vanishing/Exploding Gradients (C2W1L10)
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 54 Basic Recipe for Machine Learning (C2W1L03)
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 55 Bias/Variance (C2W1L02)
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 56 Forward Propagation in a Deep Network (C1W4L02)
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 57 Weight Initialization in a Deep Network (C2W1L11)
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 58 Numerical Approximations of Gradients (C2W1L12)
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 59 Regularization (C2W1L04)
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 60 Why Regularization Reduces Overfitting (C2W1L05)
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This course teaches how to use large language models as pair programmers to enhance, debug, and document code, using the Palm API and effective prompting techniques. Students will learn how to leverage LLMs to improve their coding skills and productivity. The course is taught by Laurence Moroney, lead AI Advocate at Google.

Key Takeaways
  1. Enroll in the course
  2. Learn about the Palm API
  3. Understand how to prompt LLMs for coding assistance
  4. Practice using LLMs for code analysis and debugging
  5. Apply LLMs to improve code quality and productivity
💡 LLMs can be used as powerful coding partners to provide quick and effective assistance with code analysis, debugging, and improvement.

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