New course: Vibe Coding 101 with Replit

DeepLearningAI · Beginner ·💻 AI-Assisted Coding ·1y ago

Key Takeaways

Learns coding fundamentals with Replit in the Vibe Coding 101 course

Full Transcript

I'm excited to introduce Vibe coding 101 with repit built in partnership with replit and taught by Mikela kasta and Matt Palmer m is president of the company and Matt head of develop relations in this course you build and deploy two applications with the help of a coding agent if you're stting as a developer and maybe taken our AI python for B course and I want to build a larger project the repet environment will let you build and host projects quickly or if you're already experienced in coding this is a better time than ever to be a developer and coding agents like the one you use in this course can help you write debug and test your code much faster than ever before and I think they're making coding even more fun RIT eliminates many of the barriers that prevent users from building and deploying applications first of all RIT provides a powerful code editor and a universal package manager so you don't need to struggle with installation of P and JavaScript packages Source control is powered by git but we made it as seamless as possible for our users rapid offers buil in ke value stores databases and also deployment Solutions and now with rapit agent every person with no coding experience can create apps and deploy them in public it will take you only a few minutes to start even if you did not know many of the terms that just use you can still build an application mat will show you exactly how to do in this course in this course you'll create a search engine optimization or SEO analyzer in a head-to-head voting app a popular term is Vibe coding where we will lean into letting the coding agent do most of the heavy lifting letting you focus on the features of your app we'll maximize the capabilities of the agent but we will also show how some planning can improve your results you will learn some of the tips and tricks to get the best results along the way if you not you're using a coding assistant and if you want to rapidly build applications especially Standalone software prototypes I think you find the principles described in this course really useful so please go take this course [Music]

Original Description

Learn more: https://bit.ly/4hP6TPN Coding agents are changing the way software is built—helping you write, test, and refine code more quickly. But using these tools effectively takes more than just writing a prompt and hoping for the best. In our new free course, Vibe Coding 101 with Replit, taught by its President, Michael Catasta, and Matt Palmer, Head of Developer Relations, you’ll learn to build web apps collaboratively with an AI coding assistant in Replit’s cloud environment. You’ll go beyond one-shot prompting and learn practical techniques that help you plan, debug, and iterate with more control. By the end of the course, you’ll be able to: - Use coding agents effectively to build, host, and share web applications in Replit - Apply structured development practices like writing product requirement documents, creating wireframes, and crafting effective prompts to guide the coding process - Build and deploy two applications—a website performance analyzer and a voting app—while using the assistant to debug, customize, and strengthen your coding skills No prior app-building experience is required! Enroll now: https://bit.ly/4hP6TPN
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1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
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2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
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9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
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10 Gradient Checking Implementation Notes (C2W1L14)
Gradient Checking Implementation Notes (C2W1L14)
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11 Learning Rate Decay (C2W2L09)
Learning Rate Decay (C2W2L09)
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12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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13 Mini Batch Gradient Descent (C2W2L01)
Mini Batch Gradient Descent (C2W2L01)
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14 The Problem of Local Optima (C2W3L10)
The Problem of Local Optima (C2W3L10)
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15 Exponentially Weighted Averages (C2W2L03)
Exponentially Weighted Averages (C2W2L03)
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16 Tuning Process (C2W3L01)
Tuning Process (C2W3L01)
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17 Understanding Exponentially Weighted Averages (C2W2L04)
Understanding Exponentially Weighted Averages (C2W2L04)
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18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
Bias Correction of Exponentially Weighted Averages (C2W2L05)
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19 Gradient Descent With Momentum (C2W2L06)
Gradient Descent With Momentum (C2W2L06)
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20 Normalizing Activations in a Network (C2W3L04)
Normalizing Activations in a Network (C2W3L04)
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21 Hyperparameter Tuning in Practice (C2W3L03)
Hyperparameter Tuning in Practice (C2W3L03)
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22 Adam Optimization Algorithm (C2W2L08)
Adam Optimization Algorithm (C2W2L08)
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23 RMSProp (C2W2L07)
RMSProp (C2W2L07)
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24 Fitting Batch Norm Into Neural Networks (C2W3L05)
Fitting Batch Norm Into Neural Networks (C2W3L05)
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25 Why Does Batch Norm Work? (C2W3L06)
Why Does Batch Norm Work? (C2W3L06)
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26 Batch Norm At Test Time (C2W3L07)
Batch Norm At Test Time (C2W3L07)
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27 Softmax Regression (C2W3L08)
Softmax Regression (C2W3L08)
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28 Deep Learning Frameworks (C2W3L10)
Deep Learning Frameworks (C2W3L10)
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29 Neural Network Overview (C1W3L01)
Neural Network Overview (C1W3L01)
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30 Training Softmax Classifier (C2W3L09)
Training Softmax Classifier (C2W3L09)
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31 Why Deep Representations? (C1W4L04)
Why Deep Representations? (C1W4L04)
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32 Gradient Descent For Neural Networks (C1W3L09)
Gradient Descent For Neural Networks (C1W3L09)
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33 Neural Network Representations (C1W3L02)
Neural Network Representations (C1W3L02)
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34 TensorFlow (C2W3L11)
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)
Deep L-Layer Neural Network (C1W4L01)
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45 Random Initialization (C1W3L11)
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46 Other Regularization Methods (C2W1L08)
Other Regularization Methods (C2W1L08)
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47 Normalizing Inputs (C2W1L09)
Normalizing Inputs (C2W1L09)
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48 Derivatives Of Activation Functions (C1W3L08)
Derivatives Of Activation Functions (C1W3L08)
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49 Parameters vs Hyperparameters (C1W4L07)
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)
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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