Short course on Gemini CLI: Code & Create with an Open-Source Agent

DeepLearningAI · Intermediate ·🤖 AI Agents & Automation ·5mo ago

Key Takeaways

Short course on Gemini CLI: Code & Create with an Open-Source Agent

Original Description

Learn more: https://bit.ly/4qJapAx Join this new short course on Gemini CLI, taught by Jack Wotherspoon, Developer Advocate at Google. Gemini CLI is an open-source agentic coding assistant that works from your terminal, giving it access to your local filesystem, development tools, and cloud services. This lets you delegate complex workflows—from building web features to creating marketing materials—through high-level instructions while the agent autonomously plans and executes multiple steps. In this course, you'll apply Gemini CLI to software development and creative tasks by building features for an AI conference. You'll develop a website session catalog, create a data dashboard combining local and cloud data sources, and generate social media content from recordings. You'll master context management, integrate MCP servers, and orchestrate across multiple services with Gemini CLI extensions. In detail, you'll: - Install Gemini CLI, explore its open-source architecture, and learn how it reasons through tasks, calls tools, and leverages the command line to power complex workflows. - Automate coding workflows with GitHub Actions for pull request reviews and apply Gemini CLI to creative tasks like generating social media content from conference recordings and organizing multimedia study materials. - Manage the context window with gemini.md files and extend capabilities by connecting Model Context Protocol (MCP) servers and Gemini CLI extensions to orchestrate across local files, cloud databases, and services like GitHub, Canva and Google Workspace. - Build complete software features including a conference session catalog and an interactive data dashboard that combines local CSV files with cloud data sources—experiencing the full development workflow from research to testing. - Use Gemini CLI as a learning tool by organizing course materials on your filesystem and grounding responses with web search for accurate, cited information. Whether you're prototyping appl
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Playlist

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1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
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)
Activation Functions (C1W3L06)
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36 Explanation For Vectorized Implementation (C1W3L05)
Explanation For Vectorized Implementation (C1W3L05)
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37 Getting Matrix Dimensions Right (C1W4L03)
Getting Matrix Dimensions Right (C1W4L03)
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38 Understanding Dropout (C2W1L07)
Understanding Dropout (C2W1L07)
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39 Building Blocks of a Deep Neural Network (C1W4L05)
Building Blocks of a Deep Neural Network (C1W4L05)
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40 Why Non-linear Activation Functions (C1W3L07)
Why Non-linear Activation Functions (C1W3L07)
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41 Computing Neural Network Output (C1W3L03)
Computing Neural Network Output (C1W3L03)
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42 Backpropagation Intuition (C1W3L10)
Backpropagation Intuition (C1W3L10)
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43 Train/Dev/Test Sets (C2W1L01)
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)
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)
Vectorizing Across Multiple Examples (C1W3L04)
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51 What does this have to do with the brain? (C1W4L08)
What does this have to do with the brain? (C1W4L08)
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52 Dropout Regularization (C2W1L06)
Dropout Regularization (C2W1L06)
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53 Vanishing/Exploding Gradients (C2W1L10)
Vanishing/Exploding Gradients (C2W1L10)
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54 Basic Recipe for Machine Learning (C2W1L03)
Basic Recipe for Machine Learning (C2W1L03)
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55 Bias/Variance (C2W1L02)
Bias/Variance (C2W1L02)
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56 Forward Propagation in a Deep Network (C1W4L02)
Forward Propagation in a Deep Network (C1W4L02)
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57 Weight Initialization in a Deep Network (C2W1L11)
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)
Regularization (C2W1L04)
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60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
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