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Translation proofreading activities
UIUC CS241 System Programming Chinese Handout
How to participate: github.com/apachecn/ui…
Overall progress: github.com/apachecn/ui…
Project warehouse: github.com/apachecn/ui…
Claim: 1/78, proofread: 0/78
| chapter | contributors | The progress of |
|---|---|---|
| # Informal vocabulary | ||
| #Piazza: When and how to ask for help | ||
| Programming skills, Part 1 | ||
| System programming short stories and songs | ||
| C programming, Part 1: Introduction | @blue-bird1 | |
| C programming, part 2: text input and output | ||
| C programming, Part 3: Frequently asked Questions | ||
| C programming, Part 4: Strings and structures | ||
| C programming, Part 5: Debugging | ||
| C programming, review problems | ||
| Processes, Part 1: Introduction | ||
| Bifurcation, part 1: Introduction | ||
| Forks, Part 2: Fork, Exec, etc | ||
| Process control, part 1: Using signal wait macros | ||
| Progress review questions | ||
| Memory, Part 1: An introduction to heap memory | ||
| Memory – Part 2: Implementing a memory allocator | ||
| Memory, Part 3: Shredding stack example | ||
| Memory review problems | ||
| Pthreads, Part 1: Introduction | ||
| Pthreads, part 2: Usage in practice | ||
| Pthreads, Part 3: Parallel problems (Bonus) | ||
| Pthread review questions | ||
| Synchronization, Part 1: Mutex | ||
| Synchronization, part 2: Calculating semaphores | ||
| Synchronization, part 3: Using mutex and semaphore | ||
| Synchronization, part 4: critical region problems | ||
| Synchronization, part 5: Conditional variables | ||
| Synchronization, part 6: Obstacles to implementation | ||
| Synchronization, part 7: reader writer problems | ||
| Synchronization, Part 8: Annular buffer example | ||
| Synchronous review problems | ||
| Deadlocks, Part 1: Resource allocation diagrams | ||
| Deadlocks, part 2: Deadlock conditions | ||
| Deadlock, Part 3: Dining Philosophers | ||
| Deadlock review problem | ||
| Virtual memory, Part 1: Introduction to virtual memory | ||
| Pipelines, Part 1: Introduction to pipelines | ||
| Piping – Part 2: Piping programming secrets | ||
| Files, Part 1: Using files | ||
| Scheduling, part 1: scheduling process | ||
| Scheduling, part 2: Scheduling processes: algorithms | ||
| The IPC review questions | ||
| POSIX, Part 1: Error handling | ||
| Networking, Part 1: Introduction | ||
| Networking, part 2: Using getaddrInfo | ||
| Networking, Part 3: Building a simple TCP client | ||
| Networking, Part 4: Building a simple TCP server | ||
| Networking, part 5: Closing ports, reusing ports, and other tips | ||
| Networking, part 6: Creating a UDP server | ||
| Networks, Part 7: Nonblocking I O, SELECT (), and epoll | ||
| RPC, Part 1: Introduction to remote procedure calls | ||
| Network review problems | ||
| File systems, Part 1: Introduction | ||
| File systems, Part 2: Files are inodes (everything else is just data…) | ||
| File systems, part 3: permissions | ||
| File systems, part 4: Using directories | ||
| File systems, part 5: virtual file systems | ||
| File systems, part 6: Memory-mapped files and shared memory | ||
| File systems. Part 7: Extensible and reliable file systems | ||
| File systems, Part 8: Removing preinstalled malware from Android devices | ||
| File systems, Part 9: Disk block examples | ||
| File system review questions | ||
| Process control, part 1: Using signal wait macros | ||
| Signals, part 2: signals to be processed and signal masks | ||
| Signalling, part 3: enhancing signalling | ||
| Signals, part 4: signals | ||
| Signal review problem | ||
| Test subject | ||
| C programming: review questions | ||
| Multithreaded programming: review questions | ||
| Concept of synchronization: review questions | ||
| Memory: review questions | ||
| Pipes: Review questions | ||
| File systems: Review questions | ||
| Network: review questions | ||
| Signal: Review questions | ||
| System programming jokes |
Cython 3.0 英 文 版
How to participate: github.com/apachecn/cy…
Overall progress: github.com/apachecn/cy…
Project warehouse: github.com/apachecn/cy…
Claim: 0/37, proofread: 0/37
| chapter | contributors | The progress of |
|---|---|---|
| Cython – overview | ||
| Install Cython | ||
| Build the Cython code | ||
| Faster code through static typing | ||
| Basic tutorial | ||
| Call C function | ||
| Use the C library | ||
| Extension type (aka.cdef class) | ||
| PXD file | ||
| Caveats | ||
| Profiling | ||
| Unicode and passing strings | ||
| Memory allocation | ||
| Pure Python mode | ||
| Use NumPy | ||
| Using Python arrays | ||
| Further reading | ||
| Related work | ||
| Addendum: Install MinGW on Windows | ||
| Language foundation | ||
| Extension type | ||
| Special methods for extending types | ||
| Share declarations between Cython modules | ||
| Connect to external C code | ||
| Source files and compilations | ||
| Early binding speed | ||
| Use C ++ in Cython | ||
| Fusion type (template) | ||
| Port the Cython code to PyPy | ||
| Limitations | ||
| The difference between Cython and Pyrex | ||
| Type in memory view | ||
| Implement buffer protocol | ||
| Use parallelism | ||
| Debug your Cython program | ||
| Cython for NumPy users | ||
| Pythran acts as a Numpy back end |
Numba 0.44 中文 版
How to participate: github.com/apachecn/nu…
Overall progress: github.com/apachecn/nu…
Project warehouse: github.com/apachecn/nu…
Claim: 1/79, proofread: 1/79
| chapter | contributors | The progress of |
|---|---|---|
| 1. User manual | – | – |
| 1.1. Numba’s about 5 minute guide | @saltball | 100% |
| 1.2. An overview of the | ||
| 1.3. The installation | ||
1.4. use@jitCompiling Python code |
||
1.5. use@generated_jitSpecialize flexibly |
||
| 1.6. Create Numpy generic functions | ||
| 1.7. Compile Python classes with @jitClass | ||
1.8. use@cfuncCreate the C callback |
||
| 1.9. Pre-compile code | ||
1.10. use@jitAutomatic parallelization |
||
1.11. use@stencilA decorator |
||
| 1.12. Callbacks from JIT code to the Python interpreter | ||
| 1.13. Performance tip | ||
| 1.14. Thread layer | ||
| 1.15. Troubleshooting and hints | ||
| 1.16. Q&A | ||
| 1.17. The sample | ||
| 1.18. Talks and tutorials | ||
| 2. Refer to the manual | – | – |
| 2.1. Type and signature | ||
| 2.2. Instantaneous compiling | ||
| 2.3. To compile | ||
| 2.4. utilities | ||
| 2.5. The environment variable | ||
| 2.6. Supported Python features | ||
| 2.7. Supported NumPy functionality | ||
| 2.8. Deviations from Python semantics | ||
| 2.9. Floating trap | ||
| 2.10. Python 2.7 End-of-life plan | ||
| Numba for CUDA Gpus | – | – |
| 3.1. An overview of the | ||
| 3.2. Write a CUDA kernel | ||
| 3.3. Memory management | ||
| 3.4. Write device functions | ||
| 3.5. Python features supported in CUDA Python | ||
| 3.6. Supported atomic operations | ||
| 3.7. Random number generation | ||
| 3.8. Equipment management | ||
| 3.10. The sample | ||
| 3.11. Debugging CUDA Python using CUDA emulators | ||
| 3.12. The GPU to reduce | ||
| 3.13. CUDA Ufuncs and generalized Ufuncs | ||
| 3.14. Share CUDA memory | ||
| 3.15. CUDA array interface | ||
| 3.16. CUDA FAQs | ||
| CUDA Python Reference | – | – |
| 4.1. CUDA host API | ||
| 4.2. CUDA kernel apis | ||
| 4.3. Memory management | ||
| Numba for AMD ROC Gpus | – | – |
| 5.1. An overview of the | ||
| 5.2. Write the HSA kernel | ||
| 5.3. Memory management | ||
| 5.4. Write device functions | ||
| 5.5. Supported atomic operations | ||
| 5.6. The agent | ||
| 5.7. ROC Ufuncs and generalized Ufuncs | ||
| 5.8. The sample | ||
| 6. Expand the Numba | ||
| 6.1. Advanced extension API | ||
| 6.2. Low-level extension API | ||
| 6.3. Example: Interval type | ||
| 7. Developer manual | – | – |
| 7.1. Contribution to the Numba | ||
| 7.2. Numba building | ||
| 7.3. Polymorphic dispatch | ||
| 7.4. Notes on generators | ||
| 7.5. Notes about Numba Runtime | ||
| 7.6. Use Numba Rewrite Pass for fun and optimization | ||
| 7.7. Real-time variable analysis | ||
| 7.8. listed | ||
| 7.9. Template annotation | ||
| 7.10. Notes on custom pipes | ||
| 7.11. Environment object | ||
| 7.12. Hashing considerations | ||
| 7.13. Numba Project roadmap | ||
| 8. Numba enhancement recommendations | ||
| 9. The glossary |
Scrapy 1. Scrapy 2.
How to participate: github.com/apachecn/sc…
Overall progress: github.com/apachecn/sc…
Project warehouse: github.com/apachecn/sc…
Claim: 0/44, translation: 0/44
| chapter | proofreader | The progress of |
|---|---|---|
| Introduction to the | ||
| Scrapy at a glance | ||
| The installation guide | ||
| Scrapy tutorial | ||
| The instance | ||
| Command line tool | ||
| Spider | ||
| The selector | ||
| project | ||
| Item loader | ||
| Scrapy shell | ||
| Pipeline project | ||
| Feed export | ||
| Request and response | ||
| Link extractor | ||
| Set up the | ||
| exceptions | ||
| Logging | ||
| Statistical data set | ||
| Send an email | ||
| Remote Login to the Console | ||
| The Web service | ||
| Q&A | ||
| Debug the spiders | ||
| Spiders contracts | ||
| Common practice | ||
| General crawler | ||
| Use your browser’s developer tools to crawl | ||
| Debugging memory leaks | ||
| Download and process files and images | ||
| The deployment of spiders | ||
| AutoThrottle extension | ||
| Benchmarking | ||
| Operations: Pause and resume crawling | ||
| Architecture Overview | ||
| Downloader middleware | ||
| Spiders middleware | ||
| extension | ||
| Core API | ||
| signal | ||
| Item exporter | ||
| Release notes | ||
| Contributing to Scrapy | ||
| Version control and API stability |
A hundred pages short book on machine Learning
How to participate: github.com/apachecn/ml…
Overall progress: github.com/apachecn/ml…
Project warehouse: github.com/apachecn/ml…
Claim: 10/12, translation: 1/12
| chapter | contributors | The progress of |
|---|---|---|
| Zero, preface, | @PEGASUS1993 | 100% |
| Introduce a, | @PEGASUS1993 | |
| Symbols and definitions | @PEGASUS1993 | |
| Third, basic algorithm | @Rachel-Hu | |
| Four, linear algorithm analysis | @P3n9W31 | |
| 5. Basic practice | @chengchengbai | |
| Neural networks and deep learning | @Everfighting | |
| 7. Questions and answers | ||
| 8. Advanced practice | ||
| 9. Unsupervised learning | @onlyonewater | |
| Other forms of learning | @kjlintong | |
| Xi. Conclusion | @kjlintong |
Collection of Short Stories
How to participate: github.com/apachecn/mi…
Overall progress: github.com/apachecn/mi…
Project warehouse: github.com/apachecn/mi…
About convolutional neural networks: Claim: 2/12, proofread: 2/12
| chapter | contributors | The progress of |
|---|---|---|
| About convolutional neural networks | – | – |
| 1 | @daewis | 100% |
| 2.1.1-2.1.3 | @daewis | 100% |
| 2.1.4-2.1.6 | ||
| 2.2.1 | ||
| 2.2.2, 2.2.3 | ||
| 2.3-2.4 | ||
| 3.1 | ||
| 3.2 | ||
| 3.3 | ||
| 3.4-3.5 | ||
| 4.1 | ||
| 4.2 |
JavaScript for Impatient Programmers
How to participate: github.com/apachecn/im…
Overall progress: github.com/apachecn/im…
Project warehouse: github.com/apachecn/im…
Claim: 32/42, proofread: 31/42
| chapter | contributors | The progress of |
|---|---|---|
| 1. About this Book (ES2019 Edition) | @YouWillBe | 100% |
| 2. FAQ: This book | @huangzijian888 | 100% |
| 3. History and evolution of JavaScript | ||
| 4. Common problems: JavaScript | ||
| An overview of the 5. | @kj415j45 | 100% |
| 6. Grammar | @lq920320 | 100% |
7. Print messages on the console (console.*) |
@lq920320 | 100% |
| 8. Assertions API | @lq920320 | 100% |
| 9. Getting started with quizzes and exercises | @so-hard | 100% |
| Variables and assignments | @so-hard | 100% |
| Values of 11. | @lq920320 | 100% |
| 12. The operator | @wizardforcel | 100% |
13. The valueundefinedandnull |
@wizardforcel | 100% |
| 14. Boolean value | @wizardforcel | 100% |
| 15. Digital | @wizardforcel | 100% |
16. Math |
@wizardforcel | 100% |
| 17. Unicode – Brief Introduction (Advanced) | @wizardforcel | 100% |
| 18. The string | @wizardforcel | 100% |
| 19. Use template literals and tag templates | @wizardforcel | 100% |
| Symbols 20. | @wizardforcel | 100% |
| Control flow statement | @wizardforcel | 100% |
| 22. Exception handling | ||
| 23. Callable values | ||
| 24. The module | ||
| 25. Single object | ||
| 26. Prototype chains and classes | @lq920320 | 100% |
| 27. Iterate synchronously | @lq920320 | 100% |
28. Array (Array) |
@52admln | 100% |
| 29. Typed Arrays: Handling binary data (advanced) | ||
30. Mapping (Map) |
@so-hard | 100% |
31. WeakMaps (WeakMap) |
||
32. (Set) |
@liuyepiaoxiang | 100% |
33. WeakSets (WeakSet) |
||
| 34. Deconstruction | @Kavelaa | 100% |
| 35. Synchro Generator (Advanced) | ||
| 36. Asynchronous programming in JavaScript | @Kavelaa | 100% |
| 37. Promise of asynchronous programming | @iChrisJ | 100% |
| 38. Asynchronous functions | @iChrisJ | 100% |
39. Regular expressions (RegExp) |
@iChrisJ | 100% |
40. Date (Date) |
@facebesidewyj | 100% |
41. Create and parse JSON (JSON) |
@xdyushenli | |
| 42. Where are the rest of the chapters? | @wizardforcel | 100% |
Seaborn 0.9 中文 版
How to participate: github.com/apachecn/se…
Overall progress: github.com/apachecn/se…
Project warehouse: github.com/apachecn/se…
Identification: 64/74, translation: 51/74
| The serial number | chapter | The translator | The progress of |
|---|---|---|---|
| 1 | An introduction to seaborn | @yiran7324 | 100% |
| 2 | Installing and getting started | @neolei | 100% |
| 3 | Visualizing statistical relationships | @JNJYan | 100% |
| 4 | Plotting with categorical data | @hold2010 | 100% |
| 5 | Visualizing the distribution of a dataset | @alohahahaha | 100% |
| 6 | Visualizing linear relationships | @friedhelm739 | |
| 7 | Building structured multi-plot grids | @keyianpai | 100% |
| 8 | Controlling figure aesthetics | @P3n9W31 | 100% |
| 9 | Choosing color palettes | @Modrisco | 100% |
| 10 | seaborn.relplot | @Stuming | |
| 11 | seaborn.scatterplot | @tututwo | |
| 12 | seaborn.lineplot | @tututwo | |
| 13 | seaborn.catplot | @LIJIANcoder97 | 100% |
| 14 | seaborn.stripplot | @LIJIANcoder97 | 100% |
| 15 | seaborn.swarmplot | @LIJIANcoder97 | 100% |
| 16 | seaborn.boxplot | @FindNorthStar | 100% |
| 17 | seaborn.violinplot | @FindNorthStar | 100% |
| 18 | seaborn.boxenplot | @FindNorthStar | 100% |
| 19 | seaborn.pointplot | @FindNorthStar | 100% |
| 20 | seaborn.barplot | @melon-bun | |
| 21 | seaborn.countplot | @Stuming | 100% |
| 22 | seaborn.jointplot | @Stuming | |
| 23 | seaborn.pairplot | @Stuming | |
| 24 | seaborn.distplot | @hyuuo | 100% |
| 25 | seaborn.kdeplot | @hyuuo | 100% |
| 26 | seaborn.rugplot | @P3n9W31 | 100% |
| 27 | seaborn.lmplot | @P3n9W31 | 100% |
| 28 | seaborn.regplot | @P3n9W31 | 100% |
| 29 | seaborn.residplot | @P3n9W31 | 100% |
| 30 | seaborn.heatmap | @hyuuo | 100% |
| 31 | seaborn.clustermap | ||
| 32 | seaborn.FacetGrid | @hyuuo | 100% |
| 33 | seaborn.FacetGrid.map | @sfw134 | 100% |
| 34 | seaborn.FacetGrid.map_dataframe | @sfw134 | 100% |
| 35 | seaborn.PairGrid | @sfw134 | |
| 36 | seaborn.PairGrid.map | @sfw134 | |
| 37 | seaborn.PairGrid.map_diag | @sfw134 | |
| 38 | seaborn.PairGrid.map_offdiag | @sfw134 | |
| 39 | seaborn.PairGrid.map_lower | @sfw134 | |
| 40 | seaborn.PairGrid.map_upper | @sfw134 | |
| 41 | seaborn.JointGrid | ||
| 42 | seaborn.JointGrid.plot | ||
| 43 | seaborn.JointGrid.plot_joint | ||
| 44 | seaborn.JointGrid.plot_marginals | ||
| 45 | seaborn.set | @lbllol365 | 100% |
| 46 | seaborn.axes_style | @lbllol365 | 100% |
| 47 | seaborn.set_style | @lbllol365 | 100% |
| 48 | seaborn.plotting_context | ||
| 49 | seaborn.set_context | ||
| 50 | seaborn.set_color_codes | ||
| 51 | seaborn.reset_defaults | ||
| 52 | seaborn.reset_orig | ||
| 53 | seaborn.set_palette | @Modrisco | 100% |
| 54 | seaborn.color_palette | @Modrisco | 100% |
| 55 | seaborn.husl_palette | @Modrisco | 100% |
| 56 | seaborn.hls_palette | @Modrisco | 100% |
| 57 | seaborn.cubehelix_palette | @Modrisco | 100% |
| 58 | seaborn.dark_palette | @Modrisco | 100% |
| 59 | seaborn.light_palette | @Modrisco | 100% |
| 60 | seaborn.diverging_palette | @Modrisco | 100% |
| 61 | seaborn.blend_palette | @Modrisco | 100% |
| 62 | seaborn.xkcd_palette | @Modrisco | 100% |
| 63 | seaborn.crayon_palette | @Modrisco | 100% |
| 64 | seaborn.mpl_palette | @Modrisco | 100% |
| 65 | seaborn.choose_colorbrewer_palette | @Modrisco | 100% |
| 66 | seaborn.choose_cubehelix_palette | @Modrisco | 100% |
| 67 | seaborn.choose_light_palette | @Modrisco | 100% |
| 68 | seaborn.choose_dark_palette | @Modrisco | 100% |
| 69 | seaborn.choose_diverging_palette | @Modrisco | 100% |
| 70 | seaborn.load_dataset | @Modrisco | 100% |
| 71 | seaborn.despine | @Modrisco | 100% |
| 72 | seaborn.desaturate | @Modrisco | 100% |
| 73 | seaborn.saturate | @Modrisco | 100% |
| 74 | seaborn.set_hls_values | @Modrisco | 100% |
Git 中文 版
How to participate: github.com/apachecn/gi…
Overall progress: github.com/apachecn/gi…
Project warehouse: github.com/apachecn/gi…
Claim: 14/83, proofread: 12/83
| The serial number | chapter | contributors | The progress of |
|---|---|---|---|
| 1 | git | ||
| 2 | git-config | @honglyua | 100% |
| 3 | git-help | @honglyua | 100% |
| 4 | git-init | @honglyua | 100% |
| 5 | git-clone | @honglyua | 100% |
| 6 | git-add | @yulezheng | 100% |
| 7 | git-status | @honglyua | 100% |
| 8 | git-diff | @honglyua | 100% |
| 9 | git-commit | @yulezheng | |
| 10 | git-reset | @honglyua | 100% |
| 11 | git-rm | @honglyua | 100% |
| 12 | git-mv | @honglyua | 100% |
| 13 | git-branch | @honglyua | 100% |
| 14 | git-checkout | ||
| 15 | git-merge | ||
| 16 | git-mergetool | ||
| 17 | git-log | ||
| 18 | git-stash | ||
| 19 | git-tag | ||
| 20 | git-worktree | ||
| 21 | git-fetch | ||
| 22 | git-pull | @Mrhuangyi | 100% |
| 23 | git-push | @Mrhuangyi | |
| 24 | git-remote | ||
| 25 | git-submodule | ||
| 26 | git-show | ||
| 27 | git-log | ||
| 29 | git-shortlog | ||
| 30 | git-describe | ||
| 31 | git-apply | ||
| 32 | git-cherry-pick | ||
| 34 | git-rebase | ||
| 35 | git-revert | ||
| 36 | git-bisect | ||
| 37 | git-blame | ||
| 38 | git-grep | ||
| 39 | gitattributes | ||
| 40 | giteveryday | ||
| 41 | gitglossary | ||
| 42 | githooks | ||
| 43 | gitignore | ||
| 44 | gitmodules | ||
| 45 | gitrevisions | ||
| 46 | gittutorial | ||
| 47 | gitworkflows | ||
| 48 | git-am | ||
| 50 | git-format-patch | ||
| 51 | git-send-email | ||
| 52 | git-request-pull | ||
| 53 | git-svn | ||
| 54 | git-fast-import | ||
| 55 | git-clean | ||
| 56 | git-gc | ||
| 57 | git-fsck | ||
| 58 | git-reflog | ||
| 59 | git-filter-branch | ||
| 60 | git-instaweb | ||
| 61 | git-archive | ||
| 62 | git-bundle | ||
| 63 | git-daemon | ||
| 64 | git-update-server-info | ||
| 65 | git-cat-file | ||
| 66 | git-check-ignore | ||
| 67 | git-checkout-index | ||
| 68 | git-commit-tree | ||
| 69 | git-count-objects | ||
| 70 | git-diff-index | ||
| 71 | git-for-each-ref | ||
| 72 | git-hash-object | ||
| 73 | git-ls-files | ||
| 74 | git-merge-base | ||
| 75 | git-read-tree | ||
| 76 | git-rev-list | ||
| 77 | git-rev-parse | ||
| 78 | git-show-ref | ||
| 79 | git-symbolic-ref | ||
| 80 | git-update-index | ||
| 81 | git-update-ref | ||
| 82 | git-verify-pack | ||
| 83 | git-write-tree |
HBase 3.0 Chinese Reference Guide
How to participate: github.com/apachecn/hb…
Overall progress: github.com/apachecn/hb…
Project warehouse: github.com/apachecn/hb…
Claim: 14/31, proofread: 14/31
| chapter | contributors | The progress of |
|---|---|---|
| Preface | @xixici | 100% |
| Getting Started | @xixici | 100% |
| Apache HBase Configuration | @xixici | 100% |
| Upgrading | @xixici | 100% |
| The Apache HBase Shell | @xixici | 100% |
| Data Model | ||
| HBase and Schema Design | @RaymondCode | 100% |
| RegionServer Sizing Rules of Thumb | ||
| HBase and MapReduce | @BridgetLai | 100% |
| Securing Apache HBase | ||
| Architecture | ||
| In-memory Compaction | @mychaow | 100% |
| Backup and Restore | @mychaow | 100% |
| Synchronous Replication | @mychaow | 100% |
| Apache HBase APIs | @xixici | 100% |
| Apache HBase External APIs | @xixici | 100% |
| Thrift API and Filter Language | @xixici | 100% |
| HBase and Spark | @TsingJyujing | 100% |
| Apache HBase Coprocessors | ||
| Apache HBase Performance Tuning | ||
| Troubleshooting and Debugging Apache HBase | ||
| Apache HBase Case Studies | ||
| Apache HBase Operational Management | ||
| Building and Developing Apache HBase | ||
| Unit Testing HBase Applications | ||
| Protobuf in HBase | ||
| Procedure Framework (Pv2): HBASE-12439 | ||
| AMv2 Description for Devs | ||
| ZooKeeper | ||
| Community | ||
| Appendix |
UCB Prob140: probability theory for data science 【翻译】
How to participate: github.com/apachecn/pr…
Overall progress: github.com/apachecn/pr…
Project warehouse: github.com/apachecn/pr…
Claim: 22/25, translation: 19/25
| The title | The translator | Translation progress |
|---|---|---|
| A basis, | dragon | 100% |
| Second, calculate the odds | dragon | 100% |
| Random variables | dragon | 100% |
| Four, the relationship between events | @biubiubiuboomboomboom | 100% |
| 5. Event set | > 0% | |
| 6. Random counting | @viviwong | 100% |
| Poisson chemistry | @YAOYI626 | 100% |
| Eight, expectations | 50% | |
| Ix. Conditions (Continued) | @YAOYI626 | 100% |
| Markov chain | Meow. | 100% |
| 11. Markov Chain (Continued) | Meow. | 100% |
| Xii. Standard Deviation | Missing only samoyed | 100% |
| Variance and covariance | Missing only samoyed | 100% |
| Central limit theorem | Meow. | 100% |
| 15. Continuous distribution | @ThunderboltSmile | |
| Xvi. Transformation | @hellozhaihy | |
| 17. Joint density | @Winchester-Yi | 100% |
| The normal and Gamma families | @Winchester-Yi | 100% |
| Distribution of and | The routine day | 100% |
| 20. Estimation method | The routine day | 100% |
| Beta and binomial | @lvzhetx | 100% |
| 22. Forecast | 50% | |
| Joint normal random Variables | @JUNE951234 | |
| Simple linear regression | @ThomasCai | 100% |
| Multiple regression | @lanhaixuan | 100% |
Machine Learning Mastery
How to participate: github.com/apachecn/ml…
Overall progress: github.com/apachecn/ml…
Project warehouse: github.com/apachecn/ml…
Keras: Claim: 0/46, proofread: 0/46
XGBoost: Claim: 0/18, proofread: 0/18
| chapter | contributors | The progress of |
|---|---|---|
| Deep learning and Keras | – | – |
| The 5-step life cycle of neural network model in Keras | ||
| Apply deep learning in Python mini-lessons | ||
| Keras deep learning library binary classification tutorial | ||
| How to Construct multi-layer perceptron Neural Network model with Keras | ||
| How do I examine the deep learning model in Keras | ||
| 10 command line cheats for deep learning in Amazon Web Services | ||
| Crash course in machine learning convolutional Neural networks | ||
| How do I use Keras for deep learning metrics in Python | ||
| Deep learning books | ||
| Deep learning courses | ||
| Deep learning as you know it is a lie | ||
| How to set up Amazon AWS EC2 GPU to train Keras Deep learning model (in steps) | ||
| What is the difference between batch and iteration in neural networks? | ||
| Present the history of deep learning model training at Keras | ||
| Dropout Regularization in deep learning models based on Keras | ||
| Evaluate the performance of the deep learning model in Keras | ||
| How to evaluate the skills of deep learning models | ||
| Small batch gradient descent and how to configure batch size | ||
| 9 Ways to Get Deep Learning help in Keras | ||
| How to use Keras to grid search hyperparameters of deep learning models in Python | ||
| Handwritten number recognition using convolutional neural networks in Python with Keras | ||
| How to predict with Keras | ||
| Image enhancement using Keras for deep learning | ||
| 8 inspiring applications of Deep learning | ||
| Introduction to Keras, the Python deep learning library | ||
| Introduction to Python deep learning library TensorFlow | ||
| Introduction to the Python deep learning library Theano | ||
| How to use Keras functional API for deep learning | ||
| Keras Deep learning library multi-class classification tutorial | ||
| Crash course in Multilayer Perceptron Neural Networks | ||
| Object recognition in Keras Deep Learning Library based on convolutional Neural network | ||
| Popular deep learning libraries | ||
| Predicting emotions in movie reviews with Deep learning | ||
| A return to Keras deep learning library in Python | ||
| How can I use Keras to get reproducible results | ||
| How to run deep learning experiments on a Linux server | ||
| Save and load your Keras deep learning model | ||
| Step-by-step development of the first neural network in Python with Keras | ||
| Understanding stateful LSTM recurrent neural networks in Python with Keras | ||
| Use the Keras deep learning model and Scikit-learn in Python | ||
| How to classify objects in photos using pre-trained VGG models | ||
| Use learning rate scheduling for deep learning models in Python and Keras | ||
| How to visualize deep learning neural network model in Keras | ||
| What is deep learning? | ||
| When to use MLP, CNN and RNN neural networks | ||
| Why initialize a neural network with random weights? | ||
| XGBoost | – | – |
| Avoid overfitting by using XGBoost to stop early in Python | ||
| How do I tune XGBoost’s multithreading support in Python | ||
| How to configure the gradient lifting algorithm | ||
| Use XGBoost to prepare data for gradient elevation in Python | ||
| How to develop your first XGBoost model in Python using SciKit-learn | ||
| How do I evaluate gradient elevation models in Python using XGBoost | ||
| Feature importance and feature selection using XGBoost in Python | ||
| A brief introduction to machine learning gradient lifting algorithm | ||
| Introduction to XGBoost applying machine learning | ||
| How do I install XGBoost for Python on macOS | ||
| How do I save gradient elevation models in Python using XGBoost | ||
| Starting with gradient lifting, 13 algorithms on 165 data sets were compared | ||
| Use XGBoost and SciKit-learn for random gradient lifting in Python | ||
| How do I use Amazon Web Services to train the XGBoost model in the cloud | ||
| Use XGBoost in Python to adjust the learning rate of gradient elevation | ||
| How do I use XGBoost in Python to adjust the number and size of decision trees | ||
| How to visualize gradient elevation decision trees using XGBoost in Python | ||
| Get started with XGBoost’s 7-step mini-course in Python |
Pytorch 1.0 中文 版
How to participate: github.com/apachecn/py…
Overall progress: github.com/apachecn/py…
Project warehouse: github.com/apachecn/py…
Claim: 22/76, proofread: 1/76
| chapter | The translator | The progress of | Check the | The progress of |
|---|---|---|---|---|
| The tutorial section | – | – | – | – |
| Deep Learning with PyTorch: A 60 Minute Blitz | @bat67 | 100% | @AllenZYJ | |
| What is PyTorch? | @bat67 | 100% | @AllenZYJ | |
| Autograd: Automatic Differentiation | @bat67 | 100% | @AllenZYJ | |
| Neural Networks | @bat67 | 100% | @AllenZYJ | |
| Training a Classifier | @bat67 | 100% | @AllenZYJ | |
| Optional: Data Parallelism | @bat67 | 100% | ||
| Data Loading and Processing Tutorial | @yportne13 | 100% | ||
| Learning PyTorch with Examples | @bat67 | 100% | @Smilexuhc | |
| Transfer Learning Tutorial | @jiangzhonglian | 100% | @infdahai | |
| Deploying a Seq2Seq Model with the Hybrid Frontend | @cangyunye | 100% | ||
| Saving and Loading Models | @bruce1408 | 100% | @luxinfeng | |
| What is torch.nn really? | @lhc741 | 100% | @luxinfeng | |
| Finetuning Torchvision Models | @ZHHAYO | 100% | @luxinfeng | |
| Spatial Transformer Networks Tutorial | @PEGASUS1993 | 100% | @Smilexuhc | |
| Neural Transfer Using PyTorch | @bdqfork | 100% | ||
| Adversarial Example Generation | @cangyunye | 100% | @infdahai | |
| Transfering a Model from PyTorch to Caffe2 and Mobile using ONNX | @PEGASUS1993 | 100% | ||
| Chatbot Tutorial | @a625687551 | 100% | @enningxie | |
| Generating Names with a Character-Level RNN | @hhxx2015 | 100% | @hijkzzz | 100% |
| Classifying Names with a Character-Level RNN | @hhxx2015 | 100% | @hijkzzz | |
| Deep Learning for NLP with Pytorch | @bruce1408 | 100% | ||
| Introduction to PyTorch | @guobaoyo | 100% | ||
| Deep Learning with PyTorch | @bdqfork | 100% | ||
| Word Embeddings: Encoding Lexical Semantics | @sight007 | 100% | @Smilexuhc | |
| Sequence Models and Long-Short Term Memory Networks | @ETCartman | 100% | ||
| Advanced: Making Dynamic Decisions and the Bi-LSTM CRF | @apachecn | 100% | @enningxie | |
| Translation with a Sequence to Sequence Network and Attention | @mengfu188 | 100% | ||
| DCGAN Tutorial | @wangshuai9517 | 100% | @infdahai | |
| Reinforcement Learning (DQN) Tutorial | @friedhelm739 | 100% | @infdahai | |
| Creating Extensions Using numpy and scipy | @cangyunye | 100% | ||
| Custom C++ and CUDA Extensions | @P3n9W31 | 100% | ||
| Extending TorchScript with Custom C++ Operators | @apachecn | 100% | @sunxia233 | |
| Writing Distributed Applications with PyTorch | @firdameng | 100% | ||
| PyTorch 1.0 Distributed Trainer with Amazon AWS | @yportne13 | 100% | ||
| ONNX Live Tutorial | @PEGASUS1993 | 100% | ||
| Loading a PyTorch Model in C++ | @talengu | 100% | ||
| Using the PyTorch C++ Frontend | @solerji | 100% | ||
| Document parts | – | – | – | – |
| Autograd mechanics | @PEGASUS1993 | 100% | ||
| Broadcasting semantics | @PEGASUS1993 | 100% | ||
| CUDA semantics | @jiangzhonglian | 100% | ||
| Extending PyTorch | @PEGASUS1993 | 100% | ||
| Frequently Asked Questions | @PEGASUS1993 | 100% | ||
| Multiprocessing best practices | @cvley | 100% | ||
| Reproducibility | @apachecn | 100% | @bruce1408 | |
| Serialization semantics | @yuange250 | 100% | ||
| Windows FAQ | @PEGASUS1993 | 100% | ||
| torch | @infdahai | 100% | ||
| Tensors | @infdahai | |||
| Random sampling | @apachecn | 100% | ||
| Serialization, Parallelism, Utilities | @apachecn | 100% | ||
| Pointwise Ops | @apachecn | 100% | ||
| Reduction Ops | @apachecn | 100% | ||
| Comparison Ops | @apachecn | 100% | ||
| Spectral Ops | @apachecn | 100% | ||
| Other Operations | @apachecn | 100% | ||
| BLAS and LAPACK Operations | @apachecn | 100% | ||
| torch.Tensor | @hijkzzz | 100% | ||
| Tensor Attributes | @yuange250 | 100% | ||
| Type Info | @PEGASUS1993 | 100% | ||
| torch.sparse | @hijkzzz | 100% | ||
| torch.cuda | @bdqfork | 100% | ||
| torch.Storage | @yuange250 | 100% | ||
| torch.nn | @gongel | 100% | ||
| torch.nn.functional | @hijkzzz | 100% | ||
| torch.nn.init | @GeneZC | 100% | ||
| torch.optim | @apachecn | 100% | @zonasw | |
| Automatic differentiation package – torch.autograd | @gfjiangly | 100% | ||
| Distributed communication package – torch.distributed | @univeryinli | 100% | ||
| Probability distributions – torch.distributions | @hijkzzz | 100% | ||
| Torch Script | @keyianpai | 100% | ||
| Multiprocessing package – torch.multiprocessing | @hijkzzz | 100% | ||
| torch.utils.bottleneck | @belonHan | 100% | ||
| torch.utils.checkpoint | @belonHan | 100% | ||
| torch.utils.cpp_extension | @belonHan | 100% | ||
| torch.utils.data | @BXuan694 | 100% | ||
| torch.utils.dlpack | @kunwuz | 100% | ||
| torch.hub | @kunwuz | 100% | ||
| torch.utils.model_zoo | @BXuan694 | 100% | ||
| torch.onnx | @guobaoyo | 100% | ||
| Distributed communication package (deprecated) – torch.distributed.deprecated | @luxinfeng | 100% | ||
| torchvision Reference | @BXuan694 | 100% | ||
| torchvision.datasets | @BXuan694 | 100% | ||
| torchvision.models | @BXuan694 | 100% | ||
| torchvision.transforms | @BXuan694 | 100% | ||
| torchvision.utils | @BXuan694 | 100% |
OpenCV 4.0 中文 版
How to participate: github.com/apachecn/op…
Overall progress: github.com/apachecn/op…
Project warehouse: github.com/apachecn/op…
Claim: 29/51, proofread: 29/51.
| chapter | contributors | The progress of |
|---|---|---|
| 1. Introduction | – | |
| 1.1 Introduction to OpenCV-Python Tutorial | @wstone0011 | 100% |
| 1.2 install OpenCV — Python | @wstone0011 | 100% |
| 2. GUI function | – | |
| 2.1 Introduction to Images | @ranxx | 100% |
| 2.2 Video Introduction | @ranxx | 100% |
| 2.3 Drawing Function | @ranxx | 100% |
| 2.4 Mouse as brush | @ranxx | 100% |
| 2.5 As the tracking bar for the color palette | @ranxx | 100% |
| 3. Core operations | – | |
| 3.1 Basic image operations | @luxinfeng | 100% |
| 3.2 Image arithmetic operation | @luxinfeng | 100% |
| 3.3 Performance measurement and improvement techniques | @luxinfeng | 100% |
| 4. Image processing | – | |
| 4.1 Change the color space | @friedhelm739 | 100% |
| 4.2 Geometric transformation of images | @friedhelm739 | 100% |
| 4.3 Image threshold | @friedhelm739 | 100% |
| 4.4 Smoothing images | @friedhelm739 | 100% |
| 4.5 Morphological Transformation | @friedhelm739 | 100% |
| 4.6 Image Gradient | @friedhelm739 | 100% |
| 4.7 Canny edge detection | ||
| 4.8 Image Pyramid | ||
| 4.9 outline | ||
| 4.10 the histogram | ||
| 4.11 Image conversion | ||
| 4.12 Template Matching | ||
| 4.13 Hough line transformation | ||
| 4.14 Hough circle transformation | ||
| 4.15 Image segmentation based on watershed algorithm | ||
| Interactive foreground extraction based on GrabCut algorithm | ||
| 5. Feature detection and description | – | |
| 5.1 Understanding Functions | @3lackrush | 100% |
| 5.2 Harris Corner detection | ||
| 5.3 Good characteristics of Shi-Tomasi corner detection and tracking | ||
| 5.4 SIFT Introduction (Scale-invariant Feature Transform) | ||
| 5.5 Introduction to SURF (Accelerated Robust Features) | ||
| 5.6 FAST algorithm for corner detection | ||
| 5.7 Introduction (Basic Features of Binary Robust Independence) | ||
| 5.8 ORB (Directional Fast and Fast Rotation) | ||
| 5.9 Feature Matching | ||
| 5.10 Feature Matching + Homography Search for objects | ||
| Video analysis | – | |
| 6.1 Meanshift and Camshift | @xmmmmmovo | 100% |
| 6.2 the light flow | @xmmmmmovo | 100% |
| 6.3 Background subtraction | @xmmmmmovo | 100% |
| 7. Camera calibration and 3D reconstruction | – | |
| 7.1 Camera Calibration | @xmmmmmovo | 100% |
| 7.2 Posture Estimation | @xmmmmmovo | 100% |
| 7.3 Polar geometry | @xmmmmmovo | 100% |
| 7.4 Depth map of stereo image | @xmmmmmovo | 100% |
| 8. Machine learning | – | |
| 8.1 K- Nearest Neighbor | @wstone0011 | 100% |
| 8.2 Support Vector Machine (SVM) | @wstone0011 | 100% |
| 8.3 K Means clustering | @wstone0011 | 100% |
| Computational photography | – | |
| 9.1 Image denoising | ||
| 9.2 Image Restoration | ||
| 9.3 High Dynamic Range (HDR) | ||
| 10. Target detection | – | |
| 10.1 Face detection using Haar Cascades | @jiangzhonglian | 100% |
| 11. OpenCV – Python bindings | – | |
| 11.1 How does the Opencv-Python binding work? | @daidai21 | 100% |
Run out
UCB CS61b: Data Structures in Java
How to participate: github.com/apachecn/cs…
Overall progress: github.com/apachecn/cs…
Project warehouse: github.com/apachecn/cs…
Claim: 12/12, translation: 10/12
Note-taking activities
CS224n Natural language processing
How to participate: github.com/apachecn/st…
Overall progress: github.com/apachecn/st…
Project warehouse: github.com/apachecn/st…
Claim: 12/20, tidy: 1/20
| chapter | contributors | The progress of |
|---|---|---|
| Lecture 1 | @cx123cx456 | |
| Lecture 2 | @AllenZYJ | |
| Lecture 3 | @cx123cx456 | |
| Lecture 4 | @ZSIRS | |
| Lecture 5 | @ZSIRS | |
| Lecture 6 | @ZSIRS | |
| Lecture 7 | @neolei | |
| Lecture 8 | @Qichao-Ge | |
| Lecture 9 | @NewDreamstyle192 | |
| Lecture 10 | @enningxie | |
| Lecture 11 | ||
| Lecture 12 | ||
| Lecture 13 | ||
| Lecture 14 | ||
| Lecture 15 | ||
| Lecture 16 | ||
| Lecture 17 | @pingjing233 | |
| Lecture 18 | ||
| Lecture 19 | ||
| Lecture 20 | @Willianan | 100% |
About us
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