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Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code.

Currently, the largest barrier to automating front-end development is computing power. However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now.

In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup.

This collection of data science cheat sheets is a curated list of reference materials spanning a number of disciplines and tools.

Video Tutorials for you!

This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. Included are Machine Learning algorithms in R and Python. The algorithms implemented are:

1. Practical Machine Learning with R and Python – Part 1 The student will learn regression of a continuous target variable. Specifically Univariate, Multivariate, Polynomial regression and KNN regression in both R and Python.
2. Practical Machine Learning with R and Python – Part 2  The Focus is on Logistic Regression, KNN classification and Cross Validation error for both LOOCV and K-Fold in both R and Python.
3. Practical Machine Learning with R and Python – Part 3 This 3rd part includes feature selection in Machine Learning. Specifically, best fit, forward fit, backward fit, ridge(L2 regularization) & lasso (L1 regularization). It contains equivalent code in R and Python.
4. Practical Machine Learning with R and Python – Part 4 In this part, SVMs, Decision Trees, Validation, Precision-Recall, AUC and ROC curves are being discussed.
5. Practical Machine Learning with R and Python – Part 5  This part touches upon B-splines, natural splines, smoothing splines, Generalized Additive Models (GAMs), Decision Trees, Random Forests and Gradient Boosted Trees.
6. Practical Machine Learning with R and Python - Part6 This last part covers Unsupervised Machine Learning, specifically the implementations of Principal Component Analysis (PCA), K-Means and Heirarchical Clustering. The R Markdown file can be downloaded from Github.

Become an expert in Computer Vision for faces in just 10 weeks with this practical course for building applications using OpenCV + Dlib (C++ & Python).

Docker is the world’s leading software container platform. Developers use Docker to eliminate “works on my machine” problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.

Docker Terminology:

1. Docker Containers: Small user-level virtualization (isolation) that helps you install, build and run your code/workflow. All the code would be continuosly running in these containers.
2. Docker Images: An image is an inert, immutable, file that’s essentially a snapshot of a container. These are your actual committed containers (ones that have the process running, data stored, ports exposed to be used). Docker images are essentially the stored instances that you can (actually move around).
3. Dockerfile: It is a YAML (almost) based file from which Docker creates an image. It can be thought of as an automated script that has all the steps you want to execute.

Cutting edge data science projects.

The key to getting better at deep learning (or most fields in life) is practice. Practice on a variety of problems – from image processing to speech recognition. Each of these problem has it’s own unique nuance and approach. But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your newly acquired skills.

This review lists a collection of high quality datasets that every deep learning enthusiast should work on to apply and improve their skill set.  Included are papers with state-of-the-art (SOTA) results to improve models.

Deep Learning frameworks such as Theano, Caffe, TensorFlow, Torch, MXNet and CNTK are the work horses of Deep Learning work. These frameworks as well as the GPU (predominantly Nvidia) are the what enables the rapid growth of Deep Learning. It was refreshing to hear Nando de Freitas acknowledge their work in the recently concluded NIPS 2016 conference. Infrastructure does not get enough of the recognition it deserves in the academic community. Yet, programmers toil on to continually tweak and improve their frameworks.

Recently, a new framework was revealed by Facebook and a bunch of other partners (Twitter * NVIDIA * SalesForce * ParisTech * CMU * Digital Reasoning * INRIA * ENS). PyTorch came out of stealth development. PyTorch is an improvement over the popular Torch framework (Torch was a favorite at DeepMind until TensorFlow came along). The obvious change is the support of Python over the less often used Lua language. Almost all of the more popular frameworks use Python, so it is a relief that Torch has finally joined the club.

COMPLETE LIST OF AI 100 COMPANIES 

Company Name  Total Funding ($M)  Category

1. SoundHound  $ 114  NEWS & MEDIA
2. Endgame  $ 96  CYBERSECURITY
3. Invoca  $ 61  MARKETING, SALES, CRM
4. C3 IoT  $ 131  IOT
5. InsideSales.com  $ 264  MARKETING, SALES, CRM
6. CrowdFlower    $ 56ENTERPRISE AI
7. Narrative Science  $ 48  CROSS-INDUSTRY
8. ZestFinance  $ 268  FINTECH & INSURANCE
9. Aquifi  $ 33  COMMERCE
10. CrowdStrike  $ 281  CYBERSECURITY
11. Anki  $ 182  ROBOTICS
12. Shape Security  $ 106  CYBERSECURITY
13. Appthority  $ 23  CYBERSECURITY
14. Versive  $ 57  CYBERSECURITY
15. WorkFusion  $ 71  RISK & REGULATORY COMPLIANCE
16. Upstart  $ 585  FINTECH & INSURANCE
17. Vicarious Systems  $ 118  ROBOTICS
18. Captricity  $ 49  CROSS-INDUSTRY
19. Trifacta  $ 76  ENTERPRISE
20. Flatiron Health  $ 313  HEALTHCARE
21. Benson Hill Biosystems  $ 34  AGRICULTURE
22. Brain Corp  $ 114  ROBOTICS
23. MAANA  $ 40  IOT
24. Socure  $ 33  RISK & REGULATORY COMPLIANCE
25. Affirm  $ 725  FINTECH & INSURANCE
26. Sherpa  $ 8  PERSONAL ASSISTANTS
27. Dynamic Yield  $ 45  COMMERCE
28. Conversica  $ 56  MARKETING, SALES, CRM
29. Reflektion  $ 46  COMMERCE
30. MOOGsoft  $ 53  IT & NETWORKS
31. Cybereason  $ 189  CYBERSECURITY
32. DataRobot  $ 125  ENTERPRISE AI
33. Onfido  $ 60  RISK & REGULATORY COMPLIANCE
34. Face++  $ 608  CROSS-INDUSTRY
35. Casetext  $ 24  LEGAL TECH
36. Darktrace  $ 182  CYBERSECURITY
37. Algolia  $ 74  ENTERPRISE AI
38. AEYE  $ 16  AUTO TECH
39. Mobvoi  $ 257  CROSS-INDUSTRY
40. Cam Technologies  $ 47  IOT
41. Recursion Pharmaceuticals  $ 119  HEALTHCARE
42. Insilico Medicine  $ 8  HEALTHCARE
43. Neurala  $ 16  ROBOTICS
44. Mya Systems  $ 29  HR TECH
45. FLYR  $ 14  TRAVELAXA
46. AiCure  $ 31  HEALTHCARE
47. Zymergen  $ 174  LIFE SCIENCE
48. Tamr  $ 41  ENTERPRISE
49. Bytedance  $ 3,105  NEWS & MEDIA
50. Appier  $ 82  COMMERCE
51. Applitools  $ 11  SOFTWARE DEVELOPMENT & DEBUGGING
52. Orbital Insight  $ 79  GEOSPATIAL ANALYTICS
53. Preferred Networks  $ 113  IOT
54. Liulishuo  $ 100  EDUCATION
55. Osmo  $ 39  EDUCATION
56. Shift Technology  $ 40  CYBERSECURITY
57. Mighty AI  $ 27  AUTO TECH
58. Textio  $ 30  HR TECH
59. Descartes Labs  $ 38  GEOSPATIAL ANALYTICS
60. Text IQ  $ 3  RISK & REGULATORY COMPLIANCE
61. Tractable  $ 10  CROSS-INDUSTRY
62. Kyndi  $ 10  CROSS-INDUSTRY
63. SPORTLOGiQ  $ 7  SPORTS
64. Twiggle  $ 35  COMMERCE
65. NAUTO  $ 183  AUTO TECH
66. Workey  $ 10  HR TECH
67. Arterys  $ 42  HEALTHCARE
68. babylon  $ 85  HEALTHCARE
69. UBTECH Robotics  $ 521  ROBOTICS
70. CognitiveScale  $ 41  CROSS-INDUSTRY
71. Cape Analytics  $ 14  FINTECH & INSURANCE
72. Numerai  $ 8  FINTECH & INSURANCE
73. PerimeterX  $ 35  CYBERSECURITY
74. SparkCognition  $ 44  CYBERSECURITY
75. Drive.ai  $ 77  AUTO TECH
76. CloudMinds  $ 130  ROBOTICS
77. Foghorn Systems  $ 48  IOT
78. Zoox  $ 290  AUTO TECH
79. Shield AI  $ 13  PHYSICAL SECURITY
80. Freenome  $ 79  HEALTHCARE
81. Gong  $ 26  MARKETING, SALES, CRM
82. Amplero  $ 26  MARKETING, SALES, CRM
83. Prospera  $ 22  AGRICULTURE
84. LeapMind  $ 13  ENTERPRISE AI
85. Mobalytics  $ 3  E-SPORTS
86. Insight Engines  $ 16  CROSS-INDUSTRY
87. Kindred Systems  $ 43  ROBOTICS
88. Graphcore  $ 110  HARDWARE FOR AI
89. Petuum  $ 108  ENTERPRISE
90. Mythic  $ 19  HARDWARE FOR AI
91. Element AI  $ 102  ENTERPRISE AI
92. SenseTime  $ 637  CROSS-INDUSTRY
93. Cerebras Systems  $ 85  HARDWARE FOR AI
94. Afiniti  $ 80  MARKETING, SALES, CRM
95. Deep Sentinel  $ 7  PHYSICAL SECURITY
96. Merlon Intelligence  $ 8  RISK & REGULATORY COMPLIANCE
97. Obsidian Security  $ 10  CYBERSECURITY
98. Cambricon  $ 101  HARDWARE FOR AI
99. Tempus Labs  $ 70  HEALTHCARE
100. Primer  $ 15  CROSS-INDUSTRY

Use this curated list of resources to learn data science!

Many people are seeking to learn data science these days. It’s become a trendy topic associated with high salaries and some of the most interesting problems in the world. This demand has created many different resources in the data science space.

Take a tour of the most popular machine learning algorithms.

Other Lists of Algorithms

There are other great lists of algorithms out there if you’re interested. Below are few hand selected examples.

• List of Machine Learning Algorithms: On Wikipedia. Although extensive, I do not find this list or the organization of the algorithms particularly useful.
• Machine Learning Algorithms Category: Also on Wikipedia, slightly more useful than Wikipedias great list above. It organizes algorithms alphabetically.
• CRAN Task View: Machine Learning & Statistical Learning: A list of all the packages and all the algorithms supported by each machine learning package in R. Gives you a grounded feeling of what’s out there and what people are using for analysis day-to-day.
• Top 10 Algorithms in Data Mining: Published article and now a book (Affiliate Link) on the most popular algorithms for data mining. Another grounded and less overwhelming take on methods that you could go off and learn deeply.

How to Study Machine Learning Algorithms

Algorithms are a big part of machine learning. It’s a topic I am passionate about and write about a lot on this blog. Below are few hand selected posts that might interest you for further reading.

• How to Learn Any Machine Learning Algorithm: A systematic approach that you can use to study and understand any machine learning algorithm using “algorithm description templates” (I used this approach to write my first book).
• How to Create Targeted Lists of Machine Learning Algorithms: How you can create your own systematic lists of machine learning algorithms to jump start work on your next machine learning problem.
• How to Research a Machine Learning Algorithm: A systematic approach that you can use to research machine learning algorithms (works great in collaboration with the template approach listed above).
• How to Investigate Machine Learning Algorithm Behavior: A methodology you can use to understand how machine learning algorithms work by creating and executing very small studies into their behaviour. Research is not just for academics!
• How to Implement a Machine Learning Algorithm: A process and tips and tricks for implementing machine learning algorithms from scratch.

How to Run Machine Learning Algorithms

Sometimes you just want to dive into code. Below are some links you can use to run machine learning algorithms, code them up using standard libraries or implement them from scratch.

• How To Get Started With Machine Learning Algorithms in R: Links to a large number of code examples on this site demonstrating machine learning algorithms in R.
• Machine Learning Algorithm Recipes in scikit-learn: A collection of Python code examples demonstrating how to create predictive models using scikit-learn.
• How to Run Your First Classifier in Weka: A tutorial for running your very first classifier in Weka (no coding required!).

This article displays the list of machine learning algorithms such as linear, logistic regression, kmeans, decision trees along with Python R code.

We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to cloud. But what makes it defining is not what has happened, but what is coming our way in years to come. What makes this period exciting for some one like me is the democratization of the tools and techniques, which followed the boost in computing.