- 1 Digital Leadership Series
- 2 What is Machine Learning & Artificial Intelligence?
- 3 Machine Learning & Artificial Intelligence Technology
- 4 Machine Learning & Artificial Intelligence Career Demand
- 5 Machine Learning & Artificial Intelligence Manager Salary
- 6 Machine Learning & Artificial Intelligence Manager Job Description
- 7 Machine Learning & Artificial Intelligence Startups
- 8 Machine Learning & Artificial Intelligence Wiki
- 8.1 Backpropagation
- 8.2 BLSTM, BiLSTM
- 8.3 Computer Vision
- 8.4 Curriculum learning
- 8.5 Curse of dimensionality
- 8.6 DCGAN (Deep Convolutional Generative Adverserial Systems)
- 8.7 DCIGN (Deep Convolutional Inverse Graphics Network)
- 8.8 Recognition in Computer Vision (Object recognition)
- 8.9 Discriminative Model
- 8.10 FC7-Features
- 8.11 Feature Map
- 8.12 GEMM (GEneral Matrix to Matrix Multiplication)
- 8.13 Generative model
- 8.14 Gradient Descent
- 8.15 Machine Vision
- 8.16 Matrix Completion
- 8.17 MMD (Maximum Mean Descrepancy)
- 8.18 Object recognition
- 8.19 One-Shot learning
- 8.20 Regularization
- 8.21 Semi-supervised learning
- 8.22 Supervised learning
- 8.23 Spatial Pyramid Pooling (SPP)
- 8.24 Without supervision learning
- 8.25 Zero-Shot learning
Digital Leadership Series
Welcome to the Digital Leadership Series. This Machine Learning career insight guide is part of the Digital Leadership Series written by Angel Berniz. Following you can find all the Digital Leadership Series guides available:
- Big Data
- Internet of Things
- Industry 4.0
- Scaled Agile
- Machine Learning
- Lean Startup
- Design Thinking
CALL FOR LESSONS LEARNED TIPS by Pros, for writing 10 new books. The challenge is the following:
- If you are a Pro in one of these 10 areas, please contribute with one Lesson Learned tip that you would love someone else had taught you when you started. I mean a 1-page project management tip on Robotics, Big Data, Blockchain, etc.
- You can also help me with this challenge by sharing to your friends, colleagues, Twitter followers, Facebook friends, and LinkedIn connections, asking them for Pros to contribute one tip to be included in these books.
All contributors will be mentioned in the book and gain career exposure!
Send your contributions to [email protected]
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What is Machine Learning & Artificial Intelligence?
Machine learning is a kind of artificial intelligence (AI) that gives computers having the ability to learn without having to be clearly programmed. Machine learning concentrates on the introduction of software that may change when uncovered to new data.
The entire process of machine learning is comparable to those of data mining. Both systems sort through data to consider patterns. However, rather of removing data for human comprehension — out of the box the situation in data mining applications — machine learning uses that data to identify patterns in data and adjust program actions accordingly. Machine learning algorithms are frequently categorized to be supervised or unsupervized. Supervised algorithms can use what’s been learned previously to new data. Without supervision algorithms can draw inferences from datasets.
For example Facebook’s News Feed uses machine understanding how to personalize each member’s feed. If your member frequently stops scrolling to be able to read or “like” a specific friend’s posts, this news Feed will begin to show much more of that friend’s activity earlier within the feed. Behind the curtain, the program is just using record analysis and predictive analytics to recognize patterns within the user’s data and employ to patterns to populate this news Feed. If the member no more pause and read, like or discuss the friend’s posts, that new data is going to be incorporated within the data set and also the News Feed will adjust accordingly.
Machine Learning & Artificial Intelligence Technology
Machine Learning & Artificial Intelligence Career Demand
The demand of the Machine Learning & Artificial Intelligence career is very high, with exponential increase (according to IT Jobs Whatch):
Machine Learning & Artificial Intelligence Manager Salary
The salary of Machine Learning & Artificial Intelligence is high because there lack of professionals in this field:
Machine Learning & Artificial Intelligence Manager Job Description
The following Machine Learning Manager job description for an employment opportunity was published at Apple:
Predictive Search Manager, Machine Learning
- Job Number: 35082490
- Santa Clara Valley, California, United States
- Posted: Aug. 30, 2016
- Weekly Hours: 40.00
Play a part in the next revolution in human-computer interaction. Contribute to a product that is redefining mobile computing. Create groundbreaking predictive features with enormous reach and visibility. And work with the people who created the intelligent assistant that helps millions of people get things done — even before they ask. We are looking for an experienced manager who is adept at managing engineers as well as digging in to deep technical problems ranging from performance optimization to machine learning to data analytics. The ideal candidate has a strong mix of education and practical experience, and loves diving in to interesting challenges and pushing for iterative solutions.
- Direct contribution to the design and implementation of innovative, interactive products that were successfully shipped and used by consumers
- Minimum of five years experience building large-scale consumer facing software, with at least two years in a managerial role
- Experience leading teams working on machine learning and data mining
- Experience building iOS Frameworks and libraries a plus
We need someone who is adept at managing a team, as well as coordinating cross functional efforts across an organization. You must enjoy rolling up your sleeves and working closely with others in a fast paced environment with rapidly changing priorities. Design and engineer predictive systems that are informed by analyzing information on Apple devices Drive successful feature definition with Apple’s Human Interface team Lead and recruit engineers with expertise in information retrieval and machine learning Interact with numerous client & QA teams ensuring good APIs and communication Ensure creation of a performant system with constrained resources (battery, CPU, etc) Maintain a culture of strong development principles – including automated testing, robust code design, and documentation – to ensure ongoing high quality results
Master’s degree in Computer Science or equivalent experience, preferred
Machine Learning & Artificial Intelligence Startups
Machine Learning & Artificial Intelligence universe is very wide, with a lot of specialization of the companies competing in this market so that they shine in one specific area. The Machine Learning & Artificial Intelligence universe is the following:
Machine Learning & Artificial Intelligence Wiki
An imaginative implementation of gradient descent for neural systems.
Bidirectional lengthy short-term memory (see paper and poster).
The educational discipline which handles how you can gain high-level understanding from digital images or videos. Common tasks include image classifiction, semantic segmentation, recognition and localization.
A technique for pretraining. First optimize a smoothed objective and progressively consider less smoothing. So a curriculum is really a sequence of coaching criteria. One might show progressively harder training examples. See Curriculum Learning by Benigo, Louradour, Collobert and Weston for details.
Curse of dimensionality
Various problems of high-dimensional spaces that don’t exist in low-dimensional spaces. High-dimensional frequently means several 100 dimensions. See also: Average Distance of Points
DCGAN (Deep Convolutional Generative Adverserial Systems)
The Deep Convolutional Generative Sdversarial Dystems (DCGANs), which have certain architectural constraints, and demonstrate that they’re a powerful candidate for without supervision learning. Training on various image datasets, we show convincing evidence our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes both in the generator and discriminator. Furthermore, we make use of the learned features for novel tasks – demonstrating their applicability as general image representations.
DCIGN (Deep Convolutional Inverse Graphics Network)
The Deep Convolutional Inverse Graphics Network (DC-IGN), one that aims to understand an interpretable representation of images, disentangled regarding three-dimensional scene structure and viewing transformations for example depth rotations and lighting variations.
Recognition in Computer Vision (Object recognition)
Object recognition within an image is really a computer vision task. The input is definitely an image and also the output is really a list with rectangles that have objects from the given type. Face recognition is a well-studied example. A photograph could contain no face or hundrets of these. The rectangles can overlap.
The model gives a conditional probability of the classes, given the feature vector : . This kind of model is often used for prediction.
Options that come with a picture that was tell you an experienced neural network. AlexNet known as the final fully connected layer FC7. However, FC7 features aren’t always produced by AlexNet.
An element map is the effect of a single filter of the convolutional layer being applied. So it’s the activation of this filter within the given input.
GEMM (GEneral Matrix to Matrix Multiplication)
General Matrix to Matrix Multiplication is the problem of calculating the result ofwith .
The model gives the relationship of variables:. This kind of model can be used for prediction, too.
An iterative optimization formula for differentiable functions.
Computer vision requested industrial applications.
See collaborative filtering.
MMD (Maximum Mean Descrepancy)
MMD is a measure of the difference between a distributionand a distribution :
Classification on images. The job would be to decide by which class confirmed image falls, knowing through the content. This is often cat, dog, plane or similar.
Learn just with one or very couple of examples per class. See One-Shot Learning of Object Groups.
Regularization are techniques to make the fitted function smoother. This helps to prefent overfitting. Examples: L1, L2, Dropout, Weight Decay in Neural Networks. Parameterin SVMs.
Some training data has labels, but many doesn’t have labels.
All training data has labels.
Spatial Pyramid Pooling (SPP)
SPP is the thought of dividing the look right into a grid having a fixed quantity of cells along with a variable size, with respect to the input. Each cell computes one of the things and therefore results in a fixed-size representation of the variable-sized input.
Without supervision learning
No training data has labels.
Understanding how to predict classes, which no example continues to be seen during training. For instance, Flicker will get several new tags every day and they would like to predict tags for brand new images. One idea is by using WordNet and ImageNet to develop a common embedding. By doing this, new words of WordNet would be able to come with an embedding and therefore new images groups may also instantly be classified the proper way. See Zero-Shot Learning with Semantic Output Codes in addition to this YouTube video.
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