AI News, Newbie’s guide to Deep Learning

Newbie’s guide to Deep Learning

If you are super stoked after completing Dr. Ng’s course, you should check out his other courses, offered as parts of Deep Learning Specialization on Coursera. offers a free online course on Deep Learning and they offer two parts in their course: After those courses, you may be ready to tackle Hinton’s Neural Networks for Machine Learning.

Hinton’s course is relatively harder compared to previously mentioned courses since the lectures are quite dry and they contain more Math concepts.

I find hadrienj’s notes on Deep Learning Book extremely useful to actually see how underlying Math concepts work using Python (Numpy).

Implementing your own CPU-based backpropagation algorithm on a non-convolution based problem is also a good place to start to truly understand how backpropagation works.

If you want to take a notch up your Machine Learning knowledge and ready to get serious (I mean graduate-level serious), dive into Learning From Data by Caltech Professor Yaser Abu-Mostafa.

I believe it would be hard for textbooks to capture the current state of Deep Learning since the field is moving at a very fast pace.

It is freely available online so you might as well download chapter by chapter and tackle the textbook one chapter at a time.

Yeah, the knowledge of deep learning comes primarily from papers and the rate at they are being published is extreme these days.

You can follow the “Related articles” and “Cited by” to follow the prior work as well as newer work based on that paper.

The advantage of a mind map is that it is a good way to keep track of the relationships of concepts presented in the paper.

I find mind maps very useful to keep track of related literature and how they relate to the paper I am reading.

Mind maps give me a clear picture of a paper and also serves as a good summary of the paper after I have read it.

Visual Introduction to Machine Learning is a good way to visually grasp how statistical learning techniques are used to identify patterns in data.

Those who have done full-scale DL work knows how important it is to keep the whole development operations as streamlined as possible.

Not only the whole data sourcing, collection, annotation, cleaning and assessing steps take at least 60% of the whole project, but also they can be one of the most expensive parts of the project (aside from your power-hungry GPUs!).

4 Months of Machine Deep Learning

So although having hands-on practical deep learning experience is great, saying in an interview I don’t know the difference between the kNN and k-means algorithms would be embarrassing.

Also, although the Fast AI program is taught in Python, it’s not teaching Python, while the DataCamp career track had a number of Python specific courses along with data mining and manipulation.

Although I had taken both Udacity and DataCamp courses by the time I started, both of which cover machine learning — from different angles, I thought learning from one of the best in the field definitely couldn’t hurt.

In my capstone project I will define a problem, potential solution, source data, build and test models, productionize the model, implement an API, UI front-end and deploy to “production”.

Every single Machine Learning course on the internet, ranked by your reviews

The ideal course introduces the entire process and provides interactive examples, assignments, and/or quizzes where students can perform each task themselves.

Here is a succinct description: As would be expected, portions of some of the machine learning courses contain deep learning content.

If you are interested in deep learning specifically, we’ve got you covered with the following article: My top three recommendations from that list would be: Several courses listed below ask students to have prior programming, calculus, linear algebra, and statistics experience.

Several top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar.

Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms.

Ng explains his language choice: Though Python and R are likely more compelling choices in 2017 with the increased popularity of those languages, reviewers note that that shouldn’t stop you from taking the course.

Columbia’s is a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

It covers the entire machine learning workflow and an almost ridiculous (in a good way) number of algorithms through 40.5 hours of on-demand video.

Eremenko and the SuperDataScience team are revered for their ability to “make the complex simple.” Also, the prerequisites listed are “just some high school mathematics,” so this course might be a better option for those daunted by the Stanford and Columbia offerings.

few prominent reviewers noted the following: Our #1 pick had a weighted average rating of 4.7 out of 5 stars over 422 reviews.

Machine learning

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to 'learn' (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.[2]

These analytical models allow researchers, data scientists, engineers, and analysts to 'produce reliable, repeatable decisions and results' and uncover 'hidden insights' through learning from historical relationships and trends in the data.[9]

Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'[10]

Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[14]:708–710;

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.

Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).

The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.[16]

The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

An artificial neural network (ANN) learning algorithm, usually called 'neural network' (NN), is a learning algorithm that is vaguely inspired by biological neural networks.

They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network.

Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.

Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar.

Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters.

Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG).

Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution.

Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.

genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem.

In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.

Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly.[38]

Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[45]

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.

In comparison, the N-fold-cross-validation method randomly splits the data in k subsets where the k-1 instances of the data are used to train the model while the kth instance is used to test the predictive ability of the training model.

For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[62][63]

There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed.[65]

Deep Learning Specialization is dedicated to advancing AI by sharing knowledge about the field.

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