As of today, the hottest jobs in the industry are around AI, Machine Learning and Deep Learning. Let me try to outline the learning path for you in machine learning for the job profiles such as Data Scientist, Machine Learning Engineer, AI Engineer or ML Researcher.
AI basically means Artificial Intelligence – Making machines behave like an intelligent being. AI is defined around its purpose. To achieve AI, we use various hardware and software. In software, we basically use two kinds of approaches: Rule-Based and Machine Learning based.
In the rule-based approach, the logic is coded by people by understanding the problem statement. In the machine learning approach, the logic is inferred using the data or experience.
There are various algorithms or approaches that are part of the machine learning such as linear regression (fitting a line), Support vector machines, decision trees, random forest, ensemble learning and artificial neural networks etc.
The artificial neural network-based algorithms have proven very effective in recent years. The area of machine learning that deals with a complex neural network is called Deep Learning.
As part of this post, I want to help you plan your learning path in Machine Learning.
If you are looking for a non-mathematical and light on coding approach, please go through the course on “AI for Managers“. It is a very carefully curated and a very unique course that deals with AI and Machine Learning for those who are looking for a less mathematical approach.
If you are interested in Machine Learning or Deep Learning, but struggling to decide which book to use to study the same, here is a list of the best books in these fields. What makes this list even better is that some of these books are available online, for free! So go through the list, and pick the one that suits you best.
1. Deep Learning Book – by Aaron Courville, Ian Goodfellow, and Yoshua Bengio This book covers them all, including the mathematics required for Deep Learning. What’s more, it is available for free from the official website of this book. This is a must have for any serious Deep Learning practitioner.
Backpropagation is considered as one of the core algorithms in Machine Learning. It is mainly used in training the neural network. What if we tell you that understanding and implementing it is not that hard? Anyone who knows basic of Mathematics and has knowledge of basics of Python Language can learn this in 2 hours. Let’s get started.
Though there are many high-level overviews of the backpropagation algorithm what I found is that unless one implements the backpropagation from scratch, he or she is not able to understand many ideas behind neural networks.
Recently, I came up with an idea for a new Optimizer (an algorithm for training neural network). In theory, it looked great but when I implemented it and tested it, it didn’t turn out to be good.
Some of my learning are:
Neural Networks are hard to predict.
Figuring out how to customize TensorFlow is hard because the main documentation is messy.
Theory and Practical are two different things. The more hands-on you are, the higher are your chances of trying out an idea and thus iterating faster.
I am sharing my algorithm here. Even though this algorithm may not be of much use to you but it would give you ideas on how to implement your own optimizer using Tensorflow Keras.
A neural network is basically a set of neurons connected to input and output. We need to adjust the connection strengths such that it gives the least error for a given set of input. To adjust the weight we use the algorithms. One brute force algorithm could be to try all possible combinations of weights (connections strength) but that will be too time-consuming. So, we usually use the greedy algorithm most of these are variants of Gradient Descent. In this article, we will write our custom algorithm to train a neural network. In other words, we will learn how to write our own custom optimizer using TensorFlow Keras.
Every day the world is advancing into the new level of industrialization and this has resulted in the production of a vast amount of data. And, at initial stages, people started considering it as a bane, but later they found out that it’s a boon. So, they started using this data in a productive way. Big data and machine learning are terminologies based on the concept of analyzing and using the same data. Let’s get into more details.
Generally, Machine Learning (or Deep Learning) projects are quite unique and also different from traditional web application projects due to the inherent complexity involved with them.
The goal of this article is, not to go through full project management life cycle, but to discuss a few complexities and finer points which may impact different project management phases and aspects of a Machine Learning(or Deep Learning) project, and, which should be taken care of, to avoid any surprises later.
Below is a quick ready reckoner for the topics that we will be discussing in this article.
Let us say, you have trained, fine-tuned and tested Machine Learning(ML) model – sgd_clf, which was trained and tested using SGD Classifier on MNIST dataset. And now you want to deploy it in production, so that consumers of this model could use it. What are different options you have to deploy your ML model in production?
Mathematics is the science which deals with the logic of quantity, shape, and arrangement. Undeniably, math is all around us, in fact in everything we do. It wouldn’t be wrong to say, math is the building block for everything in our daily life period. Money, sports, architecture (ancient and modern), television, mobile devices, and even art, all of it has some mathematical concepts involved in it.
In India, mathematics has its origins in Vedic literature which is nearly four thousand years old. It should come as no surprise that the concept of number ‘0’ was discovered in India; also, various treatises on mathematics were authored by Indian mathematicians. The techniques of trigonometry, algebra, algorithm, square root, cube root, negative numbers, and the most significant decimal system are concepts which were discovered by Indian mathematician from ancient India and are employed worldwide even today.
One of the classic problem that has been used in the Machine Learning world for quite sometime is the MNIST problem. The objective is to identify the digit based on image. But MNIST is not very great problem because we come up with great accuracy even if we are looking at few pixels in the image. So, another common example problem against which we test algorithms is Fashion-MNIST.
Fashion-MNIST is a dataset of Zalando’s fashion article images —consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each instance is a 28×28 grayscale image, associated with a label.