Foundational Concepts in the field of Deep Learning and Machine Learning. We’ll focus on TensorFlow because if one becomes a machine learning expert, these are the tools that people in the trade use everyday.
One of the coolest things that happened in last decade is that Google released a framework for deep learning called TensorFlow. TensorFlow makes all that hard work that we’ve done superfluous because now you have a software framework. They can very easily configure and train deep networks and TensorFlow can be run on many machines at the same time. So, in this medium article, we’ll focus on TensorFlow because if one becomes a machine learning expert, these are the tools that people in the trade use everyday.
A convolutional neural network is a specialized type of deep neural network that turns out to be particularly important for self-driving cars.
Deep Learning is an exciting branch of** machine learning (ML) **that uses data, lots of data, to teach computers how to do or learn things only humans can do. Myself, I’m very interested in solving the problem of perception, recognizing what’s in an image what people are saying when they’re talking on their phone, helping robots explore the world and interact with it. Deep Learning emerged as a central tool to solve perception problems in recent time. It’s the state of the art on everything having to do with computer vision and speech recognition. But there is more. Increasingly, people are finding
that Deep Learning is a much better tool to solve complex problems, like discovering new medicines, understanding natural language (NLP), understanding documents (OCR), and, for example, ranking them for search.
Many companies today, have made deep learning a central part of their mission learning toolkit. Facebook, Baidu, Microsoft and Google, are all using deep learning in their products and pushing the research forward. It’s easy to understand why, deep learning shines wherever there is lots of data and complex problems to solve. And all these companies are facing lots of complicated problems. Understanding what’s in an image, to help you find it. Or** translating a document into another language** that you can speak.
Now, I will explore a continuum of complexity from very simple models to very large ones that one will still be able to train in minutes on a personal computer to very elaborate tasks like predicting the meaning of words or classifying images. One of the nice things about deep learning is it’s really a family of techniques that adapts to all sorts of data and all sorts of problems. All of them using a common infrastructure and a common langauge to describe things.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
In this medium article, I’m going to explain the basics concepts behind Keras, Transfer Learning and Multilayer Convolutional Neural Network. I’ll be introducing an interface that sits on top of TensorFlow, and allows us to draw on the power of TensorFlow with far more concise code. Introduction to Keras and the use of Transfer Learning in the development of Deep Learning architectures. Introduction to Keras & Transfer Learning for Self Driving Cars
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different
Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots.