I assume that you are a beginner and that you have no or little experience with deep learning, neural networks and google's tensorflow so far. What I expect is that you are interested in machine learning neural networks and artificial intelligence ( AI) in general! You want to learn the fundamentals and you want to dive into this topic which will shape the world of tomorrow. Self driving cars, industry 4.0, robotics,... all those areas are playgrounds for neural networks. So it makes sense to dive into this topic and learn the basics.

As you might know, deeplearning.ai The first batch contains Course 1 to 3. And only recently (as of November 15), Course 4, "Convolution Neural Networks" was released. And Course 5 is supposedly released in late November. So Course 3, "Structuring Machine Learning Projects" was more the "final" course in the first batch.

As you all know, Prof. Ng has a new specialization on Deep Learning. I wrote about the course extensively yet informally, which include two "Quick Impressions" before and after I finished Course 1 to 3 of the specialization. I also wrote three posts just on Heroes on Deep Learning including Prof. Geoffrey Hinton, Prof. Yoshua Bengio and Prof. Pieter Abbeel and Dr. Yuanqing Lin . This is my full review of Course 1 after finish watching all the videos. I will give a description on what the course is about, and why you want to take it.

About this course: The goal of «Intro to Reinforcement learning» is in its name: introduce students to reinforcement learning – the prominent area of modern research in artificial intelligence. The reinforcement learning differs much from both supervised and unsupervised learning and is more about how humans learn in reality. Students will learn from this course both theoretical core and recent practical RL methods. Most importantly, they will learn how to apply such methods to practical problems. In six weeks students will be guided through the basics of Reinforcement Learning (RL): we will talk about essential theory of RL, value-based methods (such as SARSA and Q-learning), policy based algorithms and methods, designed to solve the optimal exploration problem.

The increasing power and capability of machines in the digital supply network (DSN) may portend a change in what organizations ask of their workers, in terms of required skills, tasks, and roles. In the coming years, perhaps sooner than later, almost all work will likely involve people working alongside technology or robots they are not currently working with today. Navigating the future of work can be a new and confounding challenge to many supply chain executives who may already be struggling with what their organizations may look like in a novel, more interconnected age. And it can be difficult to identify and prepare for the workforce of the future when the impacts of the DSN on roles and functions are still very much evolving (see the sidebar "A brief look at the digital supply network" to learn more). But with this uncertainty comes the opportunity--and perhaps what many would consider a requirement--to rethink the role of talent in supply chains and discover the potential power of people and machines working together. The addition of advanced technology to a workplace can spur the fear of robots replacing human workers. Certainly, the introduction of advanced technologies could eliminate some tasks and reduce the need for some roles. At the same time, however, it also could lead to the creation of some new tasks and roles. In the United Kingdom, for example, technology has helped to create 3.5 million new jobs between 2001 and 2015, even while it has contributed to the loss of 800,000 other jobs.1

In a previous tutorial, I demonstrated how to create a convolutional neural network (CNN) using TensorFlow to classify the MNIST handwritten digit dataset. TensorFlow is a brilliant tool, with lots of power and flexibility. However, for quick prototyping work it can be a bit verbose. Enter Keras and this Keras tutorial. Keras is a higher level library which operates over either TensorFlow or Theano, and is intended to stream-line the process of building deep learning networks.

R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. If you want to learn reward-based learning with R, then you should surely go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Beginning with the basics of R programming, this Learning Path provides step-by-step resources and time-saving methods to help you solve programming problems efficiently. You will be able to boost your productivity with the most popular R packages and data structures such as matrices, lists, and factors.

In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia.

As reports of retrenchments That Artificial Intelligence, Machine Learning and automation is killing jobs is no news. Reports of retrenchments and cap on hirings too are sending shivers down the spines of the IT employees. "The threat is real and because of this threat perception there is a huge interest among the senior employees in the industry to take to courses in AI/ML," P J Narayanan, Director of International Institute of Information Technology (IIIT-Hyderabad), said. This is evident from findings of an analysis done by the institute of profiles of the applicants who sought to join an AI/ML course started by it. The IIIT (H), in association with human resources skilling firm TalentSprint started the course.

After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs. Before I turn to Udacity advanced courses, I want to mention one thing at the beginning. If I could give advice to myself, I would select another introduction course on Deep Learning -- Deep Learning Specialization by Andrew Ng. First of all, his way of mentoring is unique and he can explain complex things in most clear and understandable way.