Approachable and motivating

It covers what can be very complex topics, but is written in a way that makes them very easy to understand. If you’re looking for an introduction to AI and Machine Learning, I can’t think of a better resource. For business folks evaluating whether these technologies may benefit their organization, this provides a great framework. For students entering the business and/or tech world, this would be a great starting point. Though it is not a technical reference, I think it gives developers and IT folks a good understanding of how others are evaluating their work. Even if you’re not in one of the categories above, you’ve likely seen ton of news articles discussing these technologies. This book would be great for you as well. I’ve read a lot of books and articles on these topics, but there’s nothing I can point to that provides a better overview.

24 people found this helpful

Definitive

The course was excellent structured. I am experienced electrical engineer with little knowledge of Python but I managed to understand all techniques and algorithms presented. Very thorough overview of many different pieces of the Data Science landscape. A few issues getting the technologies working together since packages change over time, so I'd recommend the authors clean that up a bit. Beyond that, well worth my time! with no practical experience, and no python experience, really I could understand ML. The concepts are super-clear and the examples are also excellent .

15 people found this helpful

Intellectual and Resourceful

By this course I got good fundamentals and the way teaching done by the tutor is fantastic. This course create a lot of interest and inputs for those want to get in to Real world applications and Video game development. Concepts were explained very clearly. Exercises on each topic was so well thought through and it really helped to reinforce the concepts and validate the understanding. He makes it look easy, simplifying complex things and motivating enough to keep you going. All I can say is thank you. I am just at 30 mins but I am already searching for all your other courses While some of the concepts are taking longer to digest, the teacher is making what can be a steep learning curve less arduous. Overall course was amazing. Anthony covered all topics which were necessary and also the practical experience is what I really enjoyed.

14 people found this helpful

Everything is fine

Yes it's a good match. I have strong programming experience, and still need to go back to review the material more than once. Python programming class is a good pre-requisite for this class. This is a very good course and equip me with sound basic. I feel that I can use what I learn in this class to do real works. Leonard is excellent at explaining the machine learning concepts and walking through their implementation in Python. I especially like his ability to distill fancy concepts into simple terms throughout the course, because at the end of the day these "fancy" ML models should produce accurate, intuitive results given good input data. Do the exercises (whether while taking the course or walk through them later) because this is really how to hammer home the concepts and Python code. In general, the best way to learn the programming side of any technical implementation in my experience is by doing it. Very comprehensive course about many machine learning techniques and easy to follow exercises. Very good for a beginner.

13 people found this helpful

Neural Network Elements

Neural networks are a collection of algorithms created to recognize certain patterns. We are already familiar with these networks and how they transform data through created model of perception, clustering, and labeling raw input. The patterns which are recognized are numerically denoted as vectors, and all real-world data including text, images, the sound must be translated into those vectors. Keep in mind that neural networks are classifying and clustering different data. Think of them as a certain layer which you put on top of your data that you managed and stored. The neural network works for you as a help in order to group and classify different data in a correspondence to similarities between example inputs. Think of deep neural networks as a part of the greater machine-learning application for regression, reinforcement learning, and classification. Deep learning is used for networks which are composed of multiple layers. Each layer is made of a node which is a place where computations occur. These nodes from every layer combine input from a data with a collection of coefficients, or certain weights, so the significance is assigned to every input for each task the algorithm is processing or learning.

9 people found this helpful

Broad, Mathematical Survey

Since it is independent of backend engines, Keras manages all its graphs independently, so it is possible to define any Keras model with the Theano engine backend. It is even possible to switch to the TensorFlow backend or to reapply your Keras model which is Theano built on a TensorFlow input which creates a TF model version. This TF model is what was a Theano model initially created. Another important feature of Keras is an object-oriented design. Therefore, in Keras everything acts like an object including models, optimizers, and layers. All parameters regarding any object can be accessed. A different approach would be as in other deep learning models which are defined as chains of functions since most of these functions are heavily parameterized by the weight of their tensors. Since the manipulating of these parameters would be impractical in a functional way, Keras approach is to treat everything like an object. If the situation is different, the functional approach will imply layers, models, and optimizers as functions which would create more weights when called, and they would be stored in global name-indexed collections with other models which are using this approach. For example, TensorFlow has taken this approach. This method is just impractical since many operations like accessing an existing tensor or simple model loading must be approached by name-matching.

3 people found this helpful

Clear and usable instructions and foundation

Great and well rounded insights into Deep Learning. Always focused on communicating the taught material through tangible and real world examples.

Come for the theory, stay for the practical advic

The main features of Keras are implemented from commonly used neural network building blocks like objectives, layers, activation functions and optimizers. With Keras it is easy to work with text and image data since it features a host of tools suitable for any beginner. Facts are speaking for itself, Keras is the second-fastest growing framework, and that's not surprising. Keras is used to solve problems without having to interact with the TensorFlow or Theano, which are underlying backend engines. So, without any interaction with the background, end-to-end problems can be solved. Initially, Kears was developed on top of Theano, but shortly after the release of TensorFlow, Keras added it as backend. As new generation computation graph engines are designed, Keras will extend to support those as well in the future. Keras is not dependable on backend engines since it features its built-in graph data structure which can handle computational graphs. Therefore, it doesn't rely on TensorFlow's or Theano's native graph data structure. As a result of this independent data structure, Keras can make offline shape inference in Theano which is missing, but very much needed feature in Theano. Easy model copying and sharing are also doable in Keras.

The definitive guide to becoming a researcher

The definitive guide to becoming a researcher in the field Learn lot and did some practice too on code and trying to map some scenarios too. Very helpful and useful Course. I really like the way we made complicated concepts really simple. I really like and appreciates your view on ethics and moral values. Will taking course on Big Data by You. God Bless You.

For those interested in DL math and research

Fantastic course. I will be going over it again. I am trying to get my teeth into this topic. This guys really knows what he talking about, and knows how to explain complex topics in a "simple" manner. Ranks up there as one of the best courses I took on this platform.