All of the DLer should owe one

Simplifies the maths, good at explaining the various deep learning models in Keras. The course is very carefully designed. It has covered all the needed topics for Deep Learning from a practical perspective.

Altogether it was a great experience. Hopefully, I could take this wonderful learning experience for the betterment of my research and definitely to the society where I live in. I appreciate Mr. Anthony Williams and team for their incredible and genuine efforts. Another major point I want to highlight is that I was exposed to lot of useful websites and useful resources during the course. The exercises given made sure it covered all the aspects taught during the session.

Simple explanations, simplifies the maths, good at explaining the python programming. Once again, Thanks a lot.

24 people found this helpful

Great treatise on the mathematical tenets

Deep learning, which is called as hierarchical or deep structured learning, is the application for learning chores of neural networks which are artificial denoted as ANNs. Artificial neural networks contain multiple hidden layers. Being a part of the greater family of machine learning techniques, deep learning is based on learning data representations in opposition to task particular algorithms. Deep learning can be unsupervised, partially supervised and supervised. Deep learning models like recurrent networks and deep neural networks are applied to broad range of fields including, natural language processing, bioinformatics, machine translation, computer vision, etc. Deep learning models are classified as learning algorithms machines. The methods of deep learning include usage of multiple layers of nonlinear processing components in order to extract and transform their features. Every successive layer is based on the output of the previous layers which is used as input. Other deep learning models which are unsupervised are based on learning of numerous levels of representations and features of some image or text data. To form a hierarchical formation, features from higher levels are developed from the lower level features. Also, deep learning models can present the correspondence between numerous levels of features and different abstraction levels which are forming hierarchical concept.

17 people found this helpful

Foundations of machine learning and deep learning.

Kudos are well deserved for the author demystifying some of the biggest and least understood buzzwords out there today: How to implement various algorithms in Keras....

Finally a straightforward discussion of these advanced concepts wrapped in an easily-accessible and understandable fashion. If you're curious about the topics and nervous about buying an in-depth tome in which you will feel lost in jargon and technical mumbo-jumbo, buy this one. The author is clearly an expert in the topic area and does a fantastic job of explaining it to mortals.

Pick this one up, you'll be glad you did.

6 people found this helpful

Types of Deep Neural

Networks There are various types of deep neural networks including Hopfield network, deep belief network, generative adversarial network, autoencoder, radial basis network,etc. However, in this book, we are going to see how to create different types of neural networks with Keras such as recurrent neural network, deep feed-forward network, deep convolutional networks and de-convolutional network . But firstly, some details regarding these types of deep networks should be discussed, before going further into the subject. Feed-forward neural network is where connections among different components do not form a cycle model, which would be the main difference between feed-forward and recurrent neural networks. The feed-forward network was, in fact, the initial and the simplest neural network model. The information contained in this network are being transferred only in one linear direction, forward, from the initial input nodes to the hidden layers and their nodes. As mentioned before, recurrent neural networks form connection among components in the form of cycle. This model enables a network to expose dynamic temporal behavior. As for a convolutional neural network, it is a type of feed-forward network which has already proven value while analyzing visual imagery.

5 people found this helpful

Very satisfied with the course

The instructor has lots of data science experience, so he gives us useful and real-world examples throughout the course. Those examples can be implemented in future projects with slight changes, I suppose.

The course was easier, more straight forward, and more interesting than what I was expecting a data science course to be.

Anthony Williams, thank you very much for your well-organized course, clear concept explanations, and useful examples.

2 people found this helpful

Flagship book for Deep Learning

Distributed representations of layers are observed as all data which is generated by the interaction between layered factors. Deep learning is based on the assumption that each layer of factors is in a correspondence to the other levels of composition and abstraction. The number of created levels of abstraction depends on layers size and number. The hierarchical concept of deep learning refers to that abstract concept and higher level components which are derived from the lower level ones. Deep neural networks may include few fundamental approaches. Each deep learning model can be successful in certain domains, but the comparison between models isn't always possible unless the models have been estimated on the same data collection. Deep neural networks are feedforward meaning that data is transferred from an input to output once, without looping back. On the other hand, the reccurrent neural network makes it possible for data to move in any direction which is commonly applied to language modeling. About certain interpretations and applications of deep neural networks will be the word in the later sections of the book.

1 person found this helpful

Solid / practical guide for all.

This book is geared toward beginners who are just beginning to explore the power of such a library and its significant influence on network development. So, after you are done with reading, you will be able to create your first neural network model using Keras, you will get familiar with the practical usage of Keras, and its implementation and you will see what are some of the realworld examples of Keras and what is its overall influence in deep learning studies. Keras is recommended library for deep learning in Python since it is minimalistic, it is capable of running on top of Theano, Deep learning, Tensorflow, and CNTK. Keras is carefully designed to make fast experimentation possible with deep neural networks. The focus of the Keras design is to keep it modular and extensible.Developed as a part of ONEIROS, Keras's primary author is François Chollet who is a Google engineer. It was introduced in 2015, and its author later explained that Keras was developed to be an interface rather than machine learning framework. Keras, in fact, presents a higher-level intuitive set of abstractions which make it easy to configure neural networks.

1 person found this helpful

Almost everything.

This course provides a comprehensive overview on the emerging technologies - Deep neural network Neural network elements, Keras models, Sequential model, Functional API model, Keras layers, Core Keras layers, Convolutional Keras layers, Recurrent Keras layers, Deep learning algorithms, Supervised learning algorithms, Applications of deep learning models, Automatic speech and image recognition, Natural language processing, Video game development and Real world applications, which was very impressive to me.

DEEP LEARNING, is OUTSTANDING

This is a vast topic. This course is more like an overview, giving an idea of where to begin and how to proceed and at the end you will be a Keras expert and ready for your deep-learning projects. Great help. Thanks.

Great way to get started with Deep Learning

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.