---
product_id: 484271430
title: "Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition"
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# Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

**Brand:** amita kapoorantonio gullisujit pal
**Price:** Bs.1480
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## Quick Answers

- **What is this?** Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition by amita kapoorantonio gullisujit pal
- **How much does it cost?** Bs.1480 with free shipping
- **Is it available?** Yes, in stock and ready to ship
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## Description

Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #1,062,016 in Books ( See Top 100 in Books ) #107 in Computer Simulation (Books) #181 in Machine Theory (Books) #402 in Artificial Intelligence Expert Systems |
| Customer Reviews | 4.4 4.4 out of 5 stars (109) |
| Dimensions  | 7.5 x 1.58 x 9.25 inches |
| Edition  | 3rd ed. |
| ISBN-10  | 1803232919 |
| ISBN-13  | 978-1803232911 |
| Item Weight  | 2.6 pounds |
| Language  | English |
| Print length  | 698 pages |
| Publication date  | October 6, 2022 |
| Publisher  | Packt Publishing |

## Images

![Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition - Image 1](https://m.media-amazon.com/images/I/71V3jvUdBxL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ 5.0 out of 5 stars







  
  
    Route to pratical Deep Learning using Keras
  

*by B***K on Reviewed in the United Kingdom on 2 January 2023*

If you prefer to learn things with hands-on coding then this book is for you. It is expected that you have Tensorflow with high level Keras API already setup (or simply use google Colab), some exposure to deep learning basics along with some experience Python as well.In the first chapter book provides a brief intro to Keras and Perceptron, look at various activation functions and jumps to first code example. This chapter covers a lot of ground such as regularisation, gradient decent, learning rate etc with practical MNIST example. Book cover a number of deep learning topics such as natural language processing, Auto Encoder, GAN, CNN, reinforcement learning etc. each section includes good code examples.Extra points for very well laid out structure of all chapters. I know many people who wants to learn ML/DL but they often get put-off by the mathematics behind it. Book cleverly introduce maths behind machine learning in a separate chapter in later part of the book. Before reaching to this chapter reader already get familiarised with magical Keras and Tensorflow libraries which make building ml model so easy with only 40-50 line of codes and great flexibility. This helps reader to delve into behind the scene mathematics with some confidence and practical knowledge obtained through rest of the chapter.Book includes several references to the source materials (i.e. research papers, earlier work) for each type of neural nets, this allow you to gain further and deeper knowledge if like to get into the details.I have ML background mostly with CNN, GAN and some exposure to RNN. Graph Neural Nets was very interesting chapter for me.There are plenty of practical examples to learn from which is quite amazing.

### ⭐⭐⭐⭐ 4.0 out of 5 stars







  
  
    Very good Reference and Starting point. Supplement with Theory and Code explanations.
  

*by P***S on Reviewed in the United Kingdom on 21 December 2022*

[Some context:- I was kindly provided with a digital review copy in exchange for my feedback.- I run a Machine Learning Practical university course and am approaching this as a potential supplementary book to recommend to my students.- This is a quick review, based on my impressions of the material covered. I have not verified the correctness of all material in the book, nor have I run the code in it].The book has very good coverage in terms of Machine Learning topics relevant to Deep Learning. This largely explains its size, at near 700 pages. Each area then has its most salient subtopics covered, though not at any significant depth. A lot of the space is dedicated to fully print out example code.From what I have sampled, the text is well written and understandable, with good use of examples and figures to accompany the points made. However, it is worth noting that these are somewhat rushed through, in the sense that points are not deliberated on, and a lot of subtopics are only served with a quick introduction. A necessity perhaps given the book's breadth.If one can follow the text, and is happy to supplement it with further material in order to clarify or reinforce the occasional point, then the layout of the subtopics within each chapter will provide a comprehensive overview of its corresponding topic. This is made easier by a good selection of reference material, both in the form of code/libraries and research.I would advise any potential buyer to go through a sample of Chapter 1 (available at least on Amazon). This chapter is a good example of the book's approach. If you can follow it without too many leftover questions, or you find it easy to locate further material where you need a clarification, then the book will prove useful to you. If you find it hard to do so, then you will either need a book that goes slower (and deeper) on the fundamental theory (which could supplement this one) and the code explanations (unfortunately the available sample does not make use of much code but what is there is representative of their approach). Note, that the book's Discord page is active and can be useful in this endeavour.I expect the book would serve also as a good quick-start for someone who needs to learn hands-on, provided they have a reasonable background in programming, linear algebra, and calculus (or you might not be clear on some operations).Overall, this is a very good reference book and starting point for a broad range of applications. I am likely to use select chapters as recommended starting points for students on relevant projects (quick access to baseline solutions and a quick overview of relevant ideas and resources). After trying it out this way, select chapters might be useful for integration into the theory part of my course. I would warn anyone who aims to be an engineer or analyst to supplement this book with a book/course delving deeper into, especially, the fundamentals. As long as you don't uncritically follow the examples in the book (for example, Chapter 1 might leave you thinking it is a good idea to repeatedly test on your held out test data; it is not) you will find it a valuable reference and starting point for any given project.

### ⭐⭐⭐⭐ 4.0 out of 5 stars







  
  
    This book surely will teach you the complete Deep Learning concepts in the best ways.
  

*by S***N on Reviewed in the United Kingdom on 3 January 2023*

As we all know that in the AIML domain next to Machine Learning the Deep Learning specific projects are highly complex, more challenging and in high-demand areas. Personally, I have come across many books and YouTube videos, but I was looking for just one sort of block to cover my expectations, full fill the demands and for those who are all looking for Deep Learning perspectives along with TF and Keras framework, I got that feeling while review and read the book called “Deep Learning with TensorFlow and Keras” by three authors.Here I would like to share my observations based on my interesting chapters base from this book.Based on my experience in the AIML space, there is a very close relationship between mathematics and data science, machine learning and deep learning, so believe the authors have properly picked up and evidently summarized this in Chapter 14 – “The Math Behind Deep Learning”.  I would say this topic is very valuable to understand the various mathematics behind Deep Learning concepts.The authors have covered many things and the interesting notions I come across on the list are - Gradient descent, Activation functions of Derivative of sigmoid, tanh, ReLU, and backpropagation – Forward and Back Steps are quite interesting and most important before we apply them in our model building.The objective of the book is DL with TensorFlow, I can suggest that readers should go through thisChapter 19: TensorFlow 2 Ecosystem, to understand better before we use them with other major topics like RNN, CNN and other topics. Here we can explore various subcomponents under TF 2 ecosystem as TF Hub, Datasets, Lite and its architecture along with different edges and applications, Federated learning, and Node.js with TensorFlow models.Chapter 15: Tensor Processing Unit, is a major topic and backbone of the whole book since this TPU is a special chip developed at Google for ultra-fast execution of neural network mathematical operations additional improvement with TPUs is to remove from the chip any hardware support for graphics operations normally present in GPUs.The one-line takeaway from this topic is “TPU is a special purpose co-processor specialized for deep learning, being focused on tensor specialized operations. The authors have discussed all four generations of TPUs and Edge TPU, really this is a special gift for me and of course readers as well.In Chapter 2: Regression and Classification, the Authors have given the feel of Regression, Classification models and their types and how to use TF, and Keras API with these algorithms with classical examples, please try these samples and understand how TF and Keras work with these basic algorithms.Chapters 3 & 20: CNN and Advanced CNN – Authors are kick-starting how Convolutional Neural Networks (CNN) leverage spatial information, architectures, and other elements which highly recommended while doing DL experiments, and they are sharing how CNN is well-suited for classifying image-specific problem statements. And on top, they are providing the overview of CNNs, DCNNs, and ConvNets along with the sample code step by step using CIFAR-10 images. The topics about the very deep convolutional networks for large-scale image recognition understanding and deep inception V3 are compelling content for readers.Followed by this they were offering a detailed outline of how CNNs can be applied within the areas of computer vision, video, textual documents, audio, and music in Chapter 20. Here we could understand how to use CNN's for text processing and computer vision principles. Under image segmentation, we would extract the  U-Net architecture, and Fast/Faster/Mask R-CNN network architecture.Guys, please read “A summary of convolution operations” this is must read topic to understand different convolution operations and how I x O x K parameters are used in CNN.Chapter 6: Transformers – This chapter super power pack for DL engineers, authors have clearly described the transformer-based architectures very precisely. Particularly in the NLP space. Authors have detailed Categories of transformers and popular transformers like BERT, RoBERTa, ALBERT, StructBERT, DeBERTa,GTP2 & 3, Reformer, Transformer-XL, XLNet the list is long-lost and excellent implementation is truly credited to readers to understand on one book itself as a collective note.Chapters 4, 5 & 17:The authors have focused on text data in quite an interesting way, starting with word embeddings and various techniques and methods used in the industry like – Static, Neural, Character and subword, Dynamic, Sentence and paragraph and Language-based model  Recurrent Neural Networks, Authors have presented an exceptional feast for DL lovers in slice and dice [cells, cells variants, topologies] form and the core role of RNN in various problem-solving areas such as speech recognition, language modeling, machine translation, sentiment analysis, and image captioning, and LSTM and GRU topics are again must-read.Authors have covered the relatively new class of neural networks is nothing but the Graph Neural Network (GNN), As we know that is ideally suited for processing graph data. In the current demand, many real-life problems in areas such as social media, biochemistry, academic literature, and many others are inherently “graph-shaped,” meaning that their inputs are composed of data that can best be represented as graphs, personally this special gift for me. They have coved Graph basics, Graph machine learning, Graph convolutions and Common graph layers and their applications and way to customizations.I would say this book surely will teach you the complete Deep Learning family and understand the concepts in the best way.All the best to the authors. Overall, I can give 4.0/5.0 for this. Certainly, an extraordinary effort from the authors is much appreciated.-Shanthababu PandianArtificial Intelligence and Analytics | Cloud Data and ML Architect | Scrum MasterNational and International Speaker | Blogger

## Frequently Bought Together

- Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
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