| LDR | | 05453cmm u2200541M 4500 |
| 001 | | 000000317780 |
| 003 | | OCoLC |
| 005 | | 20230525183354 |
| 006 | | m d |
| 007 | | cr ||||||||||| |
| 008 | | 200524s2020 xx o ||| 0 eng d |
| 019 | |
▼a 1155328035 |
| 020 | |
▼a 1789953790
▼q (electronic bk.) |
| 020 | |
▼a 9781789953794
▼q (electronic bk.) |
| 035 | |
▼a 2478486
▼b (N$T) |
| 035 | |
▼a (OCoLC)1155163086
▼z (OCoLC)1155328035 |
| 040 | |
▼a YDX
▼b eng
▼c YDX
▼d EBLCP
▼d N$T
▼d OCLCF
▼d 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a QA76.73.P98 |
| 082 | 04 |
▼a 005.133
▼2 23 |
| 100 | 1 |
▼a ANUBHAV SINGH;SAYAK PAUL. |
| 245 | 10 |
▼a HANDS-ON PYTHON DEEP LEARNING FOR THE WEB;INTEGRATING NEURAL NETWORK ARCHITECTURES TO BUILD SMART WEB APPS WITH FLASK, DJANGO, AND
▼h [electronic resource]. |
| 260 | |
▼a [S.l.] :
▼b PACKT PUBLISHING,
▼c 2020. |
| 300 | |
▼a 1 online resource |
| 505 | 0 |
▼a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Dedication -- Preface -- Table of Contents -- Section 1: Artificial Intelligence on the Web -- Chapter 1: Demystifying Artificial Intelligence and Fundamentals of Machine Learning -- Introduction to artificial intelligence and its types -- Factors responsible for AI propulsion -- Data -- Advancements in algorithms -- Advancements in hardware -- The democratization of high-performance computing -- ML -- the most popular form of AI -- What is DL? -- The relation between AI, ML, and DL |
| 505 | 8 |
▼a Revisiting the fundamentals of ML -- Types of ML -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Semi-supervised learning -- Necessary terminologies -- Train, test, and validation sets -- Bias and variance -- Overfitting and underfitting -- Training error and generalization error -- A standard ML workflow -- Data retrieval -- Data preparation -- Exploratory Data Analysis (EDA) -- Data processing and wrangling -- Feature engineering and extraction/selection -- Modeling -- Model training -- Model evaluation -- Model tuning -- Model comparison and selection |
| 505 | 8 |
▼a Deployment and monitoring -- The web before and after AI -- Chatbots -- Web analytics -- Spam filtering -- Search -- Biggest web-AI players and what are they doing with AI -- Google -- Google Search -- Google Translate -- Google Assistant -- Other products -- Facebook -- Fake profiles -- Fake news and disturbing content -- Other uses -- Amazon -- Alexa -- Amazon robotics -- DeepLens -- Summary -- Section 2: Using Deep Learning for Web Development -- Chapter 2: Getting Started with Deep Learning Using Python -- Demystifying neural networks -- Artificial neurons -- Anatomy of a linear neuron |
| 505 | 8 |
▼a Anatomy of a nonlinear neuron -- A note on the input and output layers of a neural network -- Gradient descent and backpropagation -- Different types of neural network -- Convolutional neural networks -- Recurrent neural networks -- Feeding the letters to the network -- Initializing the weight matrix and more -- Putting the weight matrices together -- Applying activation functions and the final output -- Exploring Jupyter Notebooks -- Installing Jupyter Notebook -- Installation using pip -- Installation using Anaconda -- Verifying the installation -- Jupyter Notebooks |
| 505 | 8 |
▼a Setting up a deep-learning-based cloud environment -- Setting up an AWS EC2 GPU deep learning environment -- Step 1: Creating an EC2 GPU-enabled instance -- Step 2: SSHing into your EC2 instance -- Step 3: Installing CUDA drivers on the GPU instance -- Step 4: Installing the Anaconda distribution of Python -- Step 5: Run Jupyter -- Deep learning on Crestle -- Other deep learning environments -- Exploring NumPy and pandas -- NumPy -- NumPy arrays -- Basic NumPy array operations -- NumPy arrays versus Python lists -- Array slicing over multiple rows and columns -- Assignment over slicing -- Pandas |
| 520 | |
▼a This book will help you successfully implement deep learning in Python to create smart web applications from scratch. You will learn how deep learning can transform a simple web app into a smart, business-friendly product. You will also develop neural networks using open-source libraries and also integrate them with different web stack front-ends. |
| 590 | |
▼a Master record variable field(s) change: 050, 082, 630, 650 - OCLC control number change |
| 630 | 00 |
▼a Django (Electronic resource) |
| 630 | 07 |
▼a Django (Electronic resource)
▼2 fast
▼0 (OCoLC)fst01780807 |
| 650 | 0 |
▼a Python (Computer program language) |
| 650 | 0 |
▼a Web applications. |
| 650 | 0 |
▼a Application software
▼x Development. |
| 650 | 0 |
▼a Web site development. |
| 650 | 7 |
▼a Application software
▼x Development.
▼2 fast
▼0 (OCoLC)fst00811707 |
| 650 | 7 |
▼a Python (Computer program language)
▼2 fast
▼0 (OCoLC)fst01084736 |
| 650 | 7 |
▼a Web applications.
▼2 fast
▼0 (OCoLC)fst01895855 |
| 650 | 7 |
▼a Web site development.
▼2 fast
▼0 (OCoLC)fst01173243 |
| 655 | 4 |
▼a Electronic books. |
| 776 | 08 |
▼i Print version:
▼a Singh, Anubhav
▼t Hands-On Python Deep Learning for the Web : Integrating Neural Network Architectures to Build Smart Web Apps with Flask, Django, and TensorFlow
▼d Birmingham : Packt Publishing, Limited,c2020 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2478486 |
| 938 | |
▼a YBP Library Services
▼b YANK
▼n 301282641 |
| 938 | |
▼a ProQuest Ebook Central
▼b EBLB
▼n EBL6201980 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 2478486 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
▼b N$T |