Implementasi DEEP LEARNING Menggunakan Scikit-Learn, Keras, Dan Tensorflow Dengan Python GUI

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· BALIGE PUBLISHING
5.0
ការវាយតម្លៃ 5
សៀវភៅ​អេឡិចត្រូនិច
262
ទំព័រ
ការវាយតម្លៃ និងមតិវាយតម្លៃមិនត្រូវបានផ្ទៀងផ្ទាត់ទេ ស្វែងយល់បន្ថែម

អំពីសៀវភៅ​អេឡិចត្រូនិកនេះ

Buku ini merupakan versi bahasa Indonesia dari buku kami yang berjudul “The Practical Guides On Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI” yang dapat dilihat di Amazon maupun Google Books.


Dalam buku ini, Anda akan mempelajari cara menggunakan TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, dan library lainnya untuk mengimplementasikan deep learning dalam mengenali rambu lalu lintas menggunakan dataset GTSRB, mendeteksi tumor otak menggunakan dataset MRI Brain Image, mengklasifikasikan gender, dan mengenali ekspresi wajah menggunakan dataset FER2013.


Pada bab 1, Anda akan belajar membuat aplikasi GUI untuk menampilkan grafik garis menggunakan PyQt. Anda juga akan belajar bagaimana mengkonversi citra menjadi keabuan, menjadi ruang warna YUV, dan menjadi ruang warna HSV. Bab ini juga mengajarkan bagaimana menampilkan citra dan histogramnya dan merancang GUI untuk mengimplementasikannya.


Pada bab 2, Anda akan belajar menggunakan TensorFlow, Keras, Scikit-Learn, Pandas, NumPy dan sejumlah pustaka lain untuk memprediksi digit-digit tulisan tangan menggunakan dataset MNIST.


Pada bab 3, Anda akan mempelajari cara menggunakan TensorFlow, Keras, Scikit-Learn, PIL, Pandas, NumPy, dan pustaka lain untuk mengenali rambu lalu lintas menggunakan dataset GTSRB dari Kaggle. Ada beberapa jenis rambu lalu lintas seperti batas kecepatan, dilarang masuk, rambu lalu lintas, belok kiri atau kanan, anak-anak menyeberang, tidak ada kendaraan berat yang lewat, dll. Klasifikasi rambu lalu lintas adalah proses untuk mengidentifikasi kelas rambu lalu lintas tersebut. Pada proyek Python ini, Anda akan membangun model jaringan saraf tiruan (deep neural network) yang dapat mengklasifikasikan rambu lalu lintas dalam citra ke dalam kategori yang berbeda. Dengan model ini, Anda akan dapat membaca dan memahami rambu lalu lintas yang merupakan pekerjaan yang sangat penting bagi semua kendaraan otonom. Anda juga akan membangun sebuah GUI untuk tujuan ini.


Pada bab 4, Anda akan mempelajari cara menggunakan TensorFlow, Keras, Scikit-Learn, Pandas, NumPy dan pustaka lainnya untuk melakukan pendeteksian tumor otak menggunakan dataset Brain Image MRI yang disediakan oleh Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection). Anda juga akan membangun sebuah GUI untuk tujuan ini.


Pada bab 5, Anda akan mempelajari cara menggunakan TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy dan library lain untuk melakukan klasifikasi gender menggunakan dataset yang disediakan oleh Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset). Anda juga akan membangun sebuah GUI untuk tujuan ini.


Pada bab 6, Anda akan mempelajari cara menggunakan TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy dan pustaka lain untuk melakukan pengenalan ekspresi wajah menggunakan dataset FER2013 yang disediakan oleh Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition). Anda juga akan membangun sebuah GUI untuk tujuan ini.


ការដាក់ផ្កាយ និងមតិវាយតម្លៃ

5.0
ការវាយតម្លៃ 5

អំពី​អ្នកនិពន្ធ

Vivian Siahaan is a fast-learner who likes to do new things. She was born, raised in Hinalang Bagasan, Balige, on the banks of Lake Toba, and completed high school education from SMAN 1 Balige. She started herself learning Java, Android, JavaScript, CSS, C ++, Python, R, Visual Basic, Visual C #, MATLAB, Mathematica, PHP, JSP, MySQL, SQL Server, Oracle, Access, and other programming languages. She studied programming from scratch, starting with the most basic syntax and logic, by building several simple and applicable GUI applications. Animation and games are fields of programming that are interests that she always wants to develop. Besides studying mathematical logic and programming, the author also has the pleasure of reading novels. Vivian Siahaan has written dozens of ebooks that have been published on Sparta Publisher: Data Structure with Java; Java Programming: Cookbook; C ++ Programming: Cookbook; C Programming For High Schools / Vocational Schools and Students; Java Programming for SMA / SMK; Java Tutorial: GUI, Graphics and Animation; Visual Basic Programming: From A to Z; Java Programming for Animation and Games; C # Programming for SMA / SMK and Students; MATLAB For Students and Researchers; Graphics in JavaScript: Quick Learning Series; JavaScript Image Processing Methods: From A to Z; Java GUI Case Study: AWT & Swing; Basic CSS and JavaScript; PHP / MySQL Programming: Cookbook; Visual Basic: Cookbook; C ++ Programming for High Schools / Vocational Schools and Students; Concepts and Practices of C ++; PHP / MySQL For Students; C # Programming: From A to Z; Visual Basic for SMA / SMK and Students; C # .NET and SQL Server for High School / Vocational School and Students. At the ANDI Yogyakarta publisher, Vivian Siahaan also wrote a number of books including: Python Programming Theory and Practice; Python GUI Programming; Python GUI and Database; Build From Zero School Database Management System In Python / MySQL; Database Management System in Python / MySQL; Python / MySQL For Management Systems of Criminal Track Record Database; Java / MySQL For Management Systems of Criminal Track Records Database; Database and Critptography Using Java / MySQL; Build From Zero School Database Management System With Java / MySQL.

Rismon Hasiholan Sianipar was born in Pematang Siantar, in 1994. After graduating from SMAN 3 Pematang Siantar 3, the writer traveled to the city of Jogjakarta. In 1998 and 2001 the author completed his Bachelor of Engineering (S.T) and Master of Engineering (M.T) education in the Electrical Engineering of Gadjah Mada University, under the guidance of Prof. Dr. Adhi Soesanto and Prof. Dr. Thomas Sri Widodo, focusing on research on non-stationary signals by analyzing their energy using time-frequency maps. Because of its non-stationary nature, the distribution of signal energy becomes very dynamic on a time-frequency map. By mapping the distribution of energy in the time-frequency field using discrete wavelet transformations, one can design non-linear filters so that they can analyze the pattern of the data contained in it. In 2003, the author received a Monbukagakusho scholarship from the Japanese Government. In 2005 and 2008, he completed his Master of Engineering (M.Eng) and Doctor of Engineering (Dr.Eng) education at Yamaguchi University, under the guidance of Prof. Dr. Hidetoshi Miike. Both the master's thesis and his doctoral thesis, R.H. Sianipar combines SR-FHN (Stochastic Resonance Fitzhugh-Nagumo) filter strength with cryptosystem ECC (elliptic curve cryptography) 4096-bit both to suppress noise in digital images and digital video and maintain its authenticity. The results of this study have been documented in international scientific journals and officially patented in Japan. One of the patents was published in Japan with a registration number 2008-009549. He is active in collaborating with several universities and research institutions in Japan, particularly in the fields of cryptography, cryptanalysis and audio / image / video digital forensics. R.H. Sianipar also has experience in conducting code-breaking methods (cryptanalysis) on a number of intelligence data that are the object of research studies in Japan. R.H. Sianipar has a number of Japanese patents, and has written a number of national / international scientific articles, and dozens of national books. R.H. Sianipar has also participated in a number of workshops related to cryptography, cryptanalysis, digital watermarking, and digital forensics. In a number of workshops, R.H. Sianipar helps Prof. Hidetoshi Miike to create applications related to digital image / video processing, steganography, cryptography, watermarking, non-linear screening, intelligent descriptor-based computer vision, and others, which are used as training materials. Field of interest in the study of R.H. Sianipar is multimedia security, signal processing / digital image / video, cryptography, digital communication, digital forensics, and data compression / coding. Until now, R.H. Sianipar continues to develop applications related to analysis of signal, image, and digital video, both for research purposes and for commercial purposes based on the Python programming language, MATLAB, C ++, C, VB.NET, C # .NET, R, and Java.

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