TensorFlow Training in Jakarta

Dengan mengikuti pelatihan ini, peserta akan dapat:

Pelatihan yang dipimpin instruktur secara langsung (daring atau tatap muka) di Jakarta bertujuan untuk profesional tingkat lanjut yang ingin memperdalam pemahaman mereka tentang visi komputer dan menjelajahi kemampuan TensorFlow dalam pengembangan model visi yang canggih menggunakan Google Colab.

• Membangun dan melatih jaringan saraf konvolusional (CNN) menggunakan TensorFlow.
• Memanfaatkan Google Colab untuk pengembangan model berbasis awan yang dapat disesuaikan skala dan efisien.
• Melaksanakan teknik pra-pemrosesan gambar untuk tugas visi komputer.
• Mendeploy model visi komputer untuk aplikasi dunia nyata.
• Menggunakan pembelajaran transfer untuk meningkatkan kinerja model CNN.
• Visualisasi dan interpretasi hasil dari model klasifikasi gambar.

Dengan menyelesaikan pelatihan ini, peserta akan dapat:

Pelatihan langsung yang dipandu instruktur di Jakarta (daring atau tatap muka) ini ditujukan untuk ilmuwan data dan pengembang level menengah yang ingin memahami dan menerapkan teknik deep learning menggunakan lingkungan Google Colab.

• Mengatur dan menjelajahi Google Colab untuk proyek deep learning.
• Memahami dasar-dasar jaringan saraf.
• Melaksanakan model deep learning menggunakan TensorFlow.
• Mentraining dan mengevaluasi model deep learning.
• Memanfaatkan fitur lanjutan dari TensorFlow untuk deep learning.

Ini adalah kursus 4 hari yang memperkenalkan AI dan penerapannya. Ada opsi untuk memiliki satu hari tambahan untuk melaksanakan proyek AI setelah menyelesaikan kursus ini.

In this instructor-led, live training in Jakarta, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions. 

By the end of this training, participants will be able to:

• Design and code DL for NLP using Python libraries.
• Create Python code that reads a substantially huge collection of pictures and generates keywords.
• Create Python Code that generates captions from the detected keywords.

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images

This course provide working examples.

Pelatihan langsung yang dipandu oleh instruktur ini, di Jakarta (online atau onsite), ditujukan bagi ilmuwan data yang ingin menggunakan TensorFlow untuk menganalisis data potensi penipuan.

Pada akhir pelatihan ini, peserta akan dapat:

• Membuat model deteksi penipuan di Python dan TensorFlow.
• Membangun regresi linier dan model regresi linier untuk memprediksi penipuan.
• Mengembangkan aplikasi AI end-to-end untuk menganalisis data penipuan.

This instructor-led, live training in Jakarta (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

Dalam pelatihan yang dipandu instruktur secara langsung di Jakarta (online atau di lokasi), peserta akan belajar cara mengonfigurasi dan menggunakan Tensorflow Serving untuk mendeploy dan mengelola model ML dalam lingkungan produksi.

Selesai pelatihan ini, peserta akan dapat:

• Mentraining, mengekspor, dan menyajikan berbagai model Tensorflow.
• Mengetes dan mendeploy algoritma menggunakan satu arsitektur dan set API.
• Mengembangkan Tensorflow Serving untuk menyajikan jenis model lain selain model Tensorflow.

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging

This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

• understand TensorFlow’s structure and deployment mechanisms
• carry out installation / production environment / architecture tasks and configuration
• assess code quality, perform debugging, monitoring
• implement advanced production like training models, building graphs and logging

This instructor-led, live training in Jakarta (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.

By the end of this training, participants will be able to:

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

In this instructor-led, live training in Jakarta, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

• Train various types of neural networks on large amounts of data.
• Use TPUs to speed up the inference process by up to two orders of magnitude.
• Utilize TPUs to process intensive applications such as image search, cloud vision and photos.

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, embedding terms, building graphs and logging

This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN

• understand TensorFlow’s structure and deployment mechanisms

• be able to carry out installation / production environment / architecture tasks and configuration

• be able to assess code quality, perform debugging, monitoring

• be able to implement advanced production like training models, building graphs and logging