Deep Learning Training in Indonesia

Stable Diffusion adalah model pembelajaran mendalam yang kuat yang dapat menghasilkan gambar terperinci berdasarkan deskripsi teks.Pelatihan langsung yang dipandu instruktur (online atau di tempat) ini ditujukan untuk ilmuwan data tingkat menengah hingga mahir, insinyur pembelajaran mesin, peneliti pembelajaran mendalam, dan pakar visi komputer yang ingin memperluas pengetahuan dan keterampilan mereka dalam pembelajaran mendalam untuk teks-ke -generasi gambar.Pada akhir pelatihan ini, peserta akan dapat:

Memahami arsitektur dan teknik deep learning tingkat lanjut untuk pembuatan teks-ke-gambar. Terapkan model dan pengoptimalan yang kompleks untuk sintesis gambar berkualitas tinggi. Optimalkan performa dan skalabilitas untuk kumpulan data besar dan model kompleks. Sesuaikan hyperparameter untuk performa dan generalisasi model yang lebih baik. Integrasikan Stable Diffusion dengan kerangka kerja dan alat pembelajaran mendalam lainnya.

Format Kursus

Kuliah dan diskusi interaktif. Banyak latihan dan latihan. Implementasi langsung di lingkungan lab langsung.

Opsi Penyesuaian Kursus

Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.

DeepSpeed adalah pustaka pengoptimalan pembelajaran mendalam yang mempermudah penskalaan model pembelajaran mendalam pada perangkat keras terdistribusi. Dikembangkan oleh Microsoft, DeepSpeed terintegrasi dengan PyTorch untuk memberikan penskalaan yang lebih baik, pelatihan yang lebih cepat, dan penggunaan sumber daya yang lebih baik.Pelatihan langsung yang dipandu instruktur ini (online atau di tempat) ditujukan untuk ilmuwan data tingkat pemula hingga menengah dan insinyur pembelajaran mesin yang ingin meningkatkan kinerja model pembelajaran mendalam mereka.Pada akhir pelatihan ini, peserta akan dapat:

Memahami prinsip-prinsip pembelajaran mendalam terdistribusi. Instal dan konfigurasikan DeepSpeed. Skalakan model pembelajaran mendalam pada perangkat keras terdistribusi menggunakan DeepSpeed. Menerapkan dan bereksperimen dengan fitur DeepSpeed untuk pengoptimalan dan efisiensi memori.

Format Kursus

Kuliah dan diskusi interaktif. Banyak latihan dan latihan. Implementasi langsung di lingkungan lab langsung.

Opsi Penyesuaian Kursus

Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more. This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks. By the end of this training, participants will be able to:

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AlphaFold adalah sistem Artificial Intelligence (AI) yang melakukan prediksi struktur protein. Ini dikembangkan oleh Alphabet’s/Google’s DeepMind sebagai sistem pembelajaran mendalam yang dapat memprediksi dengan tepat model 3D struktur protein. Pelatihan langsung yang dipimpin oleh instruktur (online atau on-site) ini ditujukan untuk ahli biologi yang ingin memahami bagaimana AlphaFold bekerja dan menggunakan AlphaFold model sebagai panduan dalam studi eksperimental mereka. Pada akhir latihan ini, peserta akan dapat:

Memahami prinsip dasar dari AlphaFold. Ketahui bagaimana 2 (dua) bekerja. Pelajari cara menginterpretasikan AlphaFold prediksi dan hasil.

Format Kursus

Konferensi dan diskusi interaktif. Banyak latihan dan praktek. Pelaksanaan tangan di lingkungan live-lab.

Opsi Customization Kursus

Untuk meminta pelatihan tersuai untuk kursus ini, silakan hubungi kami untuk mengatur.

Stable Diffusion adalah model pembelajaran mendalam yang kuat yang dapat menghasilkan gambar terperinci berdasarkan deskripsi teks.Pelatihan langsung yang dipandu instruktur (online atau di tempat) ini ditujukan untuk ilmuwan data, insinyur pembelajaran mesin, dan peneliti visi komputer yang ingin memanfaatkan Stable Diffusion untuk menghasilkan gambar berkualitas tinggi untuk berbagai kasus penggunaan.Pada akhir pelatihan ini, peserta akan dapat:

Pahami prinsip Stable Diffusion dan cara kerjanya untuk pembuatan gambar. Buat dan latih model Stable Diffusion untuk tugas pembuatan gambar. Terapkan Stable Diffusion ke berbagai skenario pembuatan gambar, seperti inpainting, outpainting, dan terjemahan gambar ke gambar. Optimalkan performa dan stabilitas model Stable Diffusion.

Format Kursus

Kuliah dan diskusi interaktif. Banyak latihan dan latihan. Implementasi langsung di lingkungan lab langsung.

Opsi Penyesuaian Kursus

Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.

In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data. By the end of this training, participants will be able to:

• Implement machine learning algorithms and techniques for solving complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Push Python algorithms to their maximum potential.
• Use libraries and packages such as NumPy and Theano.

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Deep Reinforcement Learning mengacu pada kemampuan "agen buatan" untuk belajar dengan trial-and-error dan hadiah-dan-hukuman. Agen buatan bertujuan untuk meniru kemampuan manusia untuk mendapatkan dan membangun pengetahuannya sendiri, langsung dari masukan mentah seperti penglihatan. Untuk mewujudkan pembelajaran penguatan, pembelajaran mendalam dan jaringan saraf digunakan. Pembelajaran penguatan berbeda dari pembelajaran mesin dan tidak bergantung pada pendekatan pembelajaran yang diawasi dan tidak diawasi.Pelatihan langsung yang dipandu instruktur ini (online atau onsite) ditujukan untuk pengembang dan ilmuwan data yang ingin mempelajari dasar-dasar Deep Reinforcement Learning saat mereka melangkah melalui pembuatan Agen Deep Learning.Pada akhir pelatihan ini, peserta akan dapat:

Memahami konsep kunci di balik Deep Reinforcement Learning dan dapat membedakannya dari Machine Learning. Terapkan algoritme Reinforcement Learning lanjutan untuk memecahkan masalah dunia nyata. Bangun Agen Deep Learning.

Format Kursus

Kuliah dan diskusi interaktif. Banyak latihan dan latihan. Implementasi langsung di lingkungan lab langsung.

Opsi Penyesuaian Kursus

Untuk meminta pelatihan khusus untuk kursus ini, silakan hubungi kami untuk mengaturnya.

Pembelajaran mesin adalah cabang kecerdasan buatan di mana komputer memiliki kemampuan untuk belajar tanpa secara eksplisit diprogramkan. Pembelajaran mendalam adalah sub bidang pembelajaran mesin yang menggunakan metode berdasarkan representasi data pembelajaran dan struktur seperti jaringan saraf. Python adalah bahasa pemrograman tingkat tinggi yang terkenal karena sintax yang jelas dan pembacaan kode. Dalam pelatihan yang dipimpin oleh instruktur, peserta akan belajar bagaimana menerapkan model pembelajaran mendalam untuk telecom menggunakan Python saat mereka melangkah melalui penciptaan model risiko kredit pembelajaran mendalam. Pada akhir latihan ini, peserta akan dapat:

Memahami konsep dasar pembelajaran mendalam. Pelajari aplikasi dan penggunaan pembelajaran mendalam di telecom. Gunakan Python, Keras, dan TensorFlow untuk membuat model pembelajaran mendalam untuk telekomunikasi. Membangun model prediksi customer churn pembelajaran mendalam mereka sendiri menggunakan Python.

Format Kursus

Konferensi dan diskusi interaktif. Banyak latihan dan praktek. Pelaksanaan tangan di lingkungan live-lab.

Opsi Customization Kursus

Untuk meminta pelatihan tersuai untuk kursus ini, silakan hubungi kami untuk mengatur.

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to:

• Explore how data is being interpreted by machine learning models
• Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
• Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
• Explore the properties of a specific embedding to understand the behavior of a model
• Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Kursus ini mencakup AI (emphasizing Machine Learning dan Deep Learning) di Automotive Industri. Ini membantu menentukan teknologi apa yang dapat (potensial) digunakan dalam situasi berbilang dalam sebuah mobil: dari otomatisasi sederhana, pengenalan gambar hingga pengambilan keputusan otomatis.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to:

• understand Caffe’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, implementing layers and logging

This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Perkenalan:Pembelajaran mendalam menjadi komponen utama desain produk masa depan yang ingin menggabungkan kecerdasan buatan di jantung model mereka. Dalam 5 hingga 10 tahun ke depan, alat pengembangan pembelajaran mendalam, pustaka, dan bahasa akan menjadi komponen standar dari setiap perangkat pengembangan perangkat lunak. Sejauh ini Google, Sales Force, Facebook, Amazon telah berhasil menggunakan AI pembelajaran mendalam untuk meningkatkan bisnis mereka. Aplikasi berkisar dari terjemahan mesin otomatis, analitik gambar, analitik video, analitik gerak, menghasilkan iklan bertarget dan banyak lagi.Kursus ini ditujukan untuk organisasi yang ingin memasukkan Deep Learning sebagai bagian yang sangat penting dari strategi produk atau layanan mereka. Di bawah ini adalah garis besar kursus pembelajaran mendalam yang dapat kami sesuaikan untuk berbagai tingkat karyawan/pemangku kepentingan dalam suatu organisasi.Audiens Sasaran:(Tergantung pada audiens target, materi kursus akan disesuaikan)EksekutifTinjauan umum tentang AI dan kesesuaiannya dengan strategi perusahaan, dengan sesi terobosan tentang perencanaan strategis, peta jalan teknologi, dan alokasi sumber daya untuk memastikan nilai maksimum.Manajer ProyekCara merencanakan proyek AI, termasuk pengumpulan dan evaluasi data, pembersihan dan verifikasi data, pengembangan model proof-of-concept, integrasi ke dalam proses bisnis, dan pengiriman ke seluruh organisasi.PengembangPelatihan teknis mendalam, dengan fokus pada jaringan saraf dan pembelajaran mendalam, analitik gambar dan video (CNN), analitik suara dan teks (NLP), dan membawa AI ke dalam aplikasi yang ada.PenjualGambaran umum tentang AI dan bagaimana AI dapat memenuhi kebutuhan pelanggan, proposisi nilai untuk berbagai produk dan layanan, serta cara menghilangkan ketakutan dan mempromosikan manfaat AI.

Pembelajaran mendalam atau pembelajaran berstruktur mendalam adalah subset pembelajaran mesin yang melibatkan beberapa lapisan jaringan untuk membangun model prediksi. Ini digunakan secara luas di industri utama, seperti perawatan kesehatan, e-commerce, perbankan, manufaktur, automotif, dll. Pelatihan ini dipimpin oleh instruktur, secara langsung (online atau on-site) ditujukan untuk para penganalisis bisnis, ilmuwan data, dan pengembang yang ingin membangun dan menerapkan model pembelajaran mendalam untuk mempercepat pertumbuhan pendapatan dan menyelesaikan masalah di dunia bisnis. Pada akhir latihan ini, peserta akan dapat:

Memahami konsep inti pembelajaran mesin dan pembelajaran mendalam. Dapatkan wawasan tentang masa depan bisnis dan industri dengan ML dan DL. Definisi strategi bisnis & solusi dengan pembelajaran mendalam. Pelajari cara menerapkan ilmu data dan pembelajaran mendalam dalam memecahkan masalah bisnis. Oleh karena itu, hendaklah kita mempelajari dan mempelajari ilmu-ilmu yang ada di dalam Al-Qur’an.

Format Kursus

Konferensi dan diskusi interaktif. Banyak latihan dan praktek. Pelaksanaan tangan di lingkungan live-lab.

Opsi Customization Kursus

Untuk meminta pelatihan tersuai untuk kursus ini, silakan hubungi kami untuk mengatur.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use R to create deep learning models for finance
• Build their own deep learning stock price prediction model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use Python, Keras, and TensorFlow to create deep learning models for banking
• Build their own deep learning credit risk model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use R to create deep learning models for banking
• Build their own deep learning credit risk model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use Python, Keras, and TensorFlow to create deep learning models for finance
• Build their own deep learning stock price prediction model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine. In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations. By the end of this training, participants will be able to:

• Understand the fundamentals of Deep Learning
• Learn Deep Learning techniques and their applications in the industry
• Examine issues in medicine which can be solved by Deep Learning technologies
• Explore Deep Learning case studies in medicine
• Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

• Managers
• Medical professionals in leadership roles

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

Healthcare issues can be detected through the analysis of images such as MRI scans. Using deep learning to process images can lead to discoveries previously unattainable by human inspection alone. This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans. By the end of this training, participants will be able to:

• Install and configure the necessary development environment, software and libraries to begin developing.
• Analyze MRI images using deep learning techniques such as CNNs.
• Detect potential health conditions such as heart disease through MRI scan analysis.
• Apply techniques such as image segmentation and CNN training to identify potential disease.
• Identify the genomics of a disease using radiomics.
• Build and deploy a deep learning application aimed at healthcare image analysis.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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.

Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to:

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This course covers AI (emphasizing Machine Learning and Deep Learning)

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries

OpenCV is a library of programming functions for deciphering images with computer algorithms. OpenCV 4 is the latest OpenCV release and it provides optimized modularity, updated algorithms, and more. With OpenCV 4 and Python, users will be able to view, load, and classify images and videos for advanced image recognition. This instructor-led, live training (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning. By the end of this training, participants will be able to:

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to:

• Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
• Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice