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5.3 GB
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0CC0756F8CF832BE3C9E944A79213ECF887314C9
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Added:
April 25, 2026, 2:35 a.m.
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(Last updated: April 25, 2026, 2:36 a.m.)
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| 1. OneHack.us Premium Cracked Accounts-Tutorials-Guides-Articles Community Based Forum.url | 377 bytes |
| 2. FTUApps.com Download Cracked Developers Applications For Free.url | 239 bytes |
| Content Info.txt | 286 bytes |
| Convolutional-neural-networks.zip | 1.3 GB |
| Deep-neural-network.zip | 1.0 GB |
| FreeCoursesOnline.me Download Udacity, Masterclass, Lynda, PHLearn, etc Free.url | 290 bytes |
| Machine-learning-projects.zip | 847.7 MB |
| Neural-networks-deep-learning.zip | 1.3 GB |
| Nlp-sequence-models.zip | 893.7 MB |
| Support Us.txt | 185 bytes |
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878.1 MB
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2023-06-02
| Uploaded by SunRiseZone | Size 878.1 MB | Health [ 19 /6 ] | Added 2023-06-02 |
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3.5 GB
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| Uploaded by FreeCourseWeb | Size 3.5 GB | Health [ 27 /3 ] | Added 2023-07-06 |
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1.4 GB
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| Uploaded by freecoursewb | Size 1.4 GB | Health [ 10 /13 ] | Added 2023-06-02 |
NOTE
SOURCE: Coursera | Deep Learning Specialization [FCO]
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MEDIAINFO
Lynda and other Courses >>> https://freecoursesonline.me/ Forum for discussion >>> https://onehack.us/ https://get.freecoursesonline.me/wp-content/uploads/2024/01/deep-learning.png Coursera - Deep Learning Specialization [FCO] About Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques! What you'll learn • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering Skills you'll gain • Recurrent Neural Network • Tensorflow • Convolutional Neural Network • Artificial Neural Network • Transformers Specialization - 5 course series The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia. 1. Neural Networks and Deep Learning 2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 3. Structuring Machine Learning Projects 4. Convolutional Neural Networks 5. Sequence Models 5 Courses, Total, 1,048 Files, 112 Folders Applied Learning Project By the end you’ll be able to: • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications • Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow • Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning • Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data • Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering Instructor(s) Andrew Ng, Younes Bensouda Mourri, 3 more! Offered By DeepLearning.AI Media Information: MP4 | Video: h264, 1280x720 | Audio: AAC, 44.100 KHz, 2 Ch Genre: eLearning | Language: English + SRT | Last updated: 1/2024 | Duration: 373 Lessons ( 50h 51m ) Source: https://www.coursera.org/specializations/deep-learning
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