update

Author: adityaxdubey

Big Data/Big Data E-Books/Apache Spark Deep Learning Cookbook Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark by Ahmed Sherif, Amrith Ravindra (z-lib.org).epub

@@ -1,6 +1,14 @@
 
 # 8. BIG DATA 
 ### General 
+- [X] [Big Data & Hadoop Full Course](https://www.youtube.com/watch?v=1vbXmCrkT3Y) (by Edureka). ~ 10 hours.
+  1. Introduction to Big Data and Hadoop
+  2. HDFS Architecture
+  3. MapReduce Framework
+  4. Yarn and Hadoop Ecosystem
+  5. Hive, Pig, and HBase
+  6. Real-world use cases
+  
 - [X] [Introduction to Big Data (2015)](https://www.coursera.org/learn/big-data-introduction) (by Ilkay Altintas and Amarnath Gupta from UC San Diego at Coursera). ~ 15 hours.
   1. Introduction to Big Data.
   2. Demystifying Data Science.

Big Data/Big Data E-Books/Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka by Shilpi Saxena, Saurabh Gupta (z-lib.org).pdf

@@ -1,75 +1,75 @@
-# 5. DEEP LEARNING <sub><sup><sub>([back &uarr;](#table-of-contents))</sub></sup></sub>
-
-## Python 
-- [X] [Neural Networks and Deep Learning](https://www.coursera.org/learn/neural-networks-deep-learning) (by Andrew Ng from deeplearning.ai at Coursera) ~ 12 hours. 
-  1. Introduction to deep learning.
-  2. Neural Networks Basics.
-  3. Shallow neural networks.
-  4. Deep Neural Networks.
-
-- [X] [Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization](https://www.coursera.org/learn/deep-neural-network) (by Andrew Ng from deeplearning.ai at Coursera) ~ 9 hours. 
-  1. Practical aspects of Deep Learning.
-  2. Optimization algorithms.
-  3. Hyperparameter tuning, Batch Normalization and Programming Frameworks.
-
-- [X] [Structuring Machine Learning Projects](https://www.coursera.org/learn/machine-learning-projects) (by Andrew Ng from deeplearning.ai at Coursera) ~ 6 hours. 
-  1. ML Strategy (1).
-  2. ML Strategy (2).  
-
-- [X] [Convolutional Neural Networks](https://www.coursera.org/learn/convolutional-neural-networks) (by Andrew Ng from deeplearning.ai at Coursera) ~ 9 hours. 
-  1. Focuses on computer vision and its applications, including autonomous driving, face recognition, and medical imaging.
-  2. Learn how to build convolutional neural networks, including recent variations such as residual networks, and apply them to visual detection and recognition tasks.
-  3. Learn how to use neural style transfer to generate art and apply these algorithms to various types of data, including images, videos, and 3D data.
-
-- [X] [Sequence Models](https://www.coursera.org/learn/nlp-sequence-models) (by Andrew Ng from deeplearning.ai at Coursera) ~ 6 hours. 
-  1. Focuses on sequence models and their applications in areas such as speech recognition, music synthesis, chatbots, machine translation, and natural language processing.
-  2. Learn to build and train Recurrent Neural Networks (RNNs) and variants such as GRUs and LSTMs, which are commonly used in sequence modeling.
-  3. Gain experience with natural language processing and Word Embeddings, which are essential for processing text data, and use HuggingFace tokenizers and transformer models to solve NLP tasks such as Named Entity Recognition (NER) and Question Answering. 
-
-- [X] [Build Basic Generative Adversarial Networks (GANs)](https://www.coursera.org/learn/build-basic-generative-adversarial-networks-gans) (by Sharon Zhou from deeplearning.ai at Coursera) ~ 9 hours. 
-  1. Learn about GANs and their applications
-  2. Understand the intuition behind the fundamental components of GANs
-  3. Explore and implement multiple GAN architectures
-  4. Build conditional GANs capable of generating examples from determined categories
-
-- [X] [Build Better Generative Adversarial Networks (GANs)](https://www.coursera.org/learn/build-better-generative-adversarial-networks-gans) (by Sharon Zhou from deeplearning.ai at Coursera) ~ 6 hours. 
-  1. Assess the challenges of evaluating GANs and compare different generative models
-  2. Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
-  3. Identify sources of bias and the ways to detect it in GANs
-  4. Learn and implement the techniques associated with the state-of-the-art StyleGANs
-
-- [X] [Apply Generative Adversarial Networks (GANs)](https://www.coursera.org/learn/apply-generative-adversarial-networks-gans) (by Sharon Zhou from deeplearning.ai at Coursera) ~ 9 hours. 
-  1. Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity
-  2. Leverage the image-to-image translation framework and identify applications to modalities beyond images
-  3. Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa)
-  4. Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures
-  5. Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one
-
-- [X] [Deep Neural Networks with PyTorch](https://www.coursera.org/learn/deep-neural-networks-with-pytorch) (by Joseph Santarcangelo from IBM at Coursera) ~ 8 hours. 
-  1. Explain and apply their knowledge of Deep Neural Networks and related machine learning methods
-  2. Know how to use Python libraries such as PyTorch  for Deep Learning applications 
-  3. Build Deep Neural Networks using PyTorch
-
-- [X] [Deep Learning for Business](https://in.coursera.org/learn/deep-learning-business) (by Jong-Moon Chung from Yonsei University at Coursera) ~ 8 hours. 
-  1. Deep Learning Products & Services.
-  2. Business with Deep Learning & Machine Learning.
-  3. Deep Learning Computing Systems & Software.
-  4. Basics of Deep Learning Neural Networks.
-  5. Deep Learning with CNN & RNN.
-  6. Deep Learning Project with TensorFlow Playground.
-
-- [ ] [Deep Learning in Python](https://www.datacamp.com/courses/deep-learning-in-python) (by Dan Becker at DataCamp). ~ 4 hours.
-  1. Basics of deep learning and neural networks.
-  2. Optimizing a neural network with backward propagation.
-  3. Building deep learning models with `keras`.
-  4. Fine-tuning keras models.
-
- # Contributing
-
-Contributions are very welcome, please share back with the wider community (and get credited for it)!
-
-Please have a look at the [CONTRIBUTING](contributing.md) guidelines, also have a read about our [licensing](https://github.com/Data-Science-Community-SRM/Resourceify/blob/master/LICENSE) policy.
-
----
-
-Back to [main page (table of contents)](https://data-science-community-srm.github.io/Resourceify/)
+# 5. DEEP LEARNING <sub><sup><sub>([back &uarr;](#table-of-contents))</sub></sup></sub>
+
+## Python 
+- [X] [Neural Networks and Deep Learning](https://www.coursera.org/learn/neural-networks-deep-learning) (by Andrew Ng from deeplearning.ai at Coursera) ~ 12 hours. 
+  1. Introduction to deep learning.
+  2. Neural Networks Basics.
+  3. Shallow neural networks.
+  4. Deep Neural Networks.
+
+- [X] [Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization](https://www.coursera.org/learn/deep-neural-network) (by Andrew Ng from deeplearning.ai at Coursera) ~ 9 hours. 
+  1. Practical aspects of Deep Learning.
+  2. Optimization algorithms.
+  3. Hyperparameter tuning, Batch Normalization and Programming Frameworks.
+
+- [X] [Structuring Machine Learning Projects](https://www.coursera.org/learn/machine-learning-projects) (by Andrew Ng from deeplearning.ai at Coursera) ~ 6 hours. 
+  1. ML Strategy (1).
+  2. ML Strategy (2).  
+
+- [X] [Convolutional Neural Networks](https://www.coursera.org/learn/convolutional-neural-networks) (by Andrew Ng from deeplearning.ai at Coursera) ~ 9 hours. 
+  1. Focuses on computer vision and its applications, including autonomous driving, face recognition, and medical imaging.
+  2. Learn how to build convolutional neural networks, including recent variations such as residual networks, and apply them to visual detection and recognition tasks.
+  3. Learn how to use neural style transfer to generate art and apply these algorithms to various types of data, including images, videos, and 3D data.
+
+- [X] [Sequence Models](https://www.coursera.org/learn/nlp-sequence-models) (by Andrew Ng from deeplearning.ai at Coursera) ~ 6 hours. 
+  1. Focuses on sequence models and their applications in areas such as speech recognition, music synthesis, chatbots, machine translation, and natural language processing.
+  2. Learn to build and train Recurrent Neural Networks (RNNs) and variants such as GRUs and LSTMs, which are commonly used in sequence modeling.
+  3. Gain experience with natural language processing and Word Embeddings, which are essential for processing text data, and use HuggingFace tokenizers and transformer models to solve NLP tasks such as Named Entity Recognition (NER) and Question Answering. 
+
+- [X] [Build Basic Generative Adversarial Networks (GANs)](https://www.coursera.org/learn/build-basic-generative-adversarial-networks-gans) (by Sharon Zhou from deeplearning.ai at Coursera) ~ 9 hours. 
+  1. Learn about GANs and their applications
+  2. Understand the intuition behind the fundamental components of GANs
+  3. Explore and implement multiple GAN architectures
+  4. Build conditional GANs capable of generating examples from determined categories
+
+- [X] [Build Better Generative Adversarial Networks (GANs)](https://www.coursera.org/learn/build-better-generative-adversarial-networks-gans) (by Sharon Zhou from deeplearning.ai at Coursera) ~ 6 hours. 
+  1. Assess the challenges of evaluating GANs and compare different generative models
+  2. Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
+  3. Identify sources of bias and the ways to detect it in GANs
+  4. Learn and implement the techniques associated with the state-of-the-art StyleGANs
+
+- [X] [Apply Generative Adversarial Networks (GANs)](https://www.coursera.org/learn/apply-generative-adversarial-networks-gans) (by Sharon Zhou from deeplearning.ai at Coursera) ~ 9 hours. 
+  1. Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity
+  2. Leverage the image-to-image translation framework and identify applications to modalities beyond images
+  3. Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa)
+  4. Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures
+  5. Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one
+
+- [X] [Deep Neural Networks with PyTorch](https://www.coursera.org/learn/deep-neural-networks-with-pytorch) (by Joseph Santarcangelo from IBM at Coursera) ~ 8 hours. 
+  1. Explain and apply their knowledge of Deep Neural Networks and related machine learning methods
+  2. Know how to use Python libraries such as PyTorch  for Deep Learning applications 
+  3. Build Deep Neural Networks using PyTorch
+
+- [X] [Deep Learning for Business](https://in.coursera.org/learn/deep-learning-business) (by Jong-Moon Chung from Yonsei University at Coursera) ~ 8 hours. 
+  1. Deep Learning Products & Services.
+  2. Business with Deep Learning & Machine Learning.
+  3. Deep Learning Computing Systems & Software.
+  4. Basics of Deep Learning Neural Networks.
+  5. Deep Learning with CNN & RNN.
+  6. Deep Learning Project with TensorFlow Playground.
+
+- [ ] [Deep Learning in Python](https://www.datacamp.com/courses/deep-learning-in-python) (by Dan Becker at DataCamp). ~ 4 hours.
+  1. Basics of deep learning and neural networks.
+  2. Optimizing a neural network with backward propagation.
+  3. Building deep learning models with `keras`.
+  4. Fine-tuning keras models.
+
+ # Contributing
+
+Contributions are very welcome, please share back with the wider community (and get credited for it)!
+
+Please have a look at the [CONTRIBUTING](contributing.md) guidelines, also have a read about our [licensing](https://github.com/Data-Science-Community-SRM/Resourceify/blob/master/LICENSE) policy.
+
+---
+
+Back to [main page (table of contents)](https://data-science-community-srm.github.io/Resourceify/)

Big Data/BigData.md

@@ -1,11 +1,11 @@
-
-
-# Contributing
-
-Contributions are very welcome, please share back with the wider community (and get credited for it)!
-
-Please have a look at the [CONTRIBUTING](contributing.md) guidelines, also have a read about our [licensing](https://github.com/Data-Science-Community-SRM/Resourceify/blob/master/LICENSE) policy.
-
----
-
-Back to [main page (table of contents)](https://data-science-community-srm.github.io/Resourceify/)
+
+
+# Contributing
+
+Contributions are very welcome, please share back with the wider community (and get credited for it)!
+
+Please have a look at the [CONTRIBUTING](contributing.md) guidelines, also have a read about our [licensing](https://github.com/Data-Science-Community-SRM/Resourceify/blob/master/LICENSE) policy.
+
+---
+
+Back to [main page (table of contents)](https://data-science-community-srm.github.io/Resourceify/)