changing the spelling of TensorFlow to align to their site, but only in markdown files

3-NeuralNetworks/05-Frameworks/README.md

@@ -9,9 +9,9 @@ While `numpy` library can do the first part, we need some mechanism to compute g
 
 Another important thing is to be able to perform computations on GPU, or any other specialized compute units, such as [TPU](https://en.wikipedia.org/wiki/Tensor_Processing_Unit). Deep neural network training requires *a lot* of computations, and to be able to parallelize those computations on GPUs is very important.
 
-Currently, there are two most popular neural frameworks: [Tensorflow](http://tensorflow.org), and [PyTorch](https://pytorch.org/). Both provide low-level API to operate with tensors on both CPU and GPU. On top of the low-level API, there is also higher-level API, called [Keras](https://keras.io/) and [PyTorch Lightning](https://pytorchlightning.ai/) correspondingly.
+Currently, there are two most popular neural frameworks: [TensorFlow](http://TensorFlow.org), and [PyTorch](https://pytorch.org/). Both provide low-level API to operate with tensors on both CPU and GPU. On top of the low-level API, there is also higher-level API, called [Keras](https://keras.io/) and [PyTorch Lightning](https://pytorchlightning.ai/) correspondingly.
 
-Low-Level API | [TensorFlow](http://tensorflow.org) | [PyTorch](https://pytorch.org/)
+Low-Level API | [TensorFlow](http://TensorFlow.org) | [PyTorch](https://pytorch.org/)
 --------------|-------------------------------------|--------------------------------
 High-level API| [Keras](https://keras.io/) | [PyTorch Lightning](https://pytorchlightning.ai/)
 
@@ -25,7 +25,7 @@ It is also important to understand that you can use both APIs together, eg. you
 
 ## Learning
 
-In this course, we offer most of the content both for PyTorch and Tensorflow. You can chose your preferred framework and only go through the corresponding notebooks. If you are not sure which framework to chose - read some discussions on the internet regarding **PyTorch vs. Tensorflow**. You can also have a look at both frameworks to get better understanding.
+In this course, we offer most of the content both for PyTorch and TensorFlow. You can chose your preferred framework and only go through the corresponding notebooks. If you are not sure which framework to chose - read some discussions on the internet regarding **PyTorch vs. TensorFlow**. You can also have a look at both frameworks to get better understanding.
 
 Where possible, we will use High-Level APIs for simplicity. However, we believe it is important to understand how neural networks work from the ground up, thus in the beginning we start by working with low-level API and tensors. However, if you want to get going fast and do not want to spend a lot of time on details, you can skip those, and go straight into high-level API notebooks.
 

3-NeuralNetworks/README.md

@@ -40,5 +40,5 @@ where f is some non-linear **activation function**.
 In this section we will learn about:
 * [Perceptron](03-Perceptron/README.md), one of the earliest neural network models for two-class classification
 * [Multi-layered networks](04-OwnFramework/README.md) and [how to build our own framework](04-OwnFramework/OwnFramework.ipynb)
-* [Neural Network Frameworks](05-Frameworks/README.md), such as [PyTorch](05-Frameworks/IntroPyTorch.ipynb) and [Keras/Tensorflow](05-Frameworks/IntroKerasTF.ipynb)
+* [Neural Network Frameworks](05-Frameworks/README.md), such as [PyTorch](05-Frameworks/IntroPyTorch.ipynb) and [Keras/TensorFlow](05-Frameworks/IntroKerasTF.ipynb)
 * [Overfitting](05-Frameworks/Overfitting.md)

4-ComputerVision/07-ConvNets/README.md

@@ -30,7 +30,7 @@ The way CNNs work is based on the following important ideas:
 Let's continue exploring how convolutional neural networks work, and how we can achieve trainable filters, in corresponding notebooks:
 
 * [Convolutional Neural Networks - PyTorch](ConvNetsPyTorch.ipynb)
-* [Convolutional Neural Networks - Tensorflow](ConvNetsTF.ipynb)
+* [Convolutional Neural Networks - TensorFlow](ConvNetsTF.ipynb)
 
 ## Pyramid Architecture
 

4-ComputerVision/08-TransferLearning/README.md

@@ -23,4 +23,4 @@ In this example, we will use a dataset of [Cats and Dogs](https://www.microsoft.
 Let's see transfer learning in action in corresponding notebooks:
 
 * [Transfer Learning - PyTorch](TransferLearningPyTorch.ipynb)
-* [Transfer Learning - Tensorflow](TransferLearningTF.ipynb)
+* [Transfer Learning - TensorFlow](TransferLearningTF.ipynb)

4-ComputerVision/09-Autoencoders/README.md

@@ -49,7 +49,7 @@ Observe how images blend into each other, as we start getting latent vectors fro
 
 ## Continue to Notebooks
 
-* [Autoencoders in Tensorflow](AutoencodersTF.ipynb)
+* [Autoencoders in TensorFlow](AutoencodersTF.ipynb)
 
 ## Properties of Autoencoders
 

4-ComputerVision/10-GANs/README.md

@@ -39,7 +39,7 @@ The training happens in two stages:
 During this process, both generator and discriminator losses are not going down significantly. In the ideal situation, they should oscillate, corresponding to both networks improving their performance.
 
 ## Go to Notebook
-* [GAN Notebook in Tensorflow/Keras](GANs.ipynb)
+* [GAN Notebook in TensorFlow/Keras](GANs.ipynb)
 
 ### Problems with GAN training
 

5-NLP/13-TextRep/README.md

@@ -45,4 +45,4 @@ However, none of those approaches can fully take into account the semantics of t
 ## Continue to Notebooks
 
 * [Text Representation with PyTorch](TextRepresentationPyTorch.ipynb)
-* [Text Representation with Tensorflow](TextRepresentationTF.ipynb)
+* [Text Representation with TensorFlow](TextRepresentationTF.ipynb)

5-NLP/14-Embeddings/README.md

@@ -13,7 +13,7 @@ By using embedding layer as a first layer in our classifier network, we can swit
 ## Continue in Notebooks
 
 * [Embeddings with PyTorch](EmbeddingsPyTorch.ipynb)
-* [Embeddings Tensorflow](EmbeddingsTF.ipynb)
+* [Embeddings TensorFlow](EmbeddingsTF.ipynb)
 
 ## Semantic Embeddings: Word2Vec
 

5-NLP/15-LanguageModeling/README.md

@@ -16,5 +16,5 @@ The idea of CBoW is exactly predicting a missing word, however, to do this we ta
 ## More Info
 
 * [Official PyTorch tutorial on Language Modeling](https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html). 
-* [Official TensorFlow tutorial on training Word2Vec model](https://www.tensorflow.org/tutorials/text/word2vec).
+* [Official TensorFlow tutorial on training Word2Vec model](https://www.TensorFlow.org/tutorials/text/word2vec).
 * Using **gensim** framework to train most commonly used embeddings in a few lines of code is as described [in this documentation](https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html).

5-NLP/16-RNN/README.md

@@ -50,7 +50,7 @@ Recurrent network, one-directional or bidirectional, captures certain patterns w
 ## Continue to Notebooks
 
 * [RNNs with PyTorch](RNNPyTorch.ipynb)
-* [RNNs with Tensorflow](RNNTF.ipynb)
+* [RNNs with TensorFlow](RNNTF.ipynb)
 
 ## RNNs for other tasks
 

5-NLP/17-GenerativeNetworks/README.md

@@ -27,7 +27,7 @@ When generating text (during inference), we start with some **prompt**, which is
 ## Continue to Notebooks
 
 * [Generative Networks with PyTorch](GenerativePyTorch.ipynb)
-* [Generative Networks with Tensorflow](GenerativeTF.ipynb)
+* [Generative Networks with TensorFlow](GenerativeTF.ipynb)
 
 ## Soft text generation and temperature
 

5-NLP/18-Transformers/README.md

@@ -55,7 +55,7 @@ Next, we need to capture some patterns within our sequence. To do this, transfor
 
 In transformers, we use **Multi-Head Attention**, in order to give network the power to capture several different types of dependencies, eg. long-term vs. short-term word relations, co-reference vs. something else, etc. 
 
-[Tensorflow Notebook](TransformersTF.ipynb) contains more detains on the implementation of transformer layers.
+[TensorFlow Notebook](TransformersTF.ipynb) contains more detains on the implementation of transformer layers.
 
 ### Encoder-Decoder Attention
 
@@ -75,12 +75,12 @@ Since each input position is mapped independently to each output position, trans
 
 ![picture from http://jalammar.github.io/illustrated-bert/](images/jalammarBERT-language-modeling-masked-lm.png)
 
-There are many variations of Transformer architectures including BERT, DistilBERT. BigBird, OpenGPT3 and more that can be fine tuned. The [HuggingFace package](https://github.com/huggingface/) provides repository for training many of these architectures with both PyTorch and Tensorflow.
+There are many variations of Transformer architectures including BERT, DistilBERT. BigBird, OpenGPT3 and more that can be fine tuned. The [HuggingFace package](https://github.com/huggingface/) provides repository for training many of these architectures with both PyTorch and TensorFlow.
 
 ## Continue to Notebooks
 
 * [Transformers in PyTorch](TransformersPyTorch.ipynb)
-* [Transformers in Tensorflow](TransformersTF.ipynb)
+* [Transformers in TensorFlow](TransformersTF.ipynb)
 
 ## Related materials
 

5-NLP/README.md

@@ -28,7 +28,7 @@ If you are using local Python installation to run this course, you may need to i
 ```bash
 pip install -r requirements-torch.txt
 ```
-**For Tensorflow**
+**For TensorFlow**
 ```bash
 pip install -r requirements-tf.txt
 ```
@@ -39,7 +39,7 @@ In this section, in some of the examples we will be training quite large models.
 
 When running on GPU, you may experience situations when you run out of GPU memory. During training, the amount of GPU memory consumed depends on many factors, including minibatch size. If you experience any memory problems - you may try to minimize the minibatch size in the code.
 
-Also, some older versions of Tensorflow do not release GPU memory correctly if we are training multiple models in one Python kernel. In order to use GPU memory cautiously, you may set tensorflow option to grow GPU memory allocation only when required. You would need to include the following code in your notebooks:
+Also, some older versions of TensorFlow do not release GPU memory correctly if we are training multiple models in one Python kernel. In order to use GPU memory cautiously, you may set TensorFlow option to grow GPU memory allocation only when required. You would need to include the following code in your notebooks:
 
 ```python
 physical_devices = tf.config.list_physical_devices('GPU') 

5-NLP/requirements-tf.txt

@@ -7,7 +7,7 @@ opencv-python==4.5.1.48
 Pillow==7.1.2
 scikit-learn
 scipy
-tensorflow
-tensorflow_datasets
-tensorflow_text
+TensorFlow
+TensorFlow_datasets
+TensorFlow_text
 transformers==4.3.3

Mindmap.md

@@ -14,7 +14,7 @@
  - [Multi-Layered Networks](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/04-OwnFramework/README.md)
  - [Intro to Frameworks](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/README.md)
    - [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb)
-   - [Tensorflow](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md)
+   - [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md)
    - [Overfitting](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/Overfitting.md)
 
 ## Computer Vision

README.md

@@ -13,7 +13,7 @@
 Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Artificial Intelligence**. In this curriculum, you will learn:
 
 * Different approaches to Artificial Intelligence, including "good old" symbolic approach with **Knowledge Representation** and reasoning.
-* **Neural Networks** and **Deep Learning**, which are at the core of modern AI. We will try to illustrate all ideas using code in two most popular frameworks - Tensorflow(http://tensorflow.org) and PyTorch(http://pytorch.org).
+* **Neural Networks** and **Deep Learning**, which are at the core of modern AI. We will try to illustrate all ideas using code in two most popular frameworks - TensorFlow(http://TensorFlow.org) and PyTorch(http://pytorch.org).
 * **Neural Architectures** for working with Images and Text. We will try to cover very recent models, but may lack a little bit on state-of-the-art.
 * Less popular AI approaches, such as **Genetic Algorithms**
 
@@ -29,7 +29,7 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking [Ge
 # Content
 
 <table>
-<tr><th>No</th><th>Lesson</th><th>Intro</th><th>PyTorch</th><th>Keras/Tensorflow</th><th>Lab</th></tr>
+<tr><th>No</th><th>Lesson</th><th>Intro</th><th>PyTorch</th><th>Keras/TensorFlow</th><th>Lab</th></tr>
 
 <tr><td>I</td><td colspan="4"><b>Introduction to AI</b></td><td>PAT</td></tr>
 <tr><td>1</td><td>Introduction and History of AI</td><td><a href="1-Intro/README.md">Text</a></td><td></td><td></td><td></td></tr>
@@ -42,42 +42,42 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking [Ge
    <td colspan="2"><a href="3-NeuralNetworks/03-Perceptron/Perceptron.ipynb">Notebook</a></td><td></td></tr>
 <tr><td>4 </td><td>Multi-Layered Perceptron and Creating our own Framework</td><td><a href="3-NeuralNetworks/04-OwnFramework/README.md">Text</a></td><td colspan="2"><a href="3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb">Notebook</a><td></td></tr> 
 <tr><td>5</td>
-   <td>Intro to Frameworks (PyTorch/Tensorflow)<br/>Overfitting</td>
+   <td>Intro to Frameworks (PyTorch/TensorFlow)<br/>Overfitting</td>
    <td><a href="3-NeuralNetworks/05-Frameworks/README.md">Text</a><br/><a href="3-NeuralNetworks/05-Frameworks/Overfitting.md">Text</a></td>
    <td><a href="3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb">PyTorch</td>
-   <td><a href="3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/Tensorflow</td><td></td></tr>
+   <td><a href="3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/TensorFlow</td><td></td></tr>
 <tr><td>IV</td><td colspan="2"><b><a href="4-ComputerVision/README.md">Computer Vision</a></b></td>
   <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
-  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-tensorflow/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
+  <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
   <td>PAT</td></tr>
 <tr><td>6</td><td>Intro to Computer Vision. OpenCV</td><td>Text<td colspan="2">Notebook</td><td></td></tr>
-<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures<br/>Training Tricks</td><td><a href="4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">Tensorflow</a></td><td></td></tr>
-<tr><td>8</td><td>Pre-trained Networks and Transfer Learning</td><td><a href="4-ComputerVision/08-TransferLearning/README.md">Text</a><br/><a href="4-ComputerVision/08-TransferLearning/TrainingTricks.md">Text</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb">Tensorflow</a><br/><a href="4-ComputerVision/08-TransferLearning/Dropout.ipynb">Dropout sample</a></td><td></td></tr>
-<tr><td>9</td><td>Autoencoders and VAEs</td><td><a href="4-ComputerVision/09-Autoencoders/README.md">Text</a></td><td>PyTorch</td><td><a href="4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb">Tensorflow</a></td><td></td></tr>
-<tr><td>10</td><td>Generative Adversarial Networks</td><td><a href="4-ComputerVision/10-GANs/README.md">Text</a></td><td>PyTorch</td><td><a href="4-ComputerVision/10-GANs/GANs.ipynb">Tensorflow</a></td><td></td></tr>
-<tr><td>11</td><td>Object Detection</td><td>Text</td><td>PyTorch</td><td>Tensorflow</td><td></td></tr>
-<tr><td>12</td><td>Instance Segmentation. U-Net</td><td>Text</td><td>PyTorch</td><td>Tensorflow</td><td></td></tr>
+<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures<br/>Training Tricks</td><td><a href="4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>8</td><td>Pre-trained Networks and Transfer Learning</td><td><a href="4-ComputerVision/08-TransferLearning/README.md">Text</a><br/><a href="4-ComputerVision/08-TransferLearning/TrainingTricks.md">Text</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb">TensorFlow</a><br/><a href="4-ComputerVision/08-TransferLearning/Dropout.ipynb">Dropout sample</a></td><td></td></tr>
+<tr><td>9</td><td>Autoencoders and VAEs</td><td><a href="4-ComputerVision/09-Autoencoders/README.md">Text</a></td><td>PyTorch</td><td><a href="4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>10</td><td>Generative Adversarial Networks</td><td><a href="4-ComputerVision/10-GANs/README.md">Text</a></td><td>PyTorch</td><td><a href="4-ComputerVision/10-GANs/GANs.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>11</td><td>Object Detection</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
+<tr><td>12</td><td>Instance Segmentation. U-Net</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
 <tr><td>V</td><td colspan="2"><b><a href="5-NLP/README.md">Natural Language Processing</a></b></td>
    <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
-   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-tensorflow/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
+   <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
    <td>PAT</td></tr>
-<tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="5-NLP/13-TextRep/README.md">Text</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationTF.ipynb">Tensorflow</td><td></td></tr>
-<tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="5-NLP/14-Embeddings/README.md">Text</td><td><a href="5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/14-Embeddings/EmbeddingsTF.ipynb">Tensorflow</a></td><td></td></tr>
-<tr><td>15</td><td>Language Modeling. Training your own embeddings</td><td><a href="5-NLP/15-LanguageModeling">Text</a></td><td>PyTorch</td><td>Tensorflow</td><td></td></tr>
-<tr><td>16</td><td>Recurrent Neural Networks</td><td><a href="5-NLP/16-RNN/README.md">Text</a></td><td><a href="5-NLP/16-RNN/RNNPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/16-RNN/RNNTF.ipynb">Tensorflow</a></td><td></td></tr>
-<tr><td>17</td><td>Generative Recurrent Networks</td><td><a href="5-NLP/17-GenerativeNetworks/README.md">Text</a></td><td><a href="5-NLP/17-GenerativeNetworks/GenerativePyTorch.md">PyTorch</a></td><td><a href="5-NLP/17-GenerativeNetworks/GenerativeTF.md">Tensorflow</a></td><td></td></tr>
-<tr><td>18</td><td>Transformers. BERT.</td><td><a href="5-NLP/18-Transformers/README.md">Text</a></td><td><a href="5-NLP/18-Transformers/TransformersPyTorch.md">PyTorch</a></td><td><a href="5-NLP/18-Transformers/TransformersTF.md">Tensorflow</a></td><td></td></tr>
-<tr><td>19</td><td>Named Entity Recognition</td><td>Text</td><td>PyTorch</td><td>Tensorflow</td><td></td></tr>
-<tr><td>20</td><td>Text Generation using GPT</td><td>Text</td><td>PyTorch</td><td>Tensorflow</td><td></td></tr>
+<tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="5-NLP/13-TextRep/README.md">Text</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
+<tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="5-NLP/14-Embeddings/README.md">Text</td><td><a href="5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>15</td><td>Language Modeling. Training your own embeddings</td><td><a href="5-NLP/15-LanguageModeling">Text</a></td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
+<tr><td>16</td><td>Recurrent Neural Networks</td><td><a href="5-NLP/16-RNN/README.md">Text</a></td><td><a href="5-NLP/16-RNN/RNNPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/16-RNN/RNNTF.ipynb">TensorFlow</a></td><td></td></tr>
+<tr><td>17</td><td>Generative Recurrent Networks</td><td><a href="5-NLP/17-GenerativeNetworks/README.md">Text</a></td><td><a href="5-NLP/17-GenerativeNetworks/GenerativePyTorch.md">PyTorch</a></td><td><a href="5-NLP/17-GenerativeNetworks/GenerativeTF.md">TensorFlow</a></td><td></td></tr>
+<tr><td>18</td><td>Transformers. BERT.</td><td><a href="5-NLP/18-Transformers/README.md">Text</a></td><td><a href="5-NLP/18-Transformers/TransformersPyTorch.md">PyTorch</a></td><td><a href="5-NLP/18-Transformers/TransformersTF.md">TensorFlow</a></td><td></td></tr>
+<tr><td>19</td><td>Named Entity Recognition</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
+<tr><td>20</td><td>Text Generation using GPT</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
 <tr><td>VI</td><td colspan="4"><b>Other AI Techniques</b></td><td>PAT</td></tr>
 <tr><td>21</td><td>Genetic Algorithms</td><td>Text<td colspan="2">Notebook</td><td></td></tr>
-<tr><td>22</td><td>Deep Reinforcement Learning</td><td>Text</td><td>PyTorch</td><td>Tensorflow</td><td></td></tr>
+<tr><td>22</td><td>Deep Reinforcement Learning</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
 <tr><td>23</td><td>Multi-Agent Systems</td><td>Text</td><td></td><td></td><td></td></tr>
 <tr><td>VII</td><td colspan="4"><b>AI Ethics</b></td><td>PAT</td></tr>
 <tr><td>24</td><td>AI Ethics and Responsible AI</td><td>Text</td><td></td><td></td><td></td></tr>
 </table>
 
-Each lesson contains some pre-reading material (linked as **Text** above), and some executable Jupyter Notebooks, which are often specific to the framework (**PyTorch** or **Tensorflow**). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebooks (either PyTorch or Tensorflow). There are also **Labs** available for some topics, which give you an opportunity to try applying the material you have learnt to some specific problem. 
+Each lesson contains some pre-reading material (linked as **Text** above), and some executable Jupyter Notebooks, which are often specific to the framework (**PyTorch** or **TensorFlow**). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebooks (either PyTorch or TensorFlow). There are also **Labs** available for some topics, which give you an opportunity to try applying the material you have learnt to some specific problem. 
 
 Some sections also contain links to **MS Learn** modules that cover related topics. Microsoft Learn provides convenient GPU-enabled learning environment, although in terms of content you can expect this curriculum to go a bit deeper.
 
@@ -92,7 +92,7 @@ However, if you are serious about the course, we suggest to fork the entire repo
 
 - Start with a pre-lecture quiz.
 - Read the intro text for the lecture 
-- If the lecture has additional notebooks, go through them, reading and executing the code. If both Tensorflow and PyTorch notebooks are provided, you can focus on one of them - chose your favourite framework.
+- If the lecture has additional notebooks, go through them, reading and executing the code. If both TensorFlow and PyTorch notebooks are provided, you can focus on one of them - chose your favourite framework.
 - Notebooks often contain some of the challenges that require you to tweak the code a little bit to experiment. Do not by lazy and do it!
 - Take the post-lecture quiz.
 - If there is a lab attached to the module - complete the assignment.