diff --git a/README.md b/README.md
index 6499bb3..8e6fbce 100644
--- a/README.md
+++ b/README.md
@@ -93,10 +93,14 @@ result = gan.generate(100)
 
 ## :anchor: Tutorials
 
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial "[Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)"
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial on using [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb)
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial on [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb)
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial on using [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1l2VB6eUwvrxyu8iB30faGiQM5AKthc82?usp=sharing) Introductory Tutorial [Getting started with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1frSnJQSsuPS3asgIkmcrNtX4Y7TIQI56?usp=sharing) Tutorial [Datasets in TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Datasets.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Vw9t4TlI1Nek_t6bMPyKcPPPqCiXfOK3?usp=sharing) Tutorial [Time Series Augmentations](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/augmentations.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1_jpGrPcwoSpB8eii8XW-spaikczdPqIQ?usp=sharing) Tutorial [Time Series Generation with VAEs](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/VAEs/VAE.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rcN3pr8uglBEEOo4bTa1fvXgSMx1vKq9?usp=sharing) Tutorial [Conditional Time Series Generation with GANs](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/GANs/cGAN.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hubtddSX94KyLzuCTwmU6pAFBgBeiEB-?usp=sharing) Tutorial [Evaluation of Synthetic Time Series Data](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/evaluation.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SKD9hRi-ic27Wts9Qzkssjfe1z7o1NU4?usp=sharing) Tutorial [Model Selection](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Model%20Selection.ipynb)
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wpf9WeNVj5TkUcPF6EavVx-hUCOfyvUd?usp=sharing) Tutorial [Multiple GPUs or TPU with TSGM](https://github.com/AlexanderVNikitin/tsgm/blob/main/tutorials/Using%20Multiple%20GPUs%20or%20TPU.ipynb)
 
 For more examples, see [our tutorials](./tutorials).
 
diff --git a/tutorials/GANs/cGAN.ipynb b/tutorials/GANs/cGAN.ipynb
index 8f41078..c074697 100644
--- a/tutorials/GANs/cGAN.ipynb
+++ b/tutorials/GANs/cGAN.ipynb
@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd5a3eaf",
+   "metadata": {},
+   "source": [
+    "# Conditional Time Series Generation with GANs\n",
+    "This tutorial covers the generation of a temporal dataset where each time series has a class label assigned."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -12,16 +21,8 @@
     "\n",
     "import numpy as np\n",
     "\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10e3e196",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "from tensorflow import keras\n",
     "from tensorflow.keras import layers\n",
     "\n",
@@ -35,12 +36,12 @@
    "id": "843de5f0",
    "metadata": {},
    "source": [
-    "We want to generate a temporal dataset where each time series belongs to one of two classes. Let's go step by step through the solution.\n",
+    "We aim to generate a temporal dataset where each time series belongs to one of two classes. Let's go through the solution step by step.\n",
     "\n",
     "#### 1. Define parameters of GAN:\n",
-    "First, we are defining the parameters of GAN, and the training algorithm.\n",
-    "- `latent_dim` is the size of input noise in GAN,\n",
-    "- `output_dim` is the number of classes, which, mentioned above is two,\n",
+    "First, we need to define the parameters for the Generative Adversarial Network (GAN) and its training algorithm:\n",
+    "- `latent_dim` is the size of the input noise in GAN,\n",
+    "- `output_dim` is the number of output classes, which in this case is two.,\n",
     "- `feature_dim` is the number of time series features,\n",
     "- `seq_len` is the length of the time series."
    ]
@@ -69,7 +70,7 @@
    "metadata": {},
    "source": [
     "#### 2. Load data:\n",
-    "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_vs_const_dataset` to generate the data. Next, we featurewise scale the dataset so that each feature is in $[-1, 1]$, using `tsgm.utils.TSFeatureWiseScaler`."
+    "We will generate a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_vs_const_dataset` to generate the data. After generating the data, we will scale each feature to be within the range of $[-1, 1]$, using `tsgm.utils.TSFeatureWiseScaler`."
    ]
   },
   {
@@ -98,7 +99,7 @@
    "metadata": {},
    "source": [
     "#### 3. Visually explore the dataset.\n",
-    "There are many tools for convenient visualizations of temporal datasets. Here, we use `tsgm.utils.visualize_ts_lineplot`, which is convenient for TS classification datasets."
+    "There are many tools for convenient visualizations of temporal datasets in `tsgm,utils`. Here, we use `tsgm.utils.visualize_ts_lineplot`, which is convenient for TS classification datasets."
    ]
   },
   {
@@ -119,7 +120,7 @@
    "metadata": {},
    "source": [
     "#### 4. Choose architecture.\n",
-    "Here, you can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `tf` models."
+    "Here, one can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `keras` models."
    ]
   },
   {
@@ -141,7 +142,7 @@
    "metadata": {},
    "source": [
     "#### 5. Define model and train it.\n",
-    "We define conditional GAN model (`tsgm.models.cgan.ConditionalGAN`), compile it (here, you can choose different optimizers for discriminator and generator), and train using `.fit` model. Additionally, we use `tsgm.models.monitors.GANMonitor` to track training process."
+    "We define a conditional GAN model (`tsgm.models.cgan.ConditionalGAN`), compile it (here, one can choose different optimizers for discriminator and generator), and train using `.fit` model. Additionally, we employ `tsgm.models.monitors.GANMonitor` to monitor and track the training process, ensuring we can observe the model's progress and performance."
    ]
   },
   {
diff --git a/tutorials/VAEs/VAE.ipynb b/tutorials/VAEs/VAE.ipynb
index fa12c9a..d0acc7d 100644
--- a/tutorials/VAEs/VAE.ipynb
+++ b/tutorials/VAEs/VAE.ipynb
@@ -5,7 +5,7 @@
    "id": "60611bed",
    "metadata": {},
    "source": [
-    "## Time series generation using VAEs.\n",
+    "# Time series generation using VAEs\n",
     "This is a minimal example of unsupervised time series generation using VAEs."
    ]
   },
@@ -36,7 +36,7 @@
    "metadata": {},
    "source": [
     "#### 1. Choose architecture of encoder and decoder.\n",
-    "Here, you can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `tf` models."
+    "Here, you can either use one of the architectures presented in `tsgm.models.architectures`, or define custom discriminator and generator architectures as `keras` models."
    ]
   },
   {
@@ -56,7 +56,7 @@
    "metadata": {},
    "source": [
     "#### 2. Load data:\n",
-    "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_dataset` to generate the data. Next, we featurewise scale the dataset so that each feature is in $[0, 1]$, using `tsgm.utils.TSFeatureWiseScaler`."
+    "We are working with a toy dataset, and use `tsgm` utility called `tsgm.utils.gen_sine_dataset` to generate the data. Next, we feature-wise scale the dataset so that each feature is in $[0, 1]$, using `tsgm.utils.TSFeatureWiseScaler`."
    ]
   },
   {
@@ -77,7 +77,7 @@
    "metadata": {},
    "source": [
     "#### 3. Define model and train it.\n",
-    "We define conditional GAN model (`tsgm.models.cvae.BetaVAE`), compile it, and train using `.fit` model. Additionally, we use `tsgm.models.monitors.GANMonitor` to track training process."
+    "We define a conditional GAN model (`tsgm.models.cvae.BetaVAE`), compile it, and train using `.fit` model."
    ]
   },
   {
@@ -99,7 +99,7 @@
    "metadata": {},
    "source": [
     "#### 4. Check reconstruction of the data.\n",
-    "We reconstruct data using `vae.predict(scaled_data)`. For validating that VAE works, we check that original and reconstructed datasets are visually similar using `tsgm.utils.visualize_original_and_reconst_ts`."
+    "We reconstruct data using `vae.predict(scaled_data)`. For validating that VAE works, we check that the original and reconstructed datasets are visually similar using `tsgm.utils.visualize_original_and_reconst_ts`."
    ]
   },
   {