Update pages/Intro_DS.py

pages/Intro_DS.py

@@ -89,7 +89,24 @@ st.markdown("6.Generative Adversarial Networks (GANs")
 st.markdown("<h3 style='text-align: left; color: Black;'>Examples of DL </h3>", unsafe_allow_html=True)
 st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
 "IMage Recognition (Computer Vision):Convolutional Neural Networks (CNNs) are utilised in face recognition systems, self-driving cars, and even medical imaging (tumour identification in X-rays or MRI scans).Real-World Applications: Instagram and Facebook utilise deep learning to automatically tag individuals in images."
-"</p>", unsafe_allow_html=True)     
+"</p>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: Black;'>What is GenerativeAI </h2>", unsafe_allow_html=True)
+st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
+"Generative AI is a class of artificial intelligence systems capable of creating new material, such as text, photos, music, or even video, that resembles or replicates real-world data. Unlike classical AI, which is frequently used for tasks like as classification or prediction (e.g., determining if a picture contains a cat or a dog), Generative AI generates new data by learning the patterns and structures of current data."
+"</p>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: left; color: Black;'>Types of GenerativeAI </h3>", unsafe_allow_html=True)
+st.markdown("1.Generative Adversarial Networks (GANs)")
+st.markdown("2.Variational Autoencoders (VAEs)")
+st.markdown("3.Autoregressive Models")
+st.markdown("4.Transformers ")
+st.markdown("<h3 style='text-align: left; color: Black;'>Examples of GenAI </h3>", unsafe_allow_html=True)
+st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
+"Deepfake (Video Generation):How It Works: GANs are often used to generate deepfakes, which are synthetic media (mainly videos) in which a person's image is transformed to make it look as if they are talking or doing something they never did. These algorithms learn from real-world footage to produce believable changes.
+Real-World Applications: Deepfakes have been utilised for amusement (e.g., movies, impersonations), but they also raise questions regarding misrepresentation."  
+"</p>", unsafe_allow_html=True)
+
+
+