Update pages/Intro_DS.py

pages/Intro_DS.py

@@ -65,17 +65,35 @@ st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
             "</p>", 
     unsafe_allow_html=True)
 st.markdown("<h3 style='text-align: left; color: Black;'>Types of Machine Learning </h3>", unsafe_allow_html=True)
-st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
-           "Supervised Learning"
-           "Unsupervised Learning"
-           "Reinforcement Learning"
-           "Semi-Supervised Learning")
+st.markdown("1.Supervised Learning")
+st.markdown("2Unsupervised Learning")
+st.markdown("3.Reinforcement Learning")
+st.markdown("4.Semi-Supervised Learning")
 st.markdown("<h3 style='text-align: left; color: Black;'>Examples of ML </h3>",unsafe_allow_html=True)
 st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
 "Recommendation Systems (Netflix, YouTube, and Amazon):How It Works: Machine learning models use your previous behaviour (e.g., what you viewed or purchased) to propose new content or goods that you might enjoy.For example, Netflix recommends episodes based on what you've viewed, while Amazon sells things based on your browsing history."
 "Fraud detection (banking and credit cards),How it works: Machine learning algorithms examine transaction data for unexpected patterns that might signal fraud.For example, credit card firms utilise machine learning to detect potentially fraudulent transactions in real time, such as a card being used in two different places within minutes."
 "</p>", 
     unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: Black;'>Deep Learning (AI) </h2>", unsafe_allow_html=True)
+st.markdown("<p style='font-size: 16px; color: Blue; font-style: italic;'>"
+"Deep learning models are made up of several layers of artificial neurones, each of which gradually modifies the data. These layers are organised into a neural network, and the network's internal parameters (weights) are adjusted during training to reduce prediction error. Backpropagation is a common way for accomplishing this."
+"</p>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: left; color: Black;'>Types of Deep Learning </h3>", unsafe_allow_html=True)
+st.markdown("1.Feedforward Neural Networks (FNN)")
+st.markdown("2.Convolutional Neural Networks (CNN)")
+st.markdown("3.Recurrent Neural Networks (RNN)")
+st.markdown("4.Long Short-Term Memory (LSTM) Networks")
+st.markdown("5.Generative Adversarial Networks (GANs")
+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)     
+
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