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Seaborn – Basics Of Seaborn & Setup
Seaborn – Basics of Seaborn & Setup Table Of Contents: What is Seaborn? Why use it over Matplotlib? Installing Seaborn (pip install seaborn) Importing Seaborn & Matplotlib (import seaborn as sns) Understanding Seaborn’s built-in datasets (sns.get_dataset_names()) Loading datasets (sns.load_dataset(“tips”)) (1) What is Seaborn? Why Use It Over Matplotlib? What Is Seaborn? Why Use It Over Matplotlib ? (2) Installing Seaborn Library pip install seaborn (3) Verify The Installation pip show seaborn (4) Importing Seaborn & Matplotlib (import seaborn as sns) import seaborn as sns import matplotlib.pyplot as plt (5) Understanding Seaborn’s Built-in Datasets. import seaborn as sns # Get The
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Seaborn – Syllabus For Data Visualization
Seaborn – Learning Syllabus Table Of Contents: Basics of Seaborn & Setup Basic Plot Types Advanced Statistical Plots Pairwise Relationships & Correlation Plots Categorical Data Visualization Regression & Trend Analysis Customizing Seaborn Plots Working with Large Datasets & Complex Visualizations (1) Basics of Seaborn & Setup What is Seaborn? Why use it over Matplotlib? Installing Seaborn (pip install seaborn) Importing Seaborn & Matplotlib (import seaborn as sns) Understanding Seaborn’s built-in datasets (sns.get_dataset_names()) Loading datasets (sns.load_dataset(“tips”)) (2) Basic Plot Types Line Plot (sns.lineplot()) Scatter Plot (sns.scatterplot()) Bar Plot (sns.barplot()) Count Plot (sns.countplot()) Histogram (sns.histplot()) KDE Plot (sns.kdeplot()) (3) Advanced Statistical Plots
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Q & A – AWS Model Deployment
Q & A – AWS Model Deployement Table Of Contents: General ML Deployment Questions. Docker & Containerization. Cloud Deployment (AWS, GCP, Azure). API & Flask Integration. Scaling & Optimization. CI/CD & Automation. Security & Monitoring. (1) General ML Deployment Questions. What are the different ways to deploy a machine learning model? What is the difference between batch inference and real-time inference? What are the advantages and disadvantages of deploying models using Docker? What are edge AI deployments, and when should you use them? How do you handle model versioning in deployment? (2) General ML Deployment Questions. What is a Docker
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AWS – How To Deploy ML Model Using Sagemaker Endpoint Using Docker Container?
GenAI – How To Deploy ML Model Using Sagemaker Endpoint Using Docker Container? Table Of Content: Step By Step Process Of Deployment. (1) Step By Step Process Of Deployment. Step-1: Create A ECR Repository Step-2: Push The Docker Image Into The Repository. Step-3: Attach The Image To Sagemaker Notebook Step-4: Host The Created Image On Sagemaker End Point. (2) How The Sagemaker Will Invoke The Docker Container ? (3) – How Sagemaker Internally Works ? (4) – How Sagemaker Calls The Docker Container Internally ? (5) – If There Are ‘N’ Different Containers Then How Sagemaker Will Decide Which Container
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Transformer – Prediction Process
Transformer – Transformer Prediction Table Of Contents: Prediction Setup Of Transformer. Step By Step Flow Of Input Sentence, “We Are Friends!”. Decoder Processing For Other Timestep (1) Prediction Setup For Transformer. Input Dataset: For simplicity we will take this 3 rows as input but in reality we will have thousands of rows as input. We will use these dataset to train our Transformer model. Query Sentence: We will pass this sentence for translation, Sentence = “We Are Friends !” (2) Step By Step Flow Of Input Sentence, “We Are Friends!”. Transformer is mainly divides into Encoder and Decoder. Encoder will
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Transformer – Decoder Architecture
Transformer – Decoder Architecture Table Of Contents: What Is The Work Of Decoder In Transformer ? Overall Decoder Architecture. Understanding Decoder Work Flow With An Example. Understanding Decoder 2nd Part. (1) What Is The Work Of Decoder In Transformer ? In a Transformer model, the Decoder plays a crucial role in generating output sequences from the encoded input. It is mainly used in sequence-to-sequence (Seq2Seq) tasks such as machine translation, text generation, and summarization. (2) Overall Decoder Architecture. In the original paper of Transformer we have 6 decoder module connected in series. The output from one decoder module will be
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Transformers – Cross Attention
Transformer – Cross Attention Table Of Contents: Where Is Cross Attention Block Is Applied In Transformers? What Is Cross Attention ? How Cross Attention Works? Where We Use Cross Attention Mechanism. (1) Where Is Cross Attention Block Is Applied In Transformers? In the diagram above you can see that, the Multi-Head Attention is known as “Cross Attention”. The difference to the other “Multi Head Attention” block is that for other the 3 inputs Query, Key and Value vectors are generated from a single source but in this Cross Attention block the Query vector is coming from the Decoder block and
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Transformer – Masked Self Attention
Transformer – Masked Self Attention Table Of Contents: Transformer Decoder Definition. What Is Autoregressive Model? Lets Prove The Transformer Decoder Definition. How To Implement The Parallel Processing Logic While Training The Transformer Decoder? Implementing Masked Self Attention. (1) Transformer Decoder Definition From this above definition we can we can understand that the Transformer behaves Autoregressive while prediction and Non Auto Regressive while training. This is displayed in the diagram below. (2) What Is Autoregressive Model? Suppose you are making a Machine Learning model which work is predict the stock price, Monday it has predicted 29, Tuesday = 25 for to
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AWS – How To Deploy ML Model Using Sagemaker Endpoint?
AWS – How To Deploy ML Model Using Sagemaker Endpoint Using Prebuild Container? Table Of Contents: Setup AWS & Install Dependencies. Train & Save The Model. Create A Docker Container. Push The Docker Image To Amazon ECR. Deploy The Model To Sagemaker Endpoint. Make Prediction Using Endpoint Cleanup The Resources. (1) Setup AWS & Install Dependencies. AWS Dependencies: Ensure You Have The The Following Dependencies Installed. An AWS account with SageMaker and ECR permissions. Docker installed (docker – version). AWS CLI configured (aws configure). Boto3 and SageMaker SDK installed. Python Libraries: Python libraries to build the model. pip install boto3
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Transformers – Encoder Architecture
Transformers – Encoder Architecture Table Of Contents: What Is Encoder In Transformer? Internal Workings Of Encoder Module. How Encoder Module Works With An Example. Why We Use Addition Operation With The Original Input Again In Encoder Module? (1) What Is Encoder In Transformer? In a Transformer model, the Encoder is responsible for processing input data (like a sentence) and transforming it into a meaningful contextual representation that can be used by the Decoder (in tasks like translation) or directly for classification. Encoding is necessary because, it, Transforms words into numerical format (embeddings). Allows self-attention to analyze relationships between words. Adds
