TensorFlow Basic to Advanced Training

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Free Download TensorFlow Basic to Advanced Training
Published 11/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 33m | Size: 968 MB
Flexible, Scalable, Open-Source Machine Learning Framework

What you'll learn
Core TensorFlow concepts from setup to model building, enabling them to confidently create machine learning projects.
Techniques for building CNNs and RNNs for image, language, and sequence data, equipping them to tackle various ML problems.
Skills to deploy TensorFlow models to production, including scaling with distributed computing and deploying on mobile.
Practical experience with real-world ML applications, building models for image recognition, sentiment analysis, and more.
Requirements
Basic programming knowledge, ideally in Python
Understanding of fundamental math concepts like linear algebra and probability
Familiarity with machine learning basics is helpful but not required
A computer with internet access for installing TensorFlow and coding projects
Description
This course offers a comprehensive journey into TensorFlow, guiding learners from the basics to advanced applications of machine learning and deep learning with this powerful open-source framework. Starting with an introduction to machine learning and the unique capabilities of TensorFlow, students will gain foundational knowledge that sets the stage for more complex concepts. The course begins with installation and setup instructions to ensure every student is equipped with the necessary tools and environment for TensorFlow development. Early modules cover the essential building blocks of TensorFlow, including tensors, operations, computational graphs, and sessions. Through these topics, students will understand the core components of TensorFlow and how to utilize them effectively for simple projects and data operations.As the course progresses, learners dive deeper into neural networks, exploring how to build, train, and optimize basic models. The intermediate section introduces Keras, the user-friendly API for TensorFlow, allowing students to design and train complex models more intuitively. Topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) provide hands-on experience with real-world data types, such as images and sequences. The course then transitions to advanced topics, covering essential skills for deploying and scaling models. Students will learn to save, load, and serve TensorFlow models, enabling them to apply their knowledge in production environments. They'll also explore distributed TensorFlow for scaling applications across multiple devices and TensorFlow Extended (TFX) for building end-to-end machine learning pipelines.With practical projects and real-world applications woven throughout, students will have the chance to build models for tasks like image classification, sentiment analysis, and time series prediction, solidifying their skills through hands-on practice. By the end of the course, learners will be equipped not only with the technical knowledge but also the practical experience needed to implement, deploy, and manage TensorFlow models in professional environments. This course is ideal for anyone looking to advance their career in data science, machine learning, or artificial intelligence, empowering them with the expertise to tackle complex challenges in today's data-driven world.
Who this course is for
Aspiring Data Scientists and ML Engineers who want to build a solid foundation in TensorFlow for real-world machine learning projects
Developers and Programmers interested in expanding their skills to include machine learning and neural networks
Students and Professionals in data science, AI, or related fields, looking to add TensorFlow to their toolkit
Self-Learners who enjoy hands-on projects and are ready to dive into practical, scalable applications in machine learning
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Tensorflow: Basic To Advanced Training
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 687.81 MB | Duration: 4h 33m​

Flexible, Scalable, Open-Source Machine Learning Framework

What you'll learn

Core TensorFlow concepts from setup to model building, enabling them to confidently create machine learning projects.

Techniques for building CNNs and RNNs for image, language, and sequence data, equipping them to tackle various ML problems.

Skills to deploy TensorFlow models to production, including scaling with distributed computing and deploying on mobile.

Practical experience with real-world ML applications, building models for image recognition, sentiment analysis, and more.

Requirements

Basic programming knowledge, ideally in Python

Understanding of fundamental math concepts like linear algebra and probability

Familiarity with machine learning basics is helpful but not required

A computer with internet access for installing TensorFlow and coding projects

Description

This course offers a comprehensive journey into TensorFlow, guiding learners from the basics to advanced applications of machine learning and deep learning with this powerful open-source framework. Starting with an introduction to machine learning and the unique capabilities of TensorFlow, students will gain foundational knowledge that sets the stage for more complex concepts. The course begins with installation and setup instructions to ensure every student is equipped with the necessary tools and environment for TensorFlow development. Early modules cover the essential building blocks of TensorFlow, including tensors, operations, computational graphs, and sessions. Through these topics, students will understand the core components of TensorFlow and how to utilize them effectively for simple projects and data operations.As the course progresses, learners dive deeper into neural networks, exploring how to build, train, and optimize basic models. The intermediate section introduces Keras, the user-friendly API for TensorFlow, allowing students to design and train complex models more intuitively. Topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) provide hands-on experience with real-world data types, such as images and sequences. The course then transitions to advanced topics, covering essential skills for deploying and scaling models. Students will learn to save, load, and serve TensorFlow models, enabling them to apply their knowledge in production environments. They'll also explore distributed TensorFlow for scaling applications across multiple devices and TensorFlow Extended (TFX) for building end-to-end machine learning pipelines.With practical projects and real-world applications woven throughout, students will have the chance to build models for tasks like image classification, sentiment analysis, and time series prediction, solidifying their skills through hands-on practice. By the end of the course, learners will be equipped not only with the technical knowledge but also the practical experience needed to implement, deploy, and manage TensorFlow models in professional environments. This course is ideal for anyone looking to advance their career in data science, machine learning, or artificial intelligence, empowering them with the expertise to tackle complex challenges in today's data-driven world.

Overview

Section 1: Introduction to Machine Learning and TensorFlow

Lecture 1 What is Machine Learning?

Lecture 2 Introduction to TensorFlow

Lecture 3 TensorFlow vs. Other Machine Learning frameworks

Lecture 4 Installing TensorFlow

Lecture 5 Setting up your Development Environment

Lecture 6 Verifying the Installation

Section 2: Basics of TensorFlow

Lecture 7 Introduction to Tensors

Lecture 8 Tensor Operations

Lecture 9 Constants, Variables, and Placeholders

Lecture 10 TensorFlow Computational Graph

Lecture 11 Creating and Running a TensorFlow Session

Lecture 12 Managing Graphs and Sessions

Lecture 13 Building a Simple Feedforward Neural Network

Lecture 14 Activation Functions

Lecture 15 Loss Functions and Optimizers

Section 3: Intermediate TensorFlow

Lecture 16 Introduction to Keras API

Lecture 17 Building Complex Models with Keras

Lecture 18 Training and Evaluating Models

Lecture 19 Introduction to CNNs(Convolutional Neural Networks)

Lecture 20 Building and Training CNNs with TensorFlow

Lecture 21 Transfer Learning with Pre-trained CNNs

Lecture 22 Introduction to RNNs(Recurrent Neural Networks)

Lecture 23 Building and Training RNNs with TensorFlow

Lecture 24 Applications of RNNs: Language Modeling, Time Series Prediction

Section 4: Advanced TensorFlow

Lecture 25 Saving and Loading Models

Lecture 26 TensorFlow Serving for Model Deployment

Lecture 27 TensorFlow Lite for Mobile and Embedded Devices

Lecture 28 Introduction to Distributed Computing with TensorFlow

Lecture 29 TensorFlow's Distributed Execution Framework

Lecture 30 Scaling TensorFlow with TensorFlow Serving and Kubernetes

Lecture 31 Introduction to TFX(TensorFlow Extended)

Lecture 32 Building End-to-End ML Pipelines with TFX

Lecture 33 Model Validation, Transform, and Serving with TFX

Section 5: Practical Applications and Projects

Lecture 34 Image Classification

Lecture 35 Natural Language Processing

Lecture 36 Recommender Systems

Lecture 37 Object Detection

Lecture 38 Building a Sentiment Analysis Model

Lecture 39 Creating an Image Recognition System

Lecture 40 Developing a Time Series Prediction Model

Lecture 41 Implementing a Chatbot

Section 6: Further Learning and Resources

Lecture 42 Generative Adversarial Networks (GANs)

Lecture 43 Reinforcement Learning with TensorFlow

Lecture 44 Quantum Machine Learning with TensorFlow Quantum

Lecture 45 TensorFlow Documentation and Tutorials

Lecture 46 Online Courses and Books

Lecture 47 TensorFlow Community and Forums

Section 7: Summary of Tensor Flow

Lecture 48 Summary of Key Concepts

Lecture 49 Next Steps in Your TensorFlow Journey

Aspiring Data Scientists and ML Engineers who want to build a solid foundation in TensorFlow for real-world machine learning projects,Developers and Programmers interested in expanding their skills to include machine learning and neural networks,Students and Professionals in data science, AI, or related fields, looking to add TensorFlow to their toolkit,Self-Learners who enjoy hands-on projects and are ready to dive into practical, scalable applications in machine learning

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[0dayddl]

Master Prompt Engineering | Basic To Advanced
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.60 GB | Duration: 11h 27m​

Learn the Art of Crafting Powerful Prompts for LLM's like Chatgpt, Gemini, Claude & More

What you'll learn

Craft effective prompts for AI models using basic and advanced techniques, generating precise and high-quality outputs tailored to various tasks.

Apply prompt patterns like zero-shot, few-shot, and chain-of-thought, and advanced techniques like persona design and semantic filtering.

Understand and compare the strengths and weaknesses of LLMs like GPT-4, Gemini, and Claude to create prompts optimized for each model.

Refine prompts to improve clarity, engagement, and task-specific effectiveness for diverse real-world applications.

Master tools and techniques like OpenAI Playground and prompt chaining to enhance efficiency and creativity in AI workflows.

Stay future-ready with skills in multimodal AI, fine-tuning, and ethical practices for adapting to the evolving generative AI landscape.

Requirements

No programming experience is needed. You will learn everything you need to know

While mobile devices can be used for viewing course content, hands-on exercises are better suited for a laptop or desktop.

Description

This course is designed to meet the prompt engineering job requirements identified by companies through an analysis of the role's responsibilities and expectations.Unlock the power of Generative AI with Prompt Engineering, the essential skill for mastering AI tools like ChatGPT, Gemini, and more. In this comprehensive course, you'll learn how to craft effective prompts that yield clear, accurate, and creative outputs from cutting-edge AI models. Starting from the basics and progressing to advanced techniques, this course equips you with the tools to optimize AI for content creation, automation, business innovation, and beyond.What You Will LearnBy enrolling in this course, you will gain:A deep understanding of prompt engineering basics, including zero-shot, few-shot, and chain-of-thought prompting.Advanced skills in leveraging prompt patterns, such as persona creation, semantic filtering, and cognitive verifier techniques.Insights into the strengths and limitations of leading AI models like GPT-4, Gemini, and Claude, and how to tailor prompts for each.Hands-on experience using AI tools like OpenAI Playground and prompt chaining method to refine and optimize prompts.Techniques to analyze, compare, and fine-tune outputs from multiple models for clarity, precision, and engagement.The ability to stay ahead of trends in the evolving Generative AI landscape, from ethical considerations to industry-specific applications.Why You Should Take This ClassIn today's fast-paced digital world, generative AI is revolutionizing industries-from marketing and content creation to research and product development. But to truly harness its power, you need to communicate effectively with these AI models.No prior experience with AI is required. Whether you're a tech enthusiast or completely new to the field, this course will guide you from beginner to advanced levels with clarity and support.Enroll NowDon't miss the opportunity to master the skill that's shaping the future. Join us and become an expert in Prompt Engineering-your gateway to unlocking the full potential of Generative AI!

Overview

Section 1: Introduction to Prompt Engineering

Lecture 1 What is Prompt Engineering?

Lecture 2 Prompt Design vs Prompt Engineering

Lecture 3 Basics of AI Large Language Models (LLM's)

Lecture 4 How LLM's Process Prompts?

Lecture 5 Applications of Prompt Engineering

Section 2: Foundations of Effective Prompts

Lecture 6 Basic Components of Prompt

Lecture 7 Types of Prompts

Lecture 8 Basic Prompt Patterns : 1. Zero-shot Prompting

Lecture 9 2. Few-shot Prompting

Lecture 10 3. System Instruction Prompting

Lecture 11 4. Role-playing Technique Prompting

Section 3: Intermediate Prompt Engineering

Lecture 12 Structuring Prompts for Optimal Output

Lecture 13 Iterative Prompting

Lecture 14 Context Management - Part 1

Lecture 15 Context Management - Part 2

Section 4: Advanced Prompt Engineering

Lecture 16 Prompt Optimization

Lecture 17 Advanced Prompt Patterns (Part 1) - 1. Ask for Input Pattern

Lecture 18 2. Persona Prompt Pattern

Lecture 19 3. Question Refinement Prompt Pattern - Part 1

Lecture 20 3. Question Refinement Prompt Pattern - Part 2

Lecture 21 4. Cognitive Verifier Prompt Pattern - Part 1

Lecture 22 4. Cognitive Verifier Prompt Pattern - Part 2

Lecture 23 5. Outline Expansion Prompt Pattern

Lecture 24 Advanced Prompt Patterns (Part 2) - 1. Tail Generation Prompt Pattern

Lecture 25 2. Semantic Filter Prompt Pattern - Part 1

Lecture 26 2. Semantic Filter Prompt Pattern - Part 2

Lecture 27 3. Menu Actions Prompt Pattern

Lecture 28 4. Fact Check List Prompt Pattern

Lecture 29 5. Chain of Thought Prompt Pattern

Section 5: Specialized Tools & Techniques in Prompt Engineering

Lecture 30 Prompt Chaining - Part 1

Lecture 31 Prompt Chaining - Part 2

Lecture 32 Prompt Engineering Applications & Use Cases

Lecture 33 Initial Prompt Setup - Helpful Assistant

Lecture 34 Writing Effective Prompts for Different Use Cases - Part 1

Lecture 35 Writing Effective Prompts for Different Use Cases - Part 2

Lecture 36 How to Write Advanced Image Prompts using ChatGPT

Lecture 37 How to Write Advanced Text Prompts using ChatGPT

Lecture 38 AI Ethical Considerations

Lecture 39 Understanding Different LLM's Pros & Cons

Lecture 40 Understanding ChatGPT Capabilities with Use Case 1

Lecture 41 Capabilities of Gemini, Claude, Perplexity & Copilot with Use Case 1

Lecture 42 Understanding ChatGPT Capabilities with Use Case 2

Lecture 43 Comparing Gemini, Claude, Perplexity & Copilot with Use Case 2

Lecture 44 How to Use Different LLM's to Write Effective Prompts ?

Lecture 45 How to Use ChatGPT for Writing Advanced Prompts - Part 1

Lecture 46 How to Use ChatGPT for Writing Advanced Prompts - Part 2

Lecture 47 How to Use Gemini, Claude, Perplexity & Copilot to Write Effective Prompts

Lecture 48 Prompt Engineering Tools - OpenAI Playground Parameters Part 1

Lecture 49 OpenAI Playground Parameters Part 2

Lecture 50 OpenAI Playground Parameters Part 3

Lecture 51 OpenAI Playground Parameters Part 4

Section 6: Prompt Engineering Future Trends and Opportunities

Lecture 52 The Future of Prompt Engineering

Lecture 53 Prompt Engineering Opportunities

Lecture 54 Overview of GenAI

Lecture 55 Career Opportunities in Prompt Engineering

Lecture 56 How to Find Jobs & Freelancing Sites for Prompt Engineering

Lecture 57 How to Prepare for Future Opportunities as a Prompt Engineer

Lecture 58 Basics of Fine-Tuning and RAG

Lecture 59 What is Retrieval Augmented Generation (RAG)

Lecture 60 Fine Tuning vs RAG

Lecture 61 Role of Prompt Engineer in GenAI

Lecture 62 Applications GenAI Prompt Engineering

Lecture 63 Impact of Prompt Engineers on GenAI Success

Lecture 64 Final Thoughts, Project and Full Course Document

This course is beginner-friendly and designed to guide students with no prior AI or technical background,Beginners Exploring AI and Generative Models,Content Creators and Marketers,Developers and Programmers,Business Professionals and Entrepreneurs,Researchers and Academics,AI Enthusiasts and Hobbyists,Professionals Transitioning into AI,Learners looking to build new skills

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