Tensorflow Course: Basic To Advanced Neural Network & Beyond

Master TensorFlow and Deep learning — from basic neural networks to advanced models and real world AI applications.

What you'll learn

Introduction to TensorFlow and its ecosystem.

Setting up TensorFlow (installation, virtual environments).

TensorFlow Core Concepts: Tensors, Variables, Operations.

Understanding the TensorFlow Execution Model.

Working with Tensors: Creating, Manipulating, and Indexing.

Mathematical Operations and Broadcasting.

Variables and Constants.

Automatic Differentiation with GradientTape.

Compiling and Training Keras Models.

Model Evaluation and Performance Metrics.

Convolutional Layers, Pooling Layers, and Activation Functions.

Building CNNs for Image Classification and Object Detection.

Understanding Sequence Data and Time Series Analysis.

Building RNNs for Text Generation and Sentiment Analysis.

Text Preprocessing: Tokenization, Stemming, and Lemmatization.

TensorFlow Datasets API for Efficient Data Loading.

TensorBoard for Visualization and Debugging.

TensorFlow Lite for Mobile and Embedded Devices.

TensorFlow.js for Browser Based Machine Learning.

Requirements

No prior deep learning experience required.

Description

This comprehensive course will take you on a journey from the foundational concepts of machine learning and TensorFlow to the creation of advanced, real world deep learning models. I'll start with the basics, giving you a solid understanding of how neural networks work, and progressively build up your skills to tackle complex problems in computer vision, natural language processing (NLP), and more. Through a series of hands-on labs, projects, and practical examples, you'll learn to not only build and train models but also to understand the "why" behind the code, enabling you to confidently solve new and challenging problems.This course is designed for anyone with a basic understanding of Python programming who wants to build a career in machine learning and artificial intelligence. Whether you're a student, a software developer, or a data analyst, this course will provide you with the practical skills and foundational knowledge to become a proficient TensorFlow practitioner.Why Take This Course?Artificial Intelligence is transforming industries worldwide, and deep learning lies at its core. TensorFlow, developed by Google, has become the industry standard library for building and deploying AI applications at scale. This course provides a step by step learning journey, blending theory with hands-on coding so you not only understand concepts but can also implement them in real world projects.By the end of this course, you’ll have the knowledge and confidence to:Understand the foundations of deep learning and TensorFlow.Build simple and complex neural networks from scratch.Train, evaluate, and optimize models using modern techniques.Work with Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced architectures.Deploy machine learning models in real-world scenarios.What You’ll Learn:Master TensorFlow: From fundamentals to advanced deployment.Think Like a Deep Learning Engineer: Understand the “why” behind each step.Future Proof Skills: Learn architectures powering GPT, BERT, and other state of the art systems.Career Boost: Gain skills highly sought after in AI, ML, and data science industries.Hands-On Confidence: Not just theory—every concept is practiced with real datasets and code.No prior knowledge of machine learning or deep learning is required. A basic understanding of Python programming is recommended.Why This Course Stands OutComprehensive Curriculum: Covers both fundamentals and advanced topics.Practical Focus: Hands-on coding and real-world projects ensure you learn by doing.Step by Step Guidance: Concepts explained in simple, intuitive language.Future Proof Skills: Covers emerging areas like transformers and model deployment.By the End of the Course, You Will Be Able To:Confidently use TensorFlow for deep learning projects.Build and train different types of neural networks.Apply deep learning techniques to images, text, and sequential data.Experiment with cutting edge models like GANs and Transformers.Deploy and scale models for real world applications.Are you ready to become a TensorFlow expert and build the future with AI?Join today and start your journey from basic to advanced neural networks— and beyond!

Overview

Section 1: Introduction to TensorFlow and Setup

Lecture 1 Introduction to TensorFlow and its Ecosystem

Lecture 2 Setting up TensorFlow (installation, virtual environments)

Lecture 3 TensorFlow Core Concepts: Tensors, Variables, Operations

Lecture 4 Understanding the TensorFlow Execution Model

Lecture 5 Introduction to TensorFlow 2.x and Eager Execution

Section 2: TensorFlow Core APIs and Basic Operations

Lecture 6 Working with Tensors: Creating, Manipulating, and Indexing

Lecture 7 Mathematical Operations and Broadcasting

Lecture 8 Variables and Constants

Lecture 9 Automatic Differentiation with GradientTape

Lecture 10 Building Simple Models with TensorFlow Core APIs

Section 3: Introduction to Keras

Lecture 11 Introduction to Keras API and its Advantages

Lecture 12 Building Sequential and Functional Models with Keras

Lecture 13 Compiling and Training Keras Models

Lecture 14 Model Evaluation and Performance Metrics

Lecture 15 Working with Datasets in Keras

Section 4: Convolutional Neural Networks (CNNs) for Image Processing

Lecture 16 Introduction to CNNs and Their Architecture

Lecture 17 Convolutional Layers, Pooling Layers, and Activation Functions

Lecture 18 Building CNNs for Image Classification and Object Detection

Lecture 19 Transfer Learning with Pre-Trained CNN models (e.g., ResNet, VGG)

Section 5: Recurrent Neural Networks (RNNs) for Sequential Data

Lecture 20 Introduction to RNNs and Their Applications

Lecture 21 Understanding Sequence Data and Time Series Analysis

Lecture 22 Building RNNs for Text Generation and Sentiment Analysis

Lecture 23 Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) Networks

Section 6: Natural Language Processing (NLP) with TensorFlow

Lecture 24 Introduction to NLP Tasks and Techniques

Lecture 25 Text Preprocessing: Tokenization, Stemming, and Lemmatization

Lecture 26 Word Embeddings (Word2Vec, GloVe, Embedding layers)

Lecture 27 Building RNNs and Transformers for NLP Tasks

Lecture 28 Text Classification, Sentiment Analysis, and Machine Translation

Section 7: Advanced TensorFlow Techniques

Lecture 29 Custom Layers and Models

Lecture 30 TensorFlow Datasets API for Efficient Data Loading

Lecture 31 TensorBoard for Visualization and Debugging

Lecture 32 Model Optimization and Performance Tuning

Section 8: Model Deployment and Production

Lecture 33 Saving and Loading TensorFlow Models

Lecture 34 TensorFlow Serving for Deploying Models as APIs

Lecture 35 TensorFlow Lite for Mobile and Embedded Devices

Lecture 36 TensorFlow.js for Browser Based Machine Learning

Lecture 37 Cloud Deployment Using Google Cloud Platform (GCP) or Other Cloud Services

Anyone curious about AI and deep learning who wants to start from scratch.,Those with Python knowledge who want to dive into TensorFlow and neural networks.,rofessionals aiming to enhance their ML/DL skills with TensorFlow.,Learners who want to explore advanced neural architectures and applications.