TensorFlow for Deep Learning Bootcamp

Course Name : TensorFlow for Deep Learning Bootcamp
Students : 75,262
Duration : 63 hrs
Avg Rating : 4.7
Original Price : $119.99
Discount Price : $12.99
Instructor / Provider : Andrei Neagoie, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

What You’ll Learn:

• Build TensorFlow Models: Master techniques in Computer Vision, Convolutional Neural Networks, and Natural Language Processing.
• Comprehensive Resources: Access interactive notebooks and downloadable course slides.
• Advanced Machine Learning Skills: Enhance your expertise in Machine Learning, AI, and Deep Learning.
• Integrate Machine Learning: Learn to implement ML models in real-world applications.
• Master TensorFlow 2: Build a variety of ML models using the latest TensorFlow framework.
• Practical Applications: Create image and text recognition algorithms with deep learning techniques.
• Visualize Data: Understand how images are processed through convolutions and plot loss/accuracy metrics.
• Time Series Forecasting: Apply deep learning to predict trends and outcomes over time.
• Career-Ready Skills: Position yourself as a top candidate for TensorFlow developer roles.

Course to bought together

Course Breakdown:

1 — TensorFlow Fundamentals

• Learn what tensors are and how to create them.
• Work with tensor attributes and operations.
• Use Tensors alongside NumPy for seamless integration.
• Speed up Python functions using @tf.function.
• Leverage GPUs for enhanced TensorFlow performance.

2 — Neural Network Regression

• Build sequential TensorFlow models with multiple layers.
• Prepare datasets for machine learning tasks.
• Understand core components of deep learning: loss functions, architecture, and optimization.
• Diagnose regression problems and create neural networks to predict numerical outcomes.

3 — Neural Network Classification

• Identify and tackle classification problems.
• Build, compile, and train models for binary and multi-class classification.
• Visualize model performance metrics and refine accuracy.
• Match training data shapes with prediction targets.

4 — Computer Vision with CNNs

• Construct convolutional neural networks with Conv2D and pooling layers.
• Understand the nuances of various computer vision problems.
• Train models to work with real-world image datasets.

5 — Transfer Learning Part 1: Feature Extraction

• Use pre-trained models to extract features from datasets.
• Explore TensorFlow Hub for pre-trained resources.
• Utilize TensorBoard to compare multiple model performances.

6 — Transfer Learning Part 2: Fine-Tuning

• Experiment with machine learning workflows.
• Implement data augmentation to improve training data diversity.
• Fine-tune pre-trained models for custom use cases.
• Add functionality during training using Callbacks.

7 — Transfer Learning Part 3: Scaling Up (Food Vision Mini)

• Scale up models to handle complex tasks.
• Analyze and address incorrect model predictions.
• Achieve state-of-the-art results using only a fraction of the original data.

8 — Milestone Project 1: Food Vision

Combine your knowledge to create a computer vision model capable of classifying 101 food categories, surpassing the performance of the original Food101 paper.

9 — NLP Fundamentals

• Preprocess text data for neural networks.
• Create word embeddings with TensorFlow.
• Build and evaluate binary and multi-class classification models using RNNs, LSTMs, GRUs, and CNNs.

10 — Milestone Project 2: SkimLit

Reproduce a model inspired by PubMed 200k, enabling rapid classification of sequences in medical abstracts to aid researchers.

11 — Time Series Forecasting

• Identify time series problems and prepare datasets.
• Learn evaluation metrics like Mean Absolute Error (MAE).
• Build forecasting models with TensorFlow using RNNs and CNNs.

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